Experimental design and efficient parameter estimation in preclinical pharmacokinetic studies.

PubMed

Ette, E I; Howie, C A; Kelman, A W; Whiting, B

1995-05-01

Monte Carlo simulation technique used to evaluate the effect of the arrangement of concentrations on the efficiency of estimation of population pharmacokinetic parameters in the preclinical setting is described. Although the simulations were restricted to the one compartment model with intravenous bolus input, they provide the basis of discussing some structural aspects involved in designing a destructive ("quantic") preclinical population pharmacokinetic study with a fixed sample size as is usually the case in such studies. The efficiency of parameter estimation obtained with sampling strategies based on the three and four time point designs were evaluated in terms of the percent prediction error, design number, individual and joint confidence intervals coverage for parameter estimates approaches, and correlation analysis. The data sets contained random terms for both inter- and residual intra-animal variability. The results showed that the typical population parameter estimates for clearance and volume were efficiently (accurately and precisely) estimated for both designs, while interanimal variability (the only random effect parameter that could be estimated) was inefficiently (inaccurately and imprecisely) estimated with most sampling schedules of the two designs. The exact location of the third and fourth time point for the three and four time point designs, respectively, was not critical to the efficiency of overall estimation of all population parameters of the model. However, some individual population pharmacokinetic parameters were sensitive to the location of these times.

Marginal Structural Models for Case-Cohort Study Designs to Estimate the Association of Antiretroviral Therapy Initiation With Incident AIDS or Death

PubMed Central

Cole, Stephen R.; Hudgens, Michael G.; Tien, Phyllis C.; Anastos, Kathryn; Kingsley, Lawrence; Chmiel, Joan S.; Jacobson, Lisa P.

2012-01-01

To estimate the association of antiretroviral therapy initiation with incident acquired immunodeficiency syndrome (AIDS) or death while accounting for time-varying confounding in a cost-efficient manner, the authors combined a case-cohort study design with inverse probability-weighted estimation of a marginal structural Cox proportional hazards model. A total of 950 adults who were positive for human immunodeficiency virus type 1 were followed in 2 US cohort studies between 1995 and 2007. In the full cohort, 211 AIDS cases or deaths occurred during 4,456 person-years. In an illustrative 20% random subcohort of 190 participants, 41 AIDS cases or deaths occurred during 861 person-years. Accounting for measured confounders and determinants of dropout by inverse probability weighting, the full cohort hazard ratio was 0.41 (95% confidence interval: 0.26, 0.65) and the case-cohort hazard ratio was 0.47 (95% confidence interval: 0.26, 0.83). Standard multivariable-adjusted hazard ratios were closer to the null, regardless of study design. The precision lost with the case-cohort design was modest given the cost savings. Results from Monte Carlo simulations demonstrated that the proposed approach yields approximately unbiased estimates of the hazard ratio with appropriate confidence interval coverage. Marginal structural model analysis of case-cohort study designs provides a cost-efficient design coupled with an accurate analytic method for research settings in which there is time-varying confounding. PMID:22302074

Generalized Hurst exponent estimates differentiate EEG signals of healthy and epileptic patients

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2018-01-01

The aim of our current study is to check whether multifractal patterns of the electroencephalographic (EEG) signals of normal and epileptic patients are statistically similar or different. In this regard, the generalized Hurst exponent (GHE) method is used for robust estimation of the multifractals in each type of EEG signals, and three powerful statistical tests are performed to check existence of differences between estimated GHEs from healthy control subjects and epileptic patients. The obtained results show that multifractals exist in both types of EEG signals. Particularly, it was found that the degree of fractal is more pronounced in short variations of normal EEG signals than in short variations of EEG signals with seizure free intervals. In contrary, it is more pronounced in long variations of EEG signals with seizure free intervals than in normal EEG signals. Importantly, both parametric and nonparametric statistical tests show strong evidence that estimated GHEs of normal EEG signals are statistically and significantly different from those with seizure free intervals. Therefore, GHEs can be efficiently used to distinguish between healthy and patients suffering from epilepsy.

Efficiently estimating salmon escapement uncertainty using systematically sampled data

USGS Publications Warehouse

Reynolds, Joel H.; Woody, Carol Ann; Gove, Nancy E.; Fair, Lowell F.

2007-01-01

Fish escapement is generally monitored using nonreplicated systematic sampling designs (e.g., via visual counts from towers or hydroacoustic counts). These sampling designs support a variety of methods for estimating the variance of the total escapement. Unfortunately, all the methods give biased results, with the magnitude of the bias being determined by the underlying process patterns. Fish escapement commonly exhibits positive autocorrelation and nonlinear patterns, such as diurnal and seasonal patterns. For these patterns, poor choice of variance estimator can needlessly increase the uncertainty managers have to deal with in sustaining fish populations. We illustrate the effect of sampling design and variance estimator choice on variance estimates of total escapement for anadromous salmonids from systematic samples of fish passage. Using simulated tower counts of sockeye salmon Oncorhynchus nerka escapement on the Kvichak River, Alaska, five variance estimators for nonreplicated systematic samples were compared to determine the least biased. Using the least biased variance estimator, four confidence interval estimators were compared for expected coverage and mean interval width. Finally, five systematic sampling designs were compared to determine the design giving the smallest average variance estimate for total annual escapement. For nonreplicated systematic samples of fish escapement, all variance estimators were positively biased. Compared to the other estimators, the least biased estimator reduced bias by, on average, from 12% to 98%. All confidence intervals gave effectively identical results. Replicated systematic sampling designs consistently provided the smallest average estimated variance among those compared.

Efficient bootstrap estimates for tail statistics

NASA Astrophysics Data System (ADS)

Breivik, Øyvind; Aarnes, Ole Johan

2017-03-01

Bootstrap resamples can be used to investigate the tail of empirical distributions as well as return value estimates from the extremal behaviour of the sample. Specifically, the confidence intervals on return value estimates or bounds on in-sample tail statistics can be obtained using bootstrap techniques. However, non-parametric bootstrapping from the entire sample is expensive. It is shown here that it suffices to bootstrap from a small subset consisting of the highest entries in the sequence to make estimates that are essentially identical to bootstraps from the entire sample. Similarly, bootstrap estimates of confidence intervals of threshold return estimates are found to be well approximated by using a subset consisting of the highest entries. This has practical consequences in fields such as meteorology, oceanography and hydrology where return values are calculated from very large gridded model integrations spanning decades at high temporal resolution or from large ensembles of independent and identically distributed model fields. In such cases the computational savings are substantial.

Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates.

PubMed

LeDell, Erin; Petersen, Maya; van der Laan, Mark

In binary classification problems, the area under the ROC curve (AUC) is commonly used to evaluate the performance of a prediction model. Often, it is combined with cross-validation in order to assess how the results will generalize to an independent data set. In order to evaluate the quality of an estimate for cross-validated AUC, we obtain an estimate of its variance. For massive data sets, the process of generating a single performance estimate can be computationally expensive. Additionally, when using a complex prediction method, the process of cross-validating a predictive model on even a relatively small data set can still require a large amount of computation time. Thus, in many practical settings, the bootstrap is a computationally intractable approach to variance estimation. As an alternative to the bootstrap, we demonstrate a computationally efficient influence curve based approach to obtaining a variance estimate for cross-validated AUC.

Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates

PubMed Central

Petersen, Maya; van der Laan, Mark

2015-01-01

In binary classification problems, the area under the ROC curve (AUC) is commonly used to evaluate the performance of a prediction model. Often, it is combined with cross-validation in order to assess how the results will generalize to an independent data set. In order to evaluate the quality of an estimate for cross-validated AUC, we obtain an estimate of its variance. For massive data sets, the process of generating a single performance estimate can be computationally expensive. Additionally, when using a complex prediction method, the process of cross-validating a predictive model on even a relatively small data set can still require a large amount of computation time. Thus, in many practical settings, the bootstrap is a computationally intractable approach to variance estimation. As an alternative to the bootstrap, we demonstrate a computationally efficient influence curve based approach to obtaining a variance estimate for cross-validated AUC. PMID:26279737

Structure and Utility of Blind Speed Intervals Associated with Doppler Measurements of Range Rate

DTIC Science & Technology

1993-02-01

computer programming concepts of speed, memory , and data structures that can be exploited to fabricate efficient software realizations of two phase range...to the effect that it is possible to derive a reasonable unambiguous estimate of range rate from the measurement of the pulse-to-pulse phase shift in...the properties of the blind speed intervals generated by the base speeds involved in two measurement equations. Sections 12 through 18 make up the

Parameter identification for structural dynamics based on interval analysis algorithm

NASA Astrophysics Data System (ADS)

Yang, Chen; Lu, Zixing; Yang, Zhenyu; Liang, Ke

2018-04-01

A parameter identification method using interval analysis algorithm for structural dynamics is presented in this paper. The proposed uncertain identification method is investigated by using central difference method and ARMA system. With the help of the fixed memory least square method and matrix inverse lemma, a set-membership identification technology is applied to obtain the best estimation of the identified parameters in a tight and accurate region. To overcome the lack of insufficient statistical description of the uncertain parameters, this paper treats uncertainties as non-probabilistic intervals. As long as we know the bounds of uncertainties, this algorithm can obtain not only the center estimations of parameters, but also the bounds of errors. To improve the efficiency of the proposed method, a time-saving algorithm is presented by recursive formula. At last, to verify the accuracy of the proposed method, two numerical examples are applied and evaluated by three identification criteria respectively.

Neural network based load and price forecasting and confidence interval estimation in deregulated power markets

NASA Astrophysics Data System (ADS)

Zhang, Li

With the deregulation of the electric power market in New England, an independent system operator (ISO) has been separated from the New England Power Pool (NEPOOL). The ISO provides a regional spot market, with bids on various electricity-related products and services submitted by utilities and independent power producers. A utility can bid on the spot market and buy or sell electricity via bilateral transactions. Good estimation of market clearing prices (MCP) will help utilities and independent power producers determine bidding and transaction strategies with low risks, and this is crucial for utilities to compete in the deregulated environment. MCP prediction, however, is difficult since bidding strategies used by participants are complicated and MCP is a non-stationary process. The main objective of this research is to provide efficient short-term load and MCP forecasting and corresponding confidence interval estimation methodologies. In this research, the complexity of load and MCP with other factors is investigated, and neural networks are used to model the complex relationship between input and output. With improved learning algorithm and on-line update features for load forecasting, a neural network based load forecaster was developed, and has been in daily industry use since summer 1998 with good performance. MCP is volatile because of the complexity of market behaviors. In practice, neural network based MCP predictors usually have a cascaded structure, as several key input factors need to be estimated first. In this research, the uncertainties involved in a cascaded neural network structure for MCP prediction are analyzed, and prediction distribution under the Bayesian framework is developed. A fast algorithm to evaluate the confidence intervals by using the memoryless Quasi-Newton method is also developed. The traditional back-propagation algorithm for neural network learning needs to be improved since MCP is a non-stationary process. The extended Kalman filter (EKF) can be used as an integrated adaptive learning and confidence interval estimation algorithm for neural networks, with fast convergence and small confidence intervals. However, EKF learning is computationally expensive because it involves high dimensional matrix manipulations. A modified U-D factorization within the decoupled EKF (DEKF-UD) framework is developed in this research. The computational efficiency and numerical stability are significantly improved.

Mortality estimation from carcass searches using the R-package carcass: a tutorial

USGS Publications Warehouse

Korner-Nievergelt, Fränzi; Behr, Oliver; Brinkmann, Robert; Etterson, Matthew A.; Huso, Manuela M. P.; Dalthorp, Daniel; Korner-Nievergelt, Pius; Roth, Tobias; Niermann, Ivo

2015-01-01

This article is a tutorial for the R-package carcass. It starts with a short overview of common methods used to estimate mortality based on carcass searches. Then, it guides step by step through a simple example. First, the proportion of animals that fall into the search area is estimated. Second, carcass persistence time is estimated based on experimental data. Third, searcher efficiency is estimated. Fourth, these three estimated parameters are combined to obtain the probability that an animal killed is found by an observer. Finally, this probability is used together with the observed number of carcasses found to obtain an estimate for the total number of killed animals together with a credible interval.

NURD: an implementation of a new method to estimate isoform expression from non-uniform RNA-seq data

PubMed Central

2013-01-01

Background RNA-Seq technology has been used widely in transcriptome study, and one of the most important applications is to estimate the expression level of genes and their alternative splicing isoforms. There have been several algorithms published to estimate the expression based on different models. Recently Wu et al. published a method that can accurately estimate isoform level expression by considering position-related sequencing biases using nonparametric models. The method has advantages in handling different read distributions, but there hasn’t been an efficient program to implement this algorithm. Results We developed an efficient implementation of the algorithm in the program NURD. It uses a binary interval search algorithm. The program can correct both the global tendency of sequencing bias in the data and local sequencing bias specific to each gene. The correction makes the isoform expression estimation more reliable under various read distributions. And the implementation is computationally efficient in both the memory cost and running time and can be readily scaled up for huge datasets. Conclusion NURD is an efficient and reliable tool for estimating the isoform expression level. Given the reads mapping result and gene annotation file, NURD will output the expression estimation result. The package is freely available for academic use at http://bioinfo.au.tsinghua.edu.cn/software/NURD/. PMID:23837734

An Algorithm for Efficient Maximum Likelihood Estimation and Confidence Interval Determination in Nonlinear Estimation Problems

NASA Technical Reports Server (NTRS)

Murphy, Patrick Charles

1985-01-01

An algorithm for maximum likelihood (ML) estimation is developed with an efficient method for approximating the sensitivities. The algorithm was developed for airplane parameter estimation problems but is well suited for most nonlinear, multivariable, dynamic systems. The ML algorithm relies on a new optimization method referred to as a modified Newton-Raphson with estimated sensitivities (MNRES). MNRES determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. The fitted surface allows sensitivity information to be updated at each iteration with a significant reduction in computational effort. MNRES determines the sensitivities with less computational effort than using either a finite-difference method or integrating the analytically determined sensitivity equations. MNRES eliminates the need to derive sensitivity equations for each new model, thus eliminating algorithm reformulation with each new model and providing flexibility to use model equations in any format that is convenient. A random search technique for determining the confidence limits of ML parameter estimates is applied to nonlinear estimation problems for airplanes. The confidence intervals obtained by the search are compared with Cramer-Rao (CR) bounds at the same confidence level. It is observed that the degree of nonlinearity in the estimation problem is an important factor in the relationship between CR bounds and the error bounds determined by the search technique. The CR bounds were found to be close to the bounds determined by the search when the degree of nonlinearity was small. Beale's measure of nonlinearity is developed in this study for airplane identification problems; it is used to empirically correct confidence levels for the parameter confidence limits. The primary utility of the measure, however, was found to be in predicting the degree of agreement between Cramer-Rao bounds and search estimates.

Uncertainty Quantification and Statistical Convergence Guidelines for PIV Data

NASA Astrophysics Data System (ADS)

Stegmeir, Matthew; Kassen, Dan

2016-11-01

As Particle Image Velocimetry has continued to mature, it has developed into a robust and flexible technique for velocimetry used by expert and non-expert users. While historical estimates of PIV accuracy have typically relied heavily on "rules of thumb" and analysis of idealized synthetic images, recently increased emphasis has been placed on better quantifying real-world PIV measurement uncertainty. Multiple techniques have been developed to provide per-vector instantaneous uncertainty estimates for PIV measurements. Often real-world experimental conditions introduce complications in collecting "optimal" data, and the effect of these conditions is important to consider when planning an experimental campaign. The current work utilizes the results of PIV Uncertainty Quantification techniques to develop a framework for PIV users to utilize estimated PIV confidence intervals to compute reliable data convergence criteria for optimal sampling of flow statistics. Results are compared using experimental and synthetic data, and recommended guidelines and procedures leveraging estimated PIV confidence intervals for efficient sampling for converged statistics are provided.

Linkage disequilibrium interval mapping of quantitative trait loci.

PubMed

Boitard, Simon; Abdallah, Jihad; de Rochambeau, Hubert; Cierco-Ayrolles, Christine; Mangin, Brigitte

2006-03-16

For many years gene mapping studies have been performed through linkage analyses based on pedigree data. Recently, linkage disequilibrium methods based on unrelated individuals have been advocated as powerful tools to refine estimates of gene location. Many strategies have been proposed to deal with simply inherited disease traits. However, locating quantitative trait loci is statistically more challenging and considerable research is needed to provide robust and computationally efficient methods. Under a three-locus Wright-Fisher model, we derived approximate expressions for the expected haplotype frequencies in a population. We considered haplotypes comprising one trait locus and two flanking markers. Using these theoretical expressions, we built a likelihood-maximization method, called HAPim, for estimating the location of a quantitative trait locus. For each postulated position, the method only requires information from the two flanking markers. Over a wide range of simulation scenarios it was found to be more accurate than a two-marker composite likelihood method. It also performed as well as identity by descent methods, whilst being valuable in a wider range of populations. Our method makes efficient use of marker information, and can be valuable for fine mapping purposes. Its performance is increased if multiallelic markers are available. Several improvements can be developed to account for more complex evolution scenarios or provide robust confidence intervals for the location estimates.

Dynamic response analysis of structure under time-variant interval process model

NASA Astrophysics Data System (ADS)

Xia, Baizhan; Qin, Yuan; Yu, Dejie; Jiang, Chao

2016-10-01

Due to the aggressiveness of the environmental factor, the variation of the dynamic load, the degeneration of the material property and the wear of the machine surface, parameters related with the structure are distinctly time-variant. Typical model for time-variant uncertainties is the random process model which is constructed on the basis of a large number of samples. In this work, we propose a time-variant interval process model which can be effectively used to deal with time-variant uncertainties with limit information. And then two methods are presented for the dynamic response analysis of the structure under the time-variant interval process model. The first one is the direct Monte Carlo method (DMCM) whose computational burden is relative high. The second one is the Monte Carlo method based on the Chebyshev polynomial expansion (MCM-CPE) whose computational efficiency is high. In MCM-CPE, the dynamic response of the structure is approximated by the Chebyshev polynomials which can be efficiently calculated, and then the variational range of the dynamic response is estimated according to the samples yielded by the Monte Carlo method. To solve the dependency phenomenon of the interval operation, the affine arithmetic is integrated into the Chebyshev polynomial expansion. The computational effectiveness and efficiency of MCM-CPE is verified by two numerical examples, including a spring-mass-damper system and a shell structure.

Improving regression-model-based streamwater constituent load estimates derived from serially correlated data

USGS Publications Warehouse

Aulenbach, Brent T.

2013-01-01

A regression-model based approach is a commonly used, efficient method for estimating streamwater constituent load when there is a relationship between streamwater constituent concentration and continuous variables such as streamwater discharge, season and time. A subsetting experiment using a 30-year dataset of daily suspended sediment observations from the Mississippi River at Thebes, Illinois, was performed to determine optimal sampling frequency, model calibration period length, and regression model methodology, as well as to determine the effect of serial correlation of model residuals on load estimate precision. Two regression-based methods were used to estimate streamwater loads, the Adjusted Maximum Likelihood Estimator (AMLE), and the composite method, a hybrid load estimation approach. While both methods accurately and precisely estimated loads at the model’s calibration period time scale, precisions were progressively worse at shorter reporting periods, from annually to monthly. Serial correlation in model residuals resulted in observed AMLE precision to be significantly worse than the model calculated standard errors of prediction. The composite method effectively improved upon AMLE loads for shorter reporting periods, but required a sampling interval of at least 15-days or shorter, when the serial correlations in the observed load residuals were greater than 0.15. AMLE precision was better at shorter sampling intervals and when using the shortest model calibration periods, such that the regression models better fit the temporal changes in the concentration–discharge relationship. The models with the largest errors typically had poor high flow sampling coverage resulting in unrepresentative models. Increasing sampling frequency and/or targeted high flow sampling are more efficient approaches to ensure sufficient sampling and to avoid poorly performing models, than increasing calibration period length.

Feature-Based Correlation and Topological Similarity for Interbeat Interval Estimation Using Ultrawideband Radar.

PubMed

Sakamoto, Takuya; Imasaka, Ryohei; Taki, Hirofumi; Sato, Toru; Yoshioka, Mototaka; Inoue, Kenichi; Fukuda, Takeshi; Sakai, Hiroyuki

2016-04-01

The objectives of this paper are to propose a method that can accurately estimate the human heart rate (HR) using an ultrawideband (UWB) radar system, and to determine the performance of the proposed method through measurements. The proposed method uses the feature points of a radar signal to estimate the HR efficiently and accurately. Fourier- and periodicity-based methods are inappropriate for estimation of instantaneous HRs in real time because heartbeat waveforms are highly variable, even within the beat-to-beat interval. We define six radar waveform features that enable correlation processing to be performed quickly and accurately. In addition, we propose a feature topology signal that is generated from a feature sequence without using amplitude information. This feature topology signal is used to find unreliable feature points, and thus, to suppress inaccurate HR estimates. Measurements were taken using UWB radar, while simultaneously performing electrocardiography measurements in an experiment that was conducted on nine participants. The proposed method achieved an average root-mean-square error in the interbeat interval of 7.17 ms for the nine participants. The results demonstrate the effectiveness and accuracy of the proposed method. The significance of this study for biomedical research is that the proposed method will be useful in the realization of a remote vital signs monitoring system that enables accurate estimation of HR variability, which has been used in various clinical settings for the treatment of conditions such as diabetes and arterial hypertension.

Application of Fourier transform infrared spectroscopy with chemometrics on postmortem interval estimation based on pericardial fluids.

PubMed

Zhang, Ji; Li, Bing; Wang, Qi; Wei, Xin; Feng, Weibo; Chen, Yijiu; Huang, Ping; Wang, Zhenyuan

2017-12-21

Postmortem interval (PMI) evaluation remains a challenge in the forensic community due to the lack of efficient methods. Studies have focused on chemical analysis of biofluids for PMI estimation; however, no reports using spectroscopic methods in pericardial fluid (PF) are available. In this study, Fourier transform infrared (FTIR) spectroscopy with attenuated total reflectance (ATR) accessory was applied to collect comprehensive biochemical information from rabbit PF at different PMIs. The PMI-dependent spectral signature was determined by two-dimensional (2D) correlation analysis. The partial least square (PLS) and nu-support vector machine (nu-SVM) models were then established based on the acquired spectral dataset. Spectral variables associated with amide I, amide II, COO - , C-H bending, and C-O or C-OH vibrations arising from proteins, polypeptides, amino acids and carbohydrates, respectively, were susceptible to PMI in 2D correlation analysis. Moreover, the nu-SVM model appeared to achieve a more satisfactory prediction than the PLS model in calibration; the reliability of both models was determined in an external validation set. The study shows the possibility of application of ATR-FTIR methods in postmortem interval estimation using PF samples.

Two-step chlorination: A new approach to disinfection of a primary sewage effluent.

PubMed

Li, Yu; Yang, Mengting; Zhang, Xiangru; Jiang, Jingyi; Liu, Jiaqi; Yau, Cie Fu; Graham, Nigel J D; Li, Xiaoyan

2017-01-01

Sewage disinfection aims at inactivating pathogenic microorganisms and preventing the transmission of waterborne diseases. Chlorination is extensively applied for disinfecting sewage effluents. The objective of achieving a disinfection goal and reducing disinfectant consumption and operational costs remains a challenge in sewage treatment. In this study, we have demonstrated that, for the same chlorine dosage, a two-step addition of chlorine (two-step chlorination) was significantly more efficient in disinfecting a primary sewage effluent than a one-step addition of chlorine (one-step chlorination), and shown how the two-step chlorination was optimized with respect to time interval and dosage ratio. Two-step chlorination of the sewage effluent attained its highest disinfection efficiency at a time interval of 19 s and a dosage ratio of 5:1. Compared to one-step chlorination, two-step chlorination enhanced the disinfection efficiency by up to 0.81- or even 1.02-log for two different chlorine doses and contact times. An empirical relationship involving disinfection efficiency, time interval and dosage ratio was obtained by best fitting. Mechanisms (including a higher overall Ct value, an intensive synergistic effect, and a shorter recovery time) were proposed for the higher disinfection efficiency of two-step chlorination in the sewage effluent disinfection. Annual chlorine consumption costs in one-step and two-step chlorination of the primary sewage effluent were estimated. Compared to one-step chlorination, two-step chlorination reduced the cost by up to 16.7%. Copyright © 2016 Elsevier Ltd. All rights reserved.

Simplified Estimation and Testing in Unbalanced Repeated Measures Designs.

PubMed

Spiess, Martin; Jordan, Pascal; Wendt, Mike

2018-05-07

In this paper we propose a simple estimator for unbalanced repeated measures design models where each unit is observed at least once in each cell of the experimental design. The estimator does not require a model of the error covariance structure. Thus, circularity of the error covariance matrix and estimation of correlation parameters and variances are not necessary. Together with a weak assumption about the reason for the varying number of observations, the proposed estimator and its variance estimator are unbiased. As an alternative to confidence intervals based on the normality assumption, a bias-corrected and accelerated bootstrap technique is considered. We also propose the naive percentile bootstrap for Wald-type tests where the standard Wald test may break down when the number of observations is small relative to the number of parameters to be estimated. In a simulation study we illustrate the properties of the estimator and the bootstrap techniques to calculate confidence intervals and conduct hypothesis tests in small and large samples under normality and non-normality of the errors. The results imply that the simple estimator is only slightly less efficient than an estimator that correctly assumes a block structure of the error correlation matrix, a special case of which is an equi-correlation matrix. Application of the estimator and the bootstrap technique is illustrated using data from a task switch experiment based on an experimental within design with 32 cells and 33 participants.

Technical and scale efficiency in public and private Irish nursing homes - a bootstrap DEA approach.

PubMed

Ni Luasa, Shiovan; Dineen, Declan; Zieba, Marta

2016-10-27

This article provides methodological and empirical insights into the estimation of technical efficiency in the nursing home sector. Focusing on long-stay care and using primary data, we examine technical and scale efficiency in 39 public and 73 private Irish nursing homes by applying an input-oriented data envelopment analysis (DEA). We employ robust bootstrap methods to validate our nonparametric DEA scores and to integrate the effects of potential determinants in estimating the efficiencies. Both the homogenous and two-stage double bootstrap procedures are used to obtain confidence intervals for the bias-corrected DEA scores. Importantly, the application of the double bootstrap approach affords true DEA technical efficiency scores after adjusting for the effects of ownership, size, case-mix, and other determinants such as location, and quality. Based on our DEA results for variable returns to scale technology, the average technical efficiency score is 62 %, and the mean scale efficiency is 88 %, with nearly all units operating on the increasing returns to scale part of the production frontier. Moreover, based on the double bootstrap results, Irish nursing homes are less technically efficient, and more scale efficient than the conventional DEA estimates suggest. Regarding the efficiency determinants, in terms of ownership, we find that private facilities are less efficient than the public units. Furthermore, the size of the nursing home has a positive effect, and this reinforces our finding that Irish homes produce at increasing returns to scale. Also, notably, we find that a tendency towards quality improvements can lead to poorer technical efficiency performance.

Designing efficient nitrous oxide sampling strategies in agroecosystems using simulation models

NASA Astrophysics Data System (ADS)

Saha, Debasish; Kemanian, Armen R.; Rau, Benjamin M.; Adler, Paul R.; Montes, Felipe

2017-04-01

Annual cumulative soil nitrous oxide (N2O) emissions calculated from discrete chamber-based flux measurements have unknown uncertainty. We used outputs from simulations obtained with an agroecosystem model to design sampling strategies that yield accurate cumulative N2O flux estimates with a known uncertainty level. Daily soil N2O fluxes were simulated for Ames, IA (corn-soybean rotation), College Station, TX (corn-vetch rotation), Fort Collins, CO (irrigated corn), and Pullman, WA (winter wheat), representing diverse agro-ecoregions of the United States. Fertilization source, rate, and timing were site-specific. These simulated fluxes surrogated daily measurements in the analysis. We ;sampled; the fluxes using a fixed interval (1-32 days) or a rule-based (decision tree-based) sampling method. Two types of decision trees were built: a high-input tree (HI) that included soil inorganic nitrogen (SIN) as a predictor variable, and a low-input tree (LI) that excluded SIN. Other predictor variables were identified with Random Forest. The decision trees were inverted to be used as rules for sampling a representative number of members from each terminal node. The uncertainty of the annual N2O flux estimation increased along with the fixed interval length. A 4- and 8-day fixed sampling interval was required at College Station and Ames, respectively, to yield ±20% accuracy in the flux estimate; a 12-day interval rendered the same accuracy at Fort Collins and Pullman. Both the HI and the LI rule-based methods provided the same accuracy as that of fixed interval method with up to a 60% reduction in sampling events, particularly at locations with greater temporal flux variability. For instance, at Ames, the HI rule-based and the fixed interval methods required 16 and 91 sampling events, respectively, to achieve the same absolute bias of 0.2 kg N ha-1 yr-1 in estimating cumulative N2O flux. These results suggest that using simulation models along with decision trees can reduce the cost and improve the accuracy of the estimations of cumulative N2O fluxes using the discrete chamber-based method.

Study design and sampling intensity for demographic analyses of bear populations

USGS Publications Warehouse

Harris, R.B.; Schwartz, C.C.; Mace, R.D.; Haroldson, M.A.

2011-01-01

The rate of population change through time (??) is a fundamental element of a wildlife population's conservation status, yet estimating it with acceptable precision for bears is difficult. For studies that follow known (usually marked) bears, ?? can be estimated during some defined time by applying either life-table or matrix projection methods to estimates of individual vital rates. Usually however, confidence intervals surrounding the estimate are broader than one would like. Using an estimator suggested by Doak et al. (2005), we explored the precision to be expected in ?? from demographic analyses of typical grizzly (Ursus arctos) and American black (U. americanus) bear data sets. We also evaluated some trade-offs among vital rates in sampling strategies. Confidence intervals around ?? were more sensitive to adding to the duration of a short (e.g., 3 yrs) than a long (e.g., 10 yrs) study, and more sensitive to adding additional bears to studies with small (e.g., 10 adult females/yr) than large (e.g., 30 adult females/yr) sample sizes. Confidence intervals of ?? projected using process-only variance of vital rates were only slightly smaller than those projected using total variances of vital rates. Under sampling constraints typical of most bear studies, it may be more efficient to invest additional resources into monitoring recruitment and juvenile survival rates of females already a part of the study, than to simply increase the sample size of study females. ?? 2011 International Association for Bear Research and Management.

Using Stochastic Approximation Techniques to Efficiently Construct Confidence Intervals for Heritability.

PubMed

Schweiger, Regev; Fisher, Eyal; Rahmani, Elior; Shenhav, Liat; Rosset, Saharon; Halperin, Eran

2018-06-22

Estimation of heritability is an important task in genetics. The use of linear mixed models (LMMs) to determine narrow-sense single-nucleotide polymorphism (SNP)-heritability and related quantities has received much recent attention, due of its ability to account for variants with small effect sizes. Typically, heritability estimation under LMMs uses the restricted maximum likelihood (REML) approach. The common way to report the uncertainty in REML estimation uses standard errors (SEs), which rely on asymptotic properties. However, these assumptions are often violated because of the bounded parameter space, statistical dependencies, and limited sample size, leading to biased estimates and inflated or deflated confidence intervals (CIs). In addition, for larger data sets (e.g., tens of thousands of individuals), the construction of SEs itself may require considerable time, as it requires expensive matrix inversions and multiplications. Here, we present FIESTA (Fast confidence IntErvals using STochastic Approximation), a method for constructing accurate CIs. FIESTA is based on parametric bootstrap sampling, and, therefore, avoids unjustified assumptions on the distribution of the heritability estimator. FIESTA uses stochastic approximation techniques, which accelerate the construction of CIs by several orders of magnitude, compared with previous approaches as well as to the analytical approximation used by SEs. FIESTA builds accurate CIs rapidly, for example, requiring only several seconds for data sets of tens of thousands of individuals, making FIESTA a very fast solution to the problem of building accurate CIs for heritability for all data set sizes.

Analyzing whether countries are equally efficient at improving longevity for men and women.

PubMed

Barthold, Douglas; Nandi, Arijit; Mendoza Rodríguez, José M; Heymann, Jody

2014-11-01

We examined the efficiency of country-specific health care spending in improving life expectancies for men and women. We estimated efficiencies of health care spending for 27 Organisation for Economic Co-operation and Development (OECD) countries during the period 1991 to 2007 using multivariable regression models, including country fixed-effects and controlling for time-varying levels of national social expenditures, economic development, and health behaviors. Findings indicated robust differences in health-spending efficiency. A 1% annual increase in health expenditures was associated with percent changes in life expectancy ranging from 0.020 in the United States (95% confidence interval [CI] = 0.008, 0.032) to 0.121 in Germany (95% CI = 0.099, 0.143). Health-spending increases were associated with greater life expectancy improvements for men than for women in nearly every OECD country. This is the first study to our knowledge to estimate the effect of country-specific health expenditures on life expectancies of men and women. Future work understanding the determinants of these differences has the potential to improve the overall efficiency and equity of national health systems.

Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study

PubMed Central

Shah, Anoop D.; Bartlett, Jonathan W.; Carpenter, James; Nicholas, Owen; Hemingway, Harry

2014-01-01

Multivariate imputation by chained equations (MICE) is commonly used for imputing missing data in epidemiologic research. The “true” imputation model may contain nonlinearities which are not included in default imputation models. Random forest imputation is a machine learning technique which can accommodate nonlinearities and interactions and does not require a particular regression model to be specified. We compared parametric MICE with a random forest-based MICE algorithm in 2 simulation studies. The first study used 1,000 random samples of 2,000 persons drawn from the 10,128 stable angina patients in the CALIBER database (Cardiovascular Disease Research using Linked Bespoke Studies and Electronic Records; 2001–2010) with complete data on all covariates. Variables were artificially made “missing at random,” and the bias and efficiency of parameter estimates obtained using different imputation methods were compared. Both MICE methods produced unbiased estimates of (log) hazard ratios, but random forest was more efficient and produced narrower confidence intervals. The second study used simulated data in which the partially observed variable depended on the fully observed variables in a nonlinear way. Parameter estimates were less biased using random forest MICE, and confidence interval coverage was better. This suggests that random forest imputation may be useful for imputing complex epidemiologic data sets in which some patients have missing data. PMID:24589914

Comparison of random forest and parametric imputation models for imputing missing data using MICE: a CALIBER study.

PubMed

Shah, Anoop D; Bartlett, Jonathan W; Carpenter, James; Nicholas, Owen; Hemingway, Harry

2014-03-15

Multivariate imputation by chained equations (MICE) is commonly used for imputing missing data in epidemiologic research. The "true" imputation model may contain nonlinearities which are not included in default imputation models. Random forest imputation is a machine learning technique which can accommodate nonlinearities and interactions and does not require a particular regression model to be specified. We compared parametric MICE with a random forest-based MICE algorithm in 2 simulation studies. The first study used 1,000 random samples of 2,000 persons drawn from the 10,128 stable angina patients in the CALIBER database (Cardiovascular Disease Research using Linked Bespoke Studies and Electronic Records; 2001-2010) with complete data on all covariates. Variables were artificially made "missing at random," and the bias and efficiency of parameter estimates obtained using different imputation methods were compared. Both MICE methods produced unbiased estimates of (log) hazard ratios, but random forest was more efficient and produced narrower confidence intervals. The second study used simulated data in which the partially observed variable depended on the fully observed variables in a nonlinear way. Parameter estimates were less biased using random forest MICE, and confidence interval coverage was better. This suggests that random forest imputation may be useful for imputing complex epidemiologic data sets in which some patients have missing data.

Life cycle efficiency of beef production: II. Relationship of cow efficiency ratios to traits of the dam and progeny weaned.

PubMed

Davis, M E; Rutledge, J J; Cundiff, L V; Hauser, E R

1983-10-01

Several measures of life cycle cow efficiency were calculated using weights and individual feed consumptions recorded on 160 dams of beef, dairy and beef X dairy breeding and their progeny. Ratios of output to input were used to estimate efficiency, where outputs included weaning weights of progeny plus salvage value of the dam and inputs included creep feed consumed by progeny plus feed consumed by the dam over her entire lifetime. In one approach to estimating efficiency, inputs and outputs were weighted by probabilities that were a function of the cow herd age distribution and percentage calf crop in a theoretical herd. The second approach to estimating cow efficiency involved dividing the sum of the weights by the sum of the feed consumption values, with all pieces of information being given equal weighting. Relationships among efficiency estimates and various traits of dams and progeny were examined. Weights, heights, and weight:height ratios of dams at 240 d of age were not correlated significantly with subsequent efficiency of calf production, indicating that indirect selection for lifetime cow efficiency at an early age based on these traits would be ineffective. However, females exhibiting more efficient weight gains from 240 d to first calving tended to become more efficient dams. Correlations of efficiency with weight of dam at calving and at weaning were negative and generally highly significant. Height at withers was negatively related to efficiency. Ratio of weight to height indicated that fatter dams generally were less efficient. The effect of milk production on efficiency depended upon the breed combinations involved. Dams calving for the first time at an early age and continuing to calve at short intervals were superior in efficiency. Weaning rate was closely related to life cycle efficiency. Large negative correlations between efficiency and feed consumption of dams were observed, while correlations of efficiency with progeny weights and feed consumptions in individual parities tended to be positive though nonsignificant. However, correlations of efficiency with accumulative progeny weights and feed consumptions generally were significant.

Outcome-Dependent Sampling with Interval-Censored Failure Time Data

PubMed Central

Zhou, Qingning; Cai, Jianwen; Zhou, Haibo

2017-01-01

Summary Epidemiologic studies and disease prevention trials often seek to relate an exposure variable to a failure time that suffers from interval-censoring. When the failure rate is low and the time intervals are wide, a large cohort is often required so as to yield reliable precision on the exposure-failure-time relationship. However, large cohort studies with simple random sampling could be prohibitive for investigators with a limited budget, especially when the exposure variables are expensive to obtain. Alternative cost-effective sampling designs and inference procedures are therefore desirable. We propose an outcome-dependent sampling (ODS) design with interval-censored failure time data, where we enrich the observed sample by selectively including certain more informative failure subjects. We develop a novel sieve semiparametric maximum empirical likelihood approach for fitting the proportional hazards model to data from the proposed interval-censoring ODS design. This approach employs the empirical likelihood and sieve methods to deal with the infinite-dimensional nuisance parameters, which greatly reduces the dimensionality of the estimation problem and eases the computation difficulty. The consistency and asymptotic normality of the resulting regression parameter estimator are established. The results from our extensive simulation study show that the proposed design and method works well for practical situations and is more efficient than the alternative designs and competing approaches. An example from the Atherosclerosis Risk in Communities (ARIC) study is provided for illustration. PMID:28771664

Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning

NASA Astrophysics Data System (ADS)

Veronesi, F.; Grassi, S.

2016-09-01

Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners.

Empirical Bayes Gaussian likelihood estimation of exposure distributions from pooled samples in human biomonitoring.

PubMed

Li, Xiang; Kuk, Anthony Y C; Xu, Jinfeng

2014-12-10

Human biomonitoring of exposure to environmental chemicals is important. Individual monitoring is not viable because of low individual exposure level or insufficient volume of materials and the prohibitive cost of taking measurements from many subjects. Pooling of samples is an efficient and cost-effective way to collect data. Estimation is, however, complicated as individual values within each pool are not observed but are only known up to their average or weighted average. The distribution of such averages is intractable when the individual measurements are lognormally distributed, which is a common assumption. We propose to replace the intractable distribution of the pool averages by a Gaussian likelihood to obtain parameter estimates. If the pool size is large, this method produces statistically efficient estimates, but regardless of pool size, the method yields consistent estimates as the number of pools increases. An empirical Bayes (EB) Gaussian likelihood approach, as well as its Bayesian analog, is developed to pool information from various demographic groups by using a mixed-effect formulation. We also discuss methods to estimate the underlying mean-variance relationship and to select a good model for the means, which can be incorporated into the proposed EB or Bayes framework. By borrowing strength across groups, the EB estimator is more efficient than the individual group-specific estimator. Simulation results show that the EB Gaussian likelihood estimates outperform a previous method proposed for the National Health and Nutrition Examination Surveys with much smaller bias and better coverage in interval estimation, especially after correction of bias. Copyright © 2014 John Wiley & Sons, Ltd.

A methodology for airplane parameter estimation and confidence interval determination in nonlinear estimation problems. Ph.D. Thesis - George Washington Univ., Apr. 1985

NASA Technical Reports Server (NTRS)

Murphy, P. C.

1986-01-01

An algorithm for maximum likelihood (ML) estimation is developed with an efficient method for approximating the sensitivities. The ML algorithm relies on a new optimization method referred to as a modified Newton-Raphson with estimated sensitivities (MNRES). MNRES determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. With the fitted surface, sensitivity information can be updated at each iteration with less computational effort than that required by either a finite-difference method or integration of the analytically determined sensitivity equations. MNRES eliminates the need to derive sensitivity equations for each new model, and thus provides flexibility to use model equations in any convenient format. A random search technique for determining the confidence limits of ML parameter estimates is applied to nonlinear estimation problems for airplanes. The confidence intervals obtained by the search are compared with Cramer-Rao (CR) bounds at the same confidence level. The degree of nonlinearity in the estimation problem is an important factor in the relationship between CR bounds and the error bounds determined by the search technique. Beale's measure of nonlinearity is developed in this study for airplane identification problems; it is used to empirically correct confidence levels and to predict the degree of agreement between CR bounds and search estimates.

Statistical estimate of mercury removal efficiencies for air pollution control devices of municipal solid waste incinerators.

PubMed

Takahashi, Fumitake; Kida, Akiko; Shimaoka, Takayuki

2010-10-15

Although representative removal efficiencies of gaseous mercury for air pollution control devices (APCDs) are important to prepare more reliable atmospheric emission inventories of mercury, they have been still uncertain because they depend sensitively on many factors like the type of APCDs, gas temperature, and mercury speciation. In this study, representative removal efficiencies of gaseous mercury for several types of APCDs of municipal solid waste incineration (MSWI) were offered using a statistical method. 534 data of mercury removal efficiencies for APCDs used in MSWI were collected. APCDs were categorized as fixed-bed absorber (FA), wet scrubber (WS), electrostatic precipitator (ESP), and fabric filter (FF), and their hybrid systems. Data series of all APCD types had Gaussian log-normality. The average removal efficiency with a 95% confidence interval for each APCD was estimated. The FA, WS, and FF with carbon and/or dry sorbent injection systems had 75% to 82% average removal efficiencies. On the other hand, the ESP with/without dry sorbent injection had lower removal efficiencies of up to 22%. The type of dry sorbent injection in the FF system, dry or semi-dry, did not make more than 1% difference to the removal efficiency. The injection of activated carbon and carbon-containing fly ash in the FF system made less than 3% difference. Estimation errors of removal efficiency were especially high for the ESP. The national average of removal efficiency of APCDs in Japanese MSWI plants was estimated on the basis of incineration capacity. Owing to the replacement of old APCDs for dioxin control, the national average removal efficiency increased from 34.5% in 1991 to 92.5% in 2003. This resulted in an additional reduction of about 0.86Mg emission in 2003. Further study using the methodology in this study to other important emission sources like coal-fired power plants will contribute to better emission inventories. Copyright © 2010 Elsevier B.V. All rights reserved.

Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors.

PubMed

Burgess, Stephen; Scott, Robert A; Timpson, Nicholas J; Davey Smith, George; Thompson, Simon G

2015-07-01

Finding individual-level data for adequately-powered Mendelian randomization analyses may be problematic. As publicly-available summarized data on genetic associations with disease outcomes from large consortia are becoming more abundant, use of published data is an attractive analysis strategy for obtaining precise estimates of the causal effects of risk factors on outcomes. We detail the necessary steps for conducting Mendelian randomization investigations using published data, and present novel statistical methods for combining data on the associations of multiple (correlated or uncorrelated) genetic variants with the risk factor and outcome into a single causal effect estimate. A two-sample analysis strategy may be employed, in which evidence on the gene-risk factor and gene-outcome associations are taken from different data sources. These approaches allow the efficient identification of risk factors that are suitable targets for clinical intervention from published data, although the ability to assess the assumptions necessary for causal inference is diminished. Methods and guidance are illustrated using the example of the causal effect of serum calcium levels on fasting glucose concentrations. The estimated causal effect of a 1 standard deviation (0.13 mmol/L) increase in calcium levels on fasting glucose (mM) using a single lead variant from the CASR gene region is 0.044 (95 % credible interval -0.002, 0.100). In contrast, using our method to account for the correlation between variants, the corresponding estimate using 17 genetic variants is 0.022 (95 % credible interval 0.009, 0.035), a more clearly positive causal effect.

An algorithm for computing moments-based flood quantile estimates when historical flood information is available

USGS Publications Warehouse

Cohn, T.A.; Lane, W.L.; Baier, W.G.

1997-01-01

This paper presents the expected moments algorithm (EMA), a simple and efficient method for incorporating historical and paleoflood information into flood frequency studies. EMA can utilize three types of at-site flood information: systematic stream gage record; information about the magnitude of historical floods; and knowledge of the number of years in the historical period when no large flood occurred. EMA employs an iterative procedure to compute method-of-moments parameter estimates. Initial parameter estimates are calculated from systematic stream gage data. These moments are then updated by including the measured historical peaks and the expected moments, given the previously estimated parameters, of the below-threshold floods from the historical period. The updated moments result in new parameter estimates, and the last two steps are repeated until the algorithm converges. Monte Carlo simulations compare EMA, Bulletin 17B's [United States Water Resources Council, 1982] historically weighted moments adjustment, and maximum likelihood estimators when fitting the three parameters of the log-Pearson type III distribution. These simulations demonstrate that EMA is more efficient than the Bulletin 17B method, and that it is nearly as efficient as maximum likelihood estimation (MLE). The experiments also suggest that EMA has two advantages over MLE when dealing with the log-Pearson type III distribution: It appears that EMA estimates always exist and that they are unique, although neither result has been proven. EMA can be used with binomial or interval-censored data and with any distributional family amenable to method-of-moments estimation.

An algorithm for computing moments-based flood quantile estimates when historical flood information is available

NASA Astrophysics Data System (ADS)

Cohn, T. A.; Lane, W. L.; Baier, W. G.

This paper presents the expected moments algorithm (EMA), a simple and efficient method for incorporating historical and paleoflood information into flood frequency studies. EMA can utilize three types of at-site flood information: systematic stream gage record; information about the magnitude of historical floods; and knowledge of the number of years in the historical period when no large flood occurred. EMA employs an iterative procedure to compute method-of-moments parameter estimates. Initial parameter estimates are calculated from systematic stream gage data. These moments are then updated by including the measured historical peaks and the expected moments, given the previously estimated parameters, of the below-threshold floods from the historical period. The updated moments result in new parameter estimates, and the last two steps are repeated until the algorithm converges. Monte Carlo simulations compare EMA, Bulletin 17B's [United States Water Resources Council, 1982] historically weighted moments adjustment, and maximum likelihood estimators when fitting the three parameters of the log-Pearson type III distribution. These simulations demonstrate that EMA is more efficient than the Bulletin 17B method, and that it is nearly as efficient as maximum likelihood estimation (MLE). The experiments also suggest that EMA has two advantages over MLE when dealing with the log-Pearson type III distribution: It appears that EMA estimates always exist and that they are unique, although neither result has been proven. EMA can be used with binomial or interval-censored data and with any distributional family amenable to method-of-moments estimation.

Comparing interval estimates for small sample ordinal CFA models

PubMed Central

Natesan, Prathiba

2015-01-01

Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading. Therefore, editors and policymakers should continue to emphasize the inclusion of interval estimates in research. PMID:26579002

Comparing interval estimates for small sample ordinal CFA models.

PubMed

Natesan, Prathiba

2015-01-01

Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading. Therefore, editors and policymakers should continue to emphasize the inclusion of interval estimates in research.

Statistical power analysis in wildlife research

USGS Publications Warehouse

Steidl, R.J.; Hayes, J.P.

1997-01-01

Statistical power analysis can be used to increase the efficiency of research efforts and to clarify research results. Power analysis is most valuable in the design or planning phases of research efforts. Such prospective (a priori) power analyses can be used to guide research design and to estimate the number of samples necessary to achieve a high probability of detecting biologically significant effects. Retrospective (a posteriori) power analysis has been advocated as a method to increase information about hypothesis tests that were not rejected. However, estimating power for tests of null hypotheses that were not rejected with the effect size observed in the study is incorrect; these power estimates will always be a??0.50 when bias adjusted and have no relation to true power. Therefore, retrospective power estimates based on the observed effect size for hypothesis tests that were not rejected are misleading; retrospective power estimates are only meaningful when based on effect sizes other than the observed effect size, such as those effect sizes hypothesized to be biologically significant. Retrospective power analysis can be used effectively to estimate the number of samples or effect size that would have been necessary for a completed study to have rejected a specific null hypothesis. Simply presenting confidence intervals can provide additional information about null hypotheses that were not rejected, including information about the size of the true effect and whether or not there is adequate evidence to 'accept' a null hypothesis as true. We suggest that (1) statistical power analyses be routinely incorporated into research planning efforts to increase their efficiency, (2) confidence intervals be used in lieu of retrospective power analyses for null hypotheses that were not rejected to assess the likely size of the true effect, (3) minimum biologically significant effect sizes be used for all power analyses, and (4) if retrospective power estimates are to be reported, then the I?-level, effect sizes, and sample sizes used in calculations must also be reported.

Correcting for bias in the selection and validation of informative diagnostic tests.

PubMed

Robertson, David S; Prevost, A Toby; Bowden, Jack

2015-04-15

When developing a new diagnostic test for a disease, there are often multiple candidate classifiers to choose from, and it is unclear if any will offer an improvement in performance compared with current technology. A two-stage design can be used to select a promising classifier (if one exists) in stage one for definitive validation in stage two. However, estimating the true properties of the chosen classifier is complicated by the first stage selection rules. In particular, the usual maximum likelihood estimator (MLE) that combines data from both stages will be biased high. Consequently, confidence intervals and p-values flowing from the MLE will also be incorrect. Building on the results of Pepe et al. (SIM 28:762-779), we derive the most efficient conditionally unbiased estimator and exact confidence intervals for a classifier's sensitivity in a two-stage design with arbitrary selection rules; the condition being that the trial proceeds to the validation stage. We apply our estimation strategy to data from a recent family history screening tool validation study by Walter et al. (BJGP 63:393-400) and are able to identify and successfully adjust for bias in the tool's estimated sensitivity to detect those at higher risk of breast cancer. © 2015 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Data-Driven Benchmarking of Building Energy Efficiency Utilizing Statistical Frontier Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kavousian, A; Rajagopal, R

2014-01-01

Frontier methods quantify the energy efficiency of buildings by forming an efficient frontier (best-practice technology) and by comparing all buildings against that frontier. Because energy consumption fluctuates over time, the efficiency scores are stochastic random variables. Existing applications of frontier methods in energy efficiency either treat efficiency scores as deterministic values or estimate their uncertainty by resampling from one set of measurements. Availability of smart meter data (repeated measurements of energy consumption of buildings) enables using actual data to estimate the uncertainty in efficiency scores. Additionally, existing applications assume a linear form for an efficient frontier; i.e.,they assume that themore » best-practice technology scales up and down proportionally with building characteristics. However, previous research shows that buildings are nonlinear systems. This paper proposes a statistical method called stochastic energy efficiency frontier (SEEF) to estimate a bias-corrected efficiency score and its confidence intervals from measured data. The paper proposes an algorithm to specify the functional form of the frontier, identify the probability distribution of the efficiency score of each building using measured data, and rank buildings based on their energy efficiency. To illustrate the power of SEEF, this paper presents the results from applying SEEF on a smart meter data set of 307 residential buildings in the United States. SEEF efficiency scores are used to rank individual buildings based on energy efficiency, to compare subpopulations of buildings, and to identify irregular behavior of buildings across different time-of-use periods. SEEF is an improvement to the energy-intensity method (comparing kWh/sq.ft.): whereas SEEF identifies efficient buildings across the entire spectrum of building sizes, the energy-intensity method showed bias toward smaller buildings. The results of this research are expected to assist researchers and practitioners compare and rank (i.e.,benchmark) buildings more robustly and over a wider range of building types and sizes. Eventually, doing so is expected to result in improved resource allocation in energy-efficiency programs.« less

Analyzing Whether Countries Are Equally Efficient at Improving Longevity for Men and Women

PubMed Central

Nandi, Arijit; Mendoza Rodríguez, José M.; Heymann, Jody

2014-01-01

Objectives. We examined the efficiency of country-specific health care spending in improving life expectancies for men and women. Methods. We estimated efficiencies of health care spending for 27 Organisation for Economic Co-operation and Development (OECD) countries during the period 1991 to 2007 using multivariable regression models, including country fixed-effects and controlling for time-varying levels of national social expenditures, economic development, and health behaviors. Results. Findings indicated robust differences in health-spending efficiency. A 1% annual increase in health expenditures was associated with percent changes in life expectancy ranging from 0.020 in the United States (95% confidence interval [CI] = 0.008, 0.032) to 0.121 in Germany (95% CI = 0.099, 0.143). Health-spending increases were associated with greater life expectancy improvements for men than for women in nearly every OECD country. Conclusions. This is the first study to our knowledge to estimate the effect of country-specific health expenditures on life expectancies of men and women. Future work understanding the determinants of these differences has the potential to improve the overall efficiency and equity of national health systems. PMID:24328639

Doubly Robust and Efficient Estimation of Marginal Structural Models for the Hazard Function

PubMed Central

Zheng, Wenjing; Petersen, Maya; van der Laan, Mark

2016-01-01

In social and health sciences, many research questions involve understanding the causal effect of a longitudinal treatment on mortality (or time-to-event outcomes in general). Often, treatment status may change in response to past covariates that are risk factors for mortality, and in turn, treatment status may also affect such subsequent covariates. In these situations, Marginal Structural Models (MSMs), introduced by Robins (1997), are well-established and widely used tools to account for time-varying confounding. In particular, a MSM can be used to specify the intervention-specific counterfactual hazard function, i.e. the hazard for the outcome of a subject in an ideal experiment where he/she was assigned to follow a given intervention on their treatment variables. The parameters of this hazard MSM are traditionally estimated using the Inverse Probability Weighted estimation (IPTW, van der Laan and Petersen (2007), Robins et al. (2000b), Robins (1999), Robins et al. (2008)). This estimator is easy to implement and admits Wald-type confidence intervals. However, its consistency hinges on the correct specification of the treatment allocation probabilities, and the estimates are generally sensitive to large treatment weights (especially in the presence of strong confounding), which are difficult to stabilize for dynamic treatment regimes. In this paper, we present a pooled targeted maximum likelihood estimator (TMLE, van der Laan and Rubin (2006)) for MSM for the hazard function under longitudinal dynamic treatment regimes. The proposed estimator is semiparametric efficient and doubly robust, hence offers bias reduction and efficiency gain over the incumbent IPTW estimator. Moreover, the substitution principle rooted in the TMLE potentially mitigates the sensitivity to large treatment weights in IPTW. We compare the performance of the proposed estimator with the IPTW and a non-targeted substitution estimator in a simulation study. PMID:27227723

A hierarchical model for estimating change in American Woodcock populations

USGS Publications Warehouse

Sauer, J.R.; Link, W.A.; Kendall, W.L.; Kelley, J.R.; Niven, D.K.

2008-01-01

The Singing-Ground Survey (SGS) is a primary source of information on population change for American woodcock (Scolopax minor). We analyzed the SGS using a hierarchical log-linear model and compared the estimates of change and annual indices of abundance to a route regression analysis of SGS data. We also grouped SGS routes into Bird Conservation Regions (BCRs) and estimated population change and annual indices using BCRs within states and provinces as strata. Based on the hierarchical model?based estimates, we concluded that woodcock populations were declining in North America between 1968 and 2006 (trend = -0.9%/yr, 95% credible interval: -1.2, -0.5). Singing-Ground Survey results are generally similar between analytical approaches, but the hierarchical model has several important advantages over the route regression. Hierarchical models better accommodate changes in survey efficiency over time and space by treating strata, years, and observers as random effects in the context of a log-linear model, providing trend estimates that are derived directly from the annual indices. We also conducted a hierarchical model analysis of woodcock data from the Christmas Bird Count and the North American Breeding Bird Survey. All surveys showed general consistency in patterns of population change, but the SGS had the shortest credible intervals. We suggest that population management and conservation planning for woodcock involving interpretation of the SGS use estimates provided by the hierarchical model.

Small area variation in diabetes prevalence in Puerto Rico.

PubMed

Tierney, Edward F; Burrows, Nilka R; Barker, Lawrence E; Beckles, Gloria L; Boyle, James P; Cadwell, Betsy L; Kirtland, Karen A; Thompson, Theodore J

2013-06-01

To estimate the 2009 prevalence of diagnosed diabetes in Puerto Rico among adults ≥ 20 years of age in order to gain a better understanding of its geographic distribution so that policymakers can more efficiently target prevention and control programs. A Bayesian multilevel model was fitted to the combined 2008-2010 Behavioral Risk Factor Surveillance System and 2009 United States Census data to estimate diabetes prevalence for each of the 78 municipios (counties) in Puerto Rico. The mean unadjusted estimate for all counties was 14.3% (range by county, 9.9%-18.0%). The average width of the confidence intervals was 6.2%. Adjusted and unadjusted estimates differed little. These 78 county estimates are higher on average and showed less variability (i.e., had a smaller range) than the previously published estimates of the 2008 diabetes prevalence for all United States counties (mean, 9.9%; range, 3.0%-18.2%).

Semiparametric regression analysis of interval-censored competing risks data.

PubMed

Mao, Lu; Lin, Dan-Yu; Zeng, Donglin

2017-09-01

Interval-censored competing risks data arise when each study subject may experience an event or failure from one of several causes and the failure time is not observed directly but rather is known to lie in an interval between two examinations. We formulate the effects of possibly time-varying (external) covariates on the cumulative incidence or sub-distribution function of competing risks (i.e., the marginal probability of failure from a specific cause) through a broad class of semiparametric regression models that captures both proportional and non-proportional hazards structures for the sub-distribution. We allow each subject to have an arbitrary number of examinations and accommodate missing information on the cause of failure. We consider nonparametric maximum likelihood estimation and devise a fast and stable EM-type algorithm for its computation. We then establish the consistency, asymptotic normality, and semiparametric efficiency of the resulting estimators for the regression parameters by appealing to modern empirical process theory. In addition, we show through extensive simulation studies that the proposed methods perform well in realistic situations. Finally, we provide an application to a study on HIV-1 infection with different viral subtypes. © 2017, The International Biometric Society.

Mixture models for undiagnosed prevalent disease and interval-censored incident disease: applications to a cohort assembled from electronic health records.

PubMed

Cheung, Li C; Pan, Qing; Hyun, Noorie; Schiffman, Mark; Fetterman, Barbara; Castle, Philip E; Lorey, Thomas; Katki, Hormuzd A

2017-09-30

For cost-effectiveness and efficiency, many large-scale general-purpose cohort studies are being assembled within large health-care providers who use electronic health records. Two key features of such data are that incident disease is interval-censored between irregular visits and there can be pre-existing (prevalent) disease. Because prevalent disease is not always immediately diagnosed, some disease diagnosed at later visits are actually undiagnosed prevalent disease. We consider prevalent disease as a point mass at time zero for clinical applications where there is no interest in time of prevalent disease onset. We demonstrate that the naive Kaplan-Meier cumulative risk estimator underestimates risks at early time points and overestimates later risks. We propose a general family of mixture models for undiagnosed prevalent disease and interval-censored incident disease that we call prevalence-incidence models. Parameters for parametric prevalence-incidence models, such as the logistic regression and Weibull survival (logistic-Weibull) model, are estimated by direct likelihood maximization or by EM algorithm. Non-parametric methods are proposed to calculate cumulative risks for cases without covariates. We compare naive Kaplan-Meier, logistic-Weibull, and non-parametric estimates of cumulative risk in the cervical cancer screening program at Kaiser Permanente Northern California. Kaplan-Meier provided poor estimates while the logistic-Weibull model was a close fit to the non-parametric. Our findings support our use of logistic-Weibull models to develop the risk estimates that underlie current US risk-based cervical cancer screening guidelines. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Automatic estimation of voice onset time for word-initial stops by applying random forest to onset detection.

PubMed

Lin, Chi-Yueh; Wang, Hsiao-Chuan

2011-07-01

The voice onset time (VOT) of a stop consonant is the interval between its burst onset and voicing onset. Among a variety of research topics on VOT, one that has been studied for years is how VOTs are efficiently measured. Manual annotation is a feasible way, but it becomes a time-consuming task when the corpus size is large. This paper proposes an automatic VOT estimation method based on an onset detection algorithm. At first, a forced alignment is applied to identify the locations of stop consonants. Then a random forest based onset detector searches each stop segment for its burst and voicing onsets to estimate a VOT. The proposed onset detection can detect the onsets in an efficient and accurate manner with only a small amount of training data. The evaluation data extracted from the TIMIT corpus were 2344 words with a word-initial stop. The experimental results showed that 83.4% of the estimations deviate less than 10 ms from their manually labeled values, and 96.5% of the estimations deviate by less than 20 ms. Some factors that influence the proposed estimation method, such as place of articulation, voicing of a stop consonant, and quality of succeeding vowel, were also investigated. © 2011 Acoustical Society of America

Development of method for evaluating estimated inundation area by using river flood analysis based on multiple flood scenarios

NASA Astrophysics Data System (ADS)

Ono, T.; Takahashi, T.

2017-12-01

Non-structural mitigation measures such as flood hazard map based on estimated inundation area have been more important because heavy rains exceeding the design rainfall frequently occur in recent years. However, conventional method may lead to an underestimation of the area because assumed locations of dike breach in river flood analysis are limited to the cases exceeding the high-water level. The objective of this study is to consider the uncertainty of estimated inundation area with difference of the location of dike breach in river flood analysis. This study proposed multiple flood scenarios which can set automatically multiple locations of dike breach in river flood analysis. The major premise of adopting this method is not to be able to predict the location of dike breach correctly. The proposed method utilized interval of dike breach which is distance of dike breaches placed next to each other. That is, multiple locations of dike breach were set every interval of dike breach. The 2D shallow water equations was adopted as the governing equation of river flood analysis, and the leap-frog scheme with staggered grid was used. The river flood analysis was verified by applying for the 2015 Kinugawa river flooding, and the proposed multiple flood scenarios was applied for the Akutagawa river in Takatsuki city. As the result of computation in the Akutagawa river, a comparison with each computed maximum inundation depth of dike breaches placed next to each other proved that the proposed method enabled to prevent underestimation of estimated inundation area. Further, the analyses on spatial distribution of inundation class and maximum inundation depth in each of the measurement points also proved that the optimum interval of dike breach which can evaluate the maximum inundation area using the minimum assumed locations of dike breach. In brief, this study found the optimum interval of dike breach in the Akutagawa river, which enabled estimated maximum inundation area to predict efficiently and accurately. The river flood analysis by using this proposed method will contribute to mitigate flood disaster by improving the accuracy of estimated inundation area.

Structural reliability analysis under evidence theory using the active learning kriging model

NASA Astrophysics Data System (ADS)

Yang, Xufeng; Liu, Yongshou; Ma, Panke

2017-11-01

Structural reliability analysis under evidence theory is investigated. It is rigorously proved that a surrogate model providing only correct sign prediction of the performance function can meet the accuracy requirement of evidence-theory-based reliability analysis. Accordingly, a method based on the active learning kriging model which only correctly predicts the sign of the performance function is proposed. Interval Monte Carlo simulation and a modified optimization method based on Karush-Kuhn-Tucker conditions are introduced to make the method more efficient in estimating the bounds of failure probability based on the kriging model. Four examples are investigated to demonstrate the efficiency and accuracy of the proposed method.

Associations Between Supportive Leadership Behavior and the Costs of Absenteeism and Presenteeism: An Epidemiological and Economic Approach.

PubMed

Schmid, Johannes Albert; Jarczok, Marc Nicolas; Sonntag, Diana; Herr, Raphael Manfred; Fischer, Joachim Ernst; Schmidt, Burkhard

2017-02-01

This study investigates associations between supportive leadership behavior (SLB) and presenteeism/absenteeism, and estimates related costs. Cross-sectional data from a German industrial sample (n = 17,060) assessing SLB and presenteeism/absenteeism were used. Adjusted interval regressions were performed. The study population was split into tertiles with respect to SLB, and minimum and maximum costs for each tertile were estimated on the basis of national industry averages. Low SLB was associated with higher presenteeism [-0.31, 95% confidence interval (95% CI) -0.33 to -0.28)] and absenteeism (-0.36, 95% CI -0.40 to -0.32). Compared with high SLB, the costs of low SLB for absenteeism are between 534.54 and 1675.16 Euro higher per person and year. For presenteeism, this difference ranges between 63.76 and 433.7 Euro. SLB has the potential to reduce absenteeism, presenteeism, and associated costs. To contribute to workforce health, productivity, and efficiency, SLB merits being fostered by corporate policy.

A Possible Link between Supra-Second Open-Ended Timing Sensitivity and Obsessive-Compulsive Tendencies

PubMed Central

Gilaie-Dotan, Sharon; Ashkenazi, Hamutal; Dar, Reuven

2016-01-01

One of the main characteristics of obsessive-compulsive disorder (OCD) is the persistent feeling of uncertainty, affecting many domains of actions and feelings. It was recently hypothesized that OCD uncertainty is related to attenuated access to internal states. As supra-second timing is associated with bodily and interoceptive awareness, we examined whether supra-second timing would be associated with OC tendencies. We measured supra-second (~9 s) and sub-second (~450 ms) timing along with control non-temporal perceptual tasks in a group of 60 university students. Supra-second timing was measured either with fixed criterion tasks requiring to temporally discriminate between two predefined fixed interval durations (9 vs. 9.9 s), or with an open-ended task requiring to discriminate between 9 s and longer intervals which were of varying durations that were not a priori known to the participants. The open-ended task employed an adaptive Bayesian procedure that efficiently estimated the duration difference required to discriminate 9 s from longer intervals. We also assessed symptoms of OCD, depression, and anxiety. Open-ended supra-second temporal sensitivity was correlated with OC tendencies, as predicted (even after controlling for depression and anxiety), whereas the other tasks were not. Higher OC tendencies were associated with lower timing sensitivity to 9 s intervals such that participants with higher OC tendency scores required longer interval differences to discriminate 9 s from longer intervals. While these results need to be substantiated in future research, they suggest that open-ended timing tasks, as those encountered in real-life (e.g., estimating how long it would take to complete a task), might be adversely affected in OCD. PMID:27445725

[Theory, method and application of method R on estimation of (co)variance components].

PubMed

Liu, Wen-Zhong

2004-07-01

Theory, method and application of Method R on estimation of (co)variance components were reviewed in order to make the method be reasonably used. Estimation requires R values,which are regressions of predicted random effects that are calculated using complete dataset on predicted random effects that are calculated using random subsets of the same data. By using multivariate iteration algorithm based on a transformation matrix,and combining with the preconditioned conjugate gradient to solve the mixed model equations, the computation efficiency of Method R is much improved. Method R is computationally inexpensive,and the sampling errors and approximate credible intervals of estimates can be obtained. Disadvantages of Method R include a larger sampling variance than other methods for the same data,and biased estimates in small datasets. As an alternative method, Method R can be used in larger datasets. It is necessary to study its theoretical properties and broaden its application range further.

H∞ state estimation of stochastic memristor-based neural networks with time-varying delays.

PubMed

Bao, Haibo; Cao, Jinde; Kurths, Jürgen; Alsaedi, Ahmed; Ahmad, Bashir

2018-03-01

This paper addresses the problem of H ∞ state estimation for a class of stochastic memristor-based neural networks with time-varying delays. Under the framework of Filippov solution, the stochastic memristor-based neural networks are transformed into systems with interval parameters. The present paper is the first to investigate the H ∞ state estimation problem for continuous-time Itô-type stochastic memristor-based neural networks. By means of Lyapunov functionals and some stochastic technique, sufficient conditions are derived to ensure that the estimation error system is asymptotically stable in the mean square with a prescribed H ∞ performance. An explicit expression of the state estimator gain is given in terms of linear matrix inequalities (LMIs). Compared with other results, our results reduce control gain and control cost effectively. Finally, numerical simulations are provided to demonstrate the efficiency of the theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

Variation in polyp size estimation among endoscopists and impact on surveillance intervals.

PubMed

Chaptini, Louis; Chaaya, Adib; Depalma, Fedele; Hunter, Krystal; Peikin, Steven; Laine, Loren

2014-10-01

Accurate estimation of polyp size is important because it is used to determine the surveillance interval after polypectomy. To evaluate the variation and accuracy in polyp size estimation among endoscopists and the impact on surveillance intervals after polypectomy. Web-based survey. A total of 873 members of the American Society for Gastrointestinal Endoscopy. Participants watched video recordings of 4 polypectomies and were asked to estimate the polyp sizes. Proportion of participants with polyp size estimates within 20% of the correct measurement and the frequency of incorrect surveillance intervals based on inaccurate size estimates. Polyp size estimates were within 20% of the correct value for 1362 (48%) of 2812 estimates (range 39%-59% for the 4 polyps). Polyp size was overestimated by >20% in 889 estimates (32%, range 15%-49%) and underestimated by >20% in 561 (20%, range 4%-46%) estimates. Incorrect surveillance intervals because of overestimation or underestimation occurred in 272 (10%) of the 2812 estimates (range 5%-14%). Participants in a private practice setting overestimated the size of 3 or of all 4 polyps by >20% more often than participants in an academic setting (difference = 7%; 95% confidence interval, 1%-11%). Survey design with the use of video clips. Substantial overestimation and underestimation of polyp size occurs with visual estimation leading to incorrect surveillance intervals in 10% of cases. Our findings support routine use of measurement tools to improve polyp size estimates. Copyright © 2014 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

A fast estimation of shock wave pressure based on trend identification

NASA Astrophysics Data System (ADS)

Yao, Zhenjian; Wang, Zhongyu; Wang, Chenchen; Lv, Jing

2018-04-01

In this paper, a fast method based on trend identification is proposed to accurately estimate the shock wave pressure in a dynamic measurement. Firstly, the collected output signal of the pressure sensor is reconstructed by discrete cosine transform (DCT) to reduce the computational complexity for the subsequent steps. Secondly, the empirical mode decomposition (EMD) is applied to decompose the reconstructed signal into several components with different frequency-bands, and the last few low-frequency components are chosen to recover the trend of the reconstructed signal. In the meantime, the optimal component number is determined based on the correlation coefficient and the normalized Euclidean distance between the trend and the reconstructed signal. Thirdly, with the areas under the gradient curve of the trend signal, the stable interval that produces the minimum can be easily identified. As a result, the stable value of the output signal is achieved in this interval. Finally, the shock wave pressure can be estimated according to the stable value of the output signal and the sensitivity of the sensor in the dynamic measurement. A series of shock wave pressure measurements are carried out with a shock tube system to validate the performance of this method. The experimental results show that the proposed method works well in shock wave pressure estimation. Furthermore, comparative experiments also demonstrate the superiority of the proposed method over the existing approaches in both estimation accuracy and computational efficiency.

Bayesian estimation of dynamic matching function for U-V analysis in Japan

NASA Astrophysics Data System (ADS)

Kyo, Koki; Noda, Hideo; Kitagawa, Genshiro

2012-05-01

In this paper we propose a Bayesian method for analyzing unemployment dynamics. We derive a Beveridge curve for unemployment and vacancy (U-V) analysis from a Bayesian model based on a labor market matching function. In our framework, the efficiency of matching and the elasticities of new hiring with respect to unemployment and vacancy are regarded as time varying parameters. To construct a flexible model and obtain reasonable estimates in an underdetermined estimation problem, we treat the time varying parameters as random variables and introduce smoothness priors. The model is then described in a state space representation, enabling the parameter estimation to be carried out using Kalman filter and fixed interval smoothing. In such a representation, dynamic features of the cyclic unemployment rate and the structural-frictional unemployment rate can be accurately captured.

Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal's Fail-Safe Number.

PubMed

Fragkos, Konstantinos C; Tsagris, Michail; Frangos, Christos C

2014-01-01

The purpose of the present paper is to assess the efficacy of confidence intervals for Rosenthal's fail-safe number. Although Rosenthal's estimator is highly used by researchers, its statistical properties are largely unexplored. First of all, we developed statistical theory which allowed us to produce confidence intervals for Rosenthal's fail-safe number. This was produced by discerning whether the number of studies analysed in a meta-analysis is fixed or random. Each case produces different variance estimators. For a given number of studies and a given distribution, we provided five variance estimators. Confidence intervals are examined with a normal approximation and a nonparametric bootstrap. The accuracy of the different confidence interval estimates was then tested by methods of simulation under different distributional assumptions. The half normal distribution variance estimator has the best probability coverage. Finally, we provide a table of lower confidence intervals for Rosenthal's estimator.

Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal's Fail-Safe Number

PubMed Central

Fragkos, Konstantinos C.; Tsagris, Michail; Frangos, Christos C.

2014-01-01

The purpose of the present paper is to assess the efficacy of confidence intervals for Rosenthal's fail-safe number. Although Rosenthal's estimator is highly used by researchers, its statistical properties are largely unexplored. First of all, we developed statistical theory which allowed us to produce confidence intervals for Rosenthal's fail-safe number. This was produced by discerning whether the number of studies analysed in a meta-analysis is fixed or random. Each case produces different variance estimators. For a given number of studies and a given distribution, we provided five variance estimators. Confidence intervals are examined with a normal approximation and a nonparametric bootstrap. The accuracy of the different confidence interval estimates was then tested by methods of simulation under different distributional assumptions. The half normal distribution variance estimator has the best probability coverage. Finally, we provide a table of lower confidence intervals for Rosenthal's estimator. PMID:27437470

[Research Progress of Carrion-breeding Phorid Flies for Post-mortem Interval Estimation in Forensic Medicine].

PubMed

Li, L; Feng, D X; Wu, J

2016-10-01

It is a difficult problem of forensic medicine to accurately estimate the post-mortem interval. Entomological approach has been regarded as an effective way to estimate the post-mortem interval. The developmental biology of carrion-breeding flies has an important position at the post-mortem interval estimation. Phorid flies are tiny and occur as the main or even the only insect evidence in relatively enclosed environments. This paper reviews the research progress of carrion-breeding phorid flies for estimating post-mortem interval in forensic medicine which includes their roles, species identification and age determination of immatures. Copyright© by the Editorial Department of Journal of Forensic Medicine.

Resource assessment in Western Australia using a geographic information system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jackson, A.

1991-03-01

Three study areas in Western Australia covering from 77,000 to 425,000 mi{sup 2} were examined for oil and gas potential using a geographic information system (GIS). A data base of source rock thickness, source richness, maturity, and expulsion efficiency was created for each interval. The GIS (Arc/Info) was used to create, manage, and analyze data for each interval in each study area. Source rock thickness and source richness data were added to the data base from digitized data. Maturity information was generated with Arc/Info by combining geochemical and depth to structure data. Expulsion efficiency data was created by a systemmore » level Arc/Info program. After the data base for each interval was built, the GIS was used to analyze the geologic data. The analysis consisted of converting each data layer into a lattice (grid) and using the lattice operation in Arc/Infor (addition, multiplication, division, and subtraction) to combine the data layers. Additional techniques for combining and selecting data were developed using Arc/Info system level programs. The procedure for performing the analyses was written as macros in Arc/Info's macro programming language (AML). The results of the analysis were estimates of oil and gas volumes for each interval. The resultant volumes were produced in tabular form for reports and cartographic form for presentation. The geographic information system provided several clear advantages over traditional methods of resource assessment including simplified management, updating, and editing of geologic data.« less

Technique for simulating peak-flow hydrographs in Maryland

USGS Publications Warehouse

Dillow, Jonathan J.A.

1998-01-01

The efficient design and management of many bridges, culverts, embankments, and flood-protection structures may require the estimation of time-of-inundation and (or) storage of floodwater relating to such structures. These estimates can be made on the basis of information derived from the peak-flow hydrograph. Average peak-flow hydrographs corresponding to a peak discharge of specific recurrence interval can be simulated for drainage basins having drainage areas less than 500 square miles in Maryland, using a direct technique of known accuracy. The technique uses dimensionless hydrographs in conjunction with estimates of basin lagtime and instantaneous peak flow. Ordinary least-squares regression analysis was used to develop an equation for estimating basin lagtime in Maryland. Drainage area, main channel slope, forest cover, and impervious area were determined to be the significant explanatory variables necessary to estimate average basin lagtime at the 95-percent confidence interval. Qualitative variables included in the equation adequately correct for geographic bias across the State. The average standard error of prediction associated with the equation is approximated as plus or minus (+/-) 37.6 percent. Volume correction factors may be applied to the basin lagtime on the basis of a comparison between actual and estimated hydrograph volumes prior to hydrograph simulation. Three dimensionless hydrographs were developed and tested using data collected during 278 significant rainfall-runoff events at 81 stream-gaging stations distributed throughout Maryland and Delaware. The data represent a range of drainage area sizes and basin conditions. The technique was verified by applying it to the simulation of 20 peak-flow events and comparing actual and simulated hydrograph widths at 50 and 75 percent of the observed peak-flow levels. The events chosen are considered extreme in that the average recurrence interval of the selected peak flows is 130 years. The average standard errors of prediction were +/- 61 and +/- 56 percent at the 50 and 75 percent of peak-flow hydrograph widths, respectively.

Time Intervals in Sequence Sampling, Not Data Modifications, Have a Major Impact on Estimates of HIV Escape Rates

PubMed Central

2018-01-01

The ability of human immunodeficiency virus (HIV) to avoid recognition by humoral and cellular immunity (viral escape) is well-documented, but the strength of the immune response needed to cause such a viral escape remains poorly quantified. Several previous studies observed a more rapid escape of HIV from CD8 T cell responses in the acute phase of infection compared to chronic infection. The rate of HIV escape was estimated with the help of simple mathematical models, and results were interpreted to suggest that CD8 T cell responses causing escape in acute HIV infection may be more efficient at killing virus-infected cells than responses that cause escape in chronic infection, or alternatively, that early escapes occur in epitopes mutations in which there is minimal fitness cost to the virus. However, these conclusions were challenged on several grounds, including linkage and interference of multiple escape mutations due to a low population size and because of potential issues associated with modifying the data to estimate escape rates. Here we use a sampling method which does not require data modification to show that previous results on the decline of the viral escape rate with time since infection remain unchanged. However, using this method we also show that estimates of the escape rate are highly sensitive to the time interval between measurements, with longer intervals biasing estimates of the escape rate downwards. Our results thus suggest that data modifications for early and late escapes were not the primary reason for the observed decline in the escape rate with time since infection. However, longer sampling periods for escapes in chronic infection strongly influence estimates of the escape rate. More frequent sampling of viral sequences in chronic infection may improve our understanding of factors influencing the rate of HIV escape from CD8 T cell responses. PMID:29495443

Time Intervals in Sequence Sampling, Not Data Modifications, Have a Major Impact on Estimates of HIV Escape Rates.

PubMed

Ganusov, Vitaly V

2018-02-27

The ability of human immunodeficiency virus (HIV) to avoid recognition by humoral and cellular immunity (viral escape) is well-documented, but the strength of the immune response needed to cause such a viral escape remains poorly quantified. Several previous studies observed a more rapid escape of HIV from CD8 T cell responses in the acute phase of infection compared to chronic infection. The rate of HIV escape was estimated with the help of simple mathematical models, and results were interpreted to suggest that CD8 T cell responses causing escape in acute HIV infection may be more efficient at killing virus-infected cells than responses that cause escape in chronic infection, or alternatively, that early escapes occur in epitopes mutations in which there is minimal fitness cost to the virus. However, these conclusions were challenged on several grounds, including linkage and interference of multiple escape mutations due to a low population size and because of potential issues associated with modifying the data to estimate escape rates. Here we use a sampling method which does not require data modification to show that previous results on the decline of the viral escape rate with time since infection remain unchanged. However, using this method we also show that estimates of the escape rate are highly sensitive to the time interval between measurements, with longer intervals biasing estimates of the escape rate downwards. Our results thus suggest that data modifications for early and late escapes were not the primary reason for the observed decline in the escape rate with time since infection. However, longer sampling periods for escapes in chronic infection strongly influence estimates of the escape rate. More frequent sampling of viral sequences in chronic infection may improve our understanding of factors influencing the rate of HIV escape from CD8 T cell responses.

An Introduction to Confidence Intervals for Both Statistical Estimates and Effect Sizes.

ERIC Educational Resources Information Center

Capraro, Mary Margaret

This paper summarizes methods of estimating confidence intervals, including classical intervals and intervals for effect sizes. The recent American Psychological Association (APA) Task Force on Statistical Inference report suggested that confidence intervals should always be reported, and the fifth edition of the APA "Publication Manual"…

Exact Scheffé-type confidence intervals for output from groundwater flow models: 1. Use of hydrogeologic information

USGS Publications Warehouse

Cooley, Richard L.

1993-01-01

A new method is developed to efficiently compute exact Scheffé-type confidence intervals for output (or other function of parameters) g(β) derived from a groundwater flow model. The method is general in that parameter uncertainty can be specified by any statistical distribution having a log probability density function (log pdf) that can be expanded in a Taylor series. However, for this study parameter uncertainty is specified by a statistical multivariate beta distribution that incorporates hydrogeologic information in the form of the investigator's best estimates of parameters and a grouping of random variables representing possible parameter values so that each group is defined by maximum and minimum bounds and an ordering according to increasing value. The new method forms the confidence intervals from maximum and minimum limits of g(β) on a contour of a linear combination of (1) the quadratic form for the parameters used by Cooley and Vecchia (1987) and (2) the log pdf for the multivariate beta distribution. Three example problems are used to compare characteristics of the confidence intervals for hydraulic head obtained using different weights for the linear combination. Different weights generally produced similar confidence intervals, whereas the method of Cooley and Vecchia (1987) often produced much larger confidence intervals.

Interval Estimation of Seismic Hazard Parameters

NASA Astrophysics Data System (ADS)

Orlecka-Sikora, Beata; Lasocki, Stanislaw

2017-03-01

The paper considers Poisson temporal occurrence of earthquakes and presents a way to integrate uncertainties of the estimates of mean activity rate and magnitude cumulative distribution function in the interval estimation of the most widely used seismic hazard functions, such as the exceedance probability and the mean return period. The proposed algorithm can be used either when the Gutenberg-Richter model of magnitude distribution is accepted or when the nonparametric estimation is in use. When the Gutenberg-Richter model of magnitude distribution is used the interval estimation of its parameters is based on the asymptotic normality of the maximum likelihood estimator. When the nonparametric kernel estimation of magnitude distribution is used, we propose the iterated bias corrected and accelerated method for interval estimation based on the smoothed bootstrap and second-order bootstrap samples. The changes resulted from the integrated approach in the interval estimation of the seismic hazard functions with respect to the approach, which neglects the uncertainty of the mean activity rate estimates have been studied using Monte Carlo simulations and two real dataset examples. The results indicate that the uncertainty of mean activity rate affects significantly the interval estimates of hazard functions only when the product of activity rate and the time period, for which the hazard is estimated, is no more than 5.0. When this product becomes greater than 5.0, the impact of the uncertainty of cumulative distribution function of magnitude dominates the impact of the uncertainty of mean activity rate in the aggregated uncertainty of the hazard functions. Following, the interval estimates with and without inclusion of the uncertainty of mean activity rate converge. The presented algorithm is generic and can be applied also to capture the propagation of uncertainty of estimates, which are parameters of a multiparameter function, onto this function.

Small area variation in diabetes prevalence in Puerto Rico

PubMed Central

Tierney, Edward F.; Burrows, Nilka R.; Barker, Lawrence E.; Beckles, Gloria L.; Boyle, James P.; Cadwell, Betsy L.; Kirtland, Karen A.; Thompson, Theodore J.

2015-01-01

Objective To estimate the 2009 prevalence of diagnosed diabetes in Puerto Rico among adults ≥ 20 years of age in order to gain a better understanding of its geographic distribution so that policymakers can more efficiently target prevention and control programs. Methods A Bayesian multilevel model was fitted to the combined 2008–2010 Behavioral Risk Factor Surveillance System and 2009 United States Census data to estimate diabetes prevalence for each of the 78 municipios (counties) in Puerto Rico. Results The mean unadjusted estimate for all counties was 14.3% (range by county, 9.9%–18.0%). The average width of the confidence intervals was 6.2%. Adjusted and unadjusted estimates differed little. Conclusions These 78 county estimates are higher on average and showed less variability (i.e., had a smaller range) than the previously published estimates of the 2008 diabetes prevalence for all United States counties (mean, 9.9%; range, 3.0%–18.2%). PMID:23939364

Estimation of flood discharges at selected recurrence intervals for streams in New Hampshire.

DOT National Transportation Integrated Search

2008-01-01

This report provides estimates of flood discharges at selected recurrence intervals for streamgages in and adjacent to New Hampshire and equations for estimating flood discharges at recurrence intervals of 2-, 5-, 10-, 25-, 50-, 100-, and 500-years f...

On Some Confidence Intervals for Estimating the Mean of a Skewed Population

ERIC Educational Resources Information Center

Shi, W.; Kibria, B. M. Golam

2007-01-01

A number of methods are available in the literature to measure confidence intervals. Here, confidence intervals for estimating the population mean of a skewed distribution are considered. This note proposes two alternative confidence intervals, namely, Median t and Mad t, which are simple adjustments to the Student's t confidence interval. In…

Confidence Intervals for Proportion Estimates in Complex Samples. Research Report. ETS RR-06-21

ERIC Educational Resources Information Center

Oranje, Andreas

2006-01-01

Confidence intervals are an important tool to indicate uncertainty of estimates and to give an idea of probable values of an estimate if a different sample from the population was drawn or a different sample of measures was used. Standard symmetric confidence intervals for proportion estimates based on a normal approximation can yield bounds…

Intraday return inefficiency and long memory in the volatilities of forex markets and the role of trading volume

NASA Astrophysics Data System (ADS)

Shahzad, Syed Jawad Hussain; Hernandez, Jose Areola; Hanif, Waqas; Kayani, Ghulam Mujtaba

2018-09-01

We investigate the dynamics of efficiency and long memory, and the impact of trading volume on the efficiency of returns and volatilities of four major traded currencies, namely, the EUR, GBP, CHF and JPY. We do so by implementing full sample and rolling window multifractal detrended fluctuation analysis (MF-DFA) and a quantile-on-quantile (QQ) approach. This paper sheds new light by employing high frequency (5-min interval) data spanning from Jan 1, 2007 to Dec 31, 2016. Realized volatilities are estimated using Andersen et al.'s (2001) measure, while the QQ method employed is drawn from Sim and Zhou (2015). We find evidence of higher efficiency levels in the JPY and CHF currency markets. The impact of trading volume on efficiency is only significant for the JPY and CHF currencies. The GBP currency appears to be the least efficient, followed by the EUR. Implications of the results are discussed.

Musical training generalises across modalities and reveals efficient and adaptive mechanisms for reproducing temporal intervals.

PubMed

Aagten-Murphy, David; Cappagli, Giulia; Burr, David

2014-03-01

Expert musicians are able to time their actions accurately and consistently during a musical performance. We investigated how musical expertise influences the ability to reproduce auditory intervals and how this generalises across different techniques and sensory modalities. We first compared various reproduction strategies and interval length, to examine the effects in general and to optimise experimental conditions for testing the effect of music, and found that the effects were robust and consistent across different paradigms. Focussing on a 'ready-set-go' paradigm subjects reproduced time intervals drawn from distributions varying in total length (176, 352 or 704 ms) or in the number of discrete intervals within the total length (3, 5, 11 or 21 discrete intervals). Overall, Musicians performed more veridical than Non-Musicians, and all subjects reproduced auditory-defined intervals more accurately than visually-defined intervals. However, Non-Musicians, particularly with visual stimuli, consistently exhibited a substantial and systematic regression towards the mean interval. When subjects judged intervals from distributions of longer total length they tended to regress more towards the mean, while the ability to discriminate between discrete intervals within the distribution had little influence on subject error. These results are consistent with a Bayesian model that minimizes reproduction errors by incorporating a central tendency prior weighted by the subject's own temporal precision relative to the current distribution of intervals. Finally a strong correlation was observed between all durations of formal musical training and total reproduction errors in both modalities (accounting for 30% of the variance). Taken together these results demonstrate that formal musical training improves temporal reproduction, and that this improvement transfers from audition to vision. They further demonstrate the flexibility of sensorimotor mechanisms in adapting to different task conditions to minimise temporal estimation errors. © 2013.

The measurement of linear frequency drift in oscillators

NASA Astrophysics Data System (ADS)

Barnes, J. A.

1985-04-01

A linear drift in frequency is an important element in most stochastic models of oscillator performance. Quartz crystal oscillators often have drifts in excess of a part in ten to the tenth power per day. Even commercial cesium beam devices often show drifts of a few parts in ten to the thirteenth per year. There are many ways to estimate the drift rates from data samples (e.g., regress the phase on a quadratic; regress the frequency on a linear; compute the simple mean of the first difference of frequency; use Kalman filters with a drift term as one element in the state vector; and others). Although most of these estimators are unbiased, they vary in efficiency (i.e., confidence intervals). Further, the estimation of confidence intervals using the standard analysis of variance (typically associated with the specific estimating technique) can give amazingly optimistic results. The source of these problems is not an error in, say, the regressions techniques, but rather the problems arise from correlations within the residuals. That is, the oscillator model is often not consistent with constraints on the analysis technique or, in other words, some specific analysis techniques are often inappropriate for the task at hand. The appropriateness of a specific analysis technique is critically dependent on the oscillator model and can often be checked with a simple whiteness test on the residuals.

Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty.

PubMed

Svensson, Fredrik; Aniceto, Natalia; Norinder, Ulf; Cortes-Ciriano, Isidro; Spjuth, Ola; Carlsson, Lars; Bender, Andreas

2018-05-29

Making predictions with an associated confidence is highly desirable as it facilitates decision making and resource prioritization. Conformal regression is a machine learning framework that allows the user to define the required confidence and delivers predictions that are guaranteed to be correct to the selected extent. In this study, we apply conformal regression to model molecular properties and bioactivity values and investigate different ways to scale the resultant prediction intervals to create as efficient (i.e., narrow) regressors as possible. Different algorithms to estimate the prediction uncertainty were used to normalize the prediction ranges, and the different approaches were evaluated on 29 publicly available data sets. Our results show that the most efficient conformal regressors are obtained when using the natural exponential of the ensemble standard deviation from the underlying random forest to scale the prediction intervals, but other approaches were almost as efficient. This approach afforded an average prediction range of 1.65 pIC50 units at the 80% confidence level when applied to bioactivity modeling. The choice of nonconformity function has a pronounced impact on the average prediction range with a difference of close to one log unit in bioactivity between the tightest and widest prediction range. Overall, conformal regression is a robust approach to generate bioactivity predictions with associated confidence.

The size of a pilot study for a clinical trial should be calculated in relation to considerations of precision and efficiency.

PubMed

Sim, Julius; Lewis, Martyn

2012-03-01

To investigate methods to determine the size of a pilot study to inform a power calculation for a randomized controlled trial (RCT) using an interval/ratio outcome measure. Calculations based on confidence intervals (CIs) for the sample standard deviation (SD). Based on CIs for the sample SD, methods are demonstrated whereby (1) the observed SD can be adjusted to secure the desired level of statistical power in the main study with a specified level of confidence; (2) the sample for the main study, if calculated using the observed SD, can be adjusted, again to obtain the desired level of statistical power in the main study; (3) the power of the main study can be calculated for the situation in which the SD in the pilot study proves to be an underestimate of the true SD; and (4) an "efficient" pilot size can be determined to minimize the combined size of the pilot and main RCT. Trialists should calculate the appropriate size of a pilot study, just as they should the size of the main RCT, taking into account the twin needs to demonstrate efficiency in terms of recruitment and to produce precise estimates of treatment effect. Copyright © 2012 Elsevier Inc. All rights reserved.

Weighted regression analysis and interval estimators

Treesearch

Donald W. Seegrist

1974-01-01

A method for deriving the weighted least squares estimators for the parameters of a multiple regression model. Confidence intervals for expected values, and prediction intervals for the means of future samples are given.

Sample size planning for composite reliability coefficients: accuracy in parameter estimation via narrow confidence intervals.

PubMed

Terry, Leann; Kelley, Ken

2012-11-01

Composite measures play an important role in psychology and related disciplines. Composite measures almost always have error. Correspondingly, it is important to understand the reliability of the scores from any particular composite measure. However, the point estimates of the reliability of composite measures are fallible and thus all such point estimates should be accompanied by a confidence interval. When confidence intervals are wide, there is much uncertainty in the population value of the reliability coefficient. Given the importance of reporting confidence intervals for estimates of reliability, coupled with the undesirability of wide confidence intervals, we develop methods that allow researchers to plan sample size in order to obtain narrow confidence intervals for population reliability coefficients. We first discuss composite reliability coefficients and then provide a discussion on confidence interval formation for the corresponding population value. Using the accuracy in parameter estimation approach, we develop two methods to obtain accurate estimates of reliability by planning sample size. The first method provides a way to plan sample size so that the expected confidence interval width for the population reliability coefficient is sufficiently narrow. The second method ensures that the confidence interval width will be sufficiently narrow with some desired degree of assurance (e.g., 99% assurance that the 95% confidence interval for the population reliability coefficient will be less than W units wide). The effectiveness of our methods was verified with Monte Carlo simulation studies. We demonstrate how to easily implement the methods with easy-to-use and freely available software. ©2011 The British Psychological Society.

Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics.

PubMed

Vaas, Lea A I; Sikorski, Johannes; Michael, Victoria; Göker, Markus; Klenk, Hans-Peter

2012-01-01

The Phenotype MicroArray (OmniLog® PM) system is able to simultaneously capture a large number of phenotypes by recording an organism's respiration over time on distinct substrates. This technique targets the object of natural selection itself, the phenotype, whereas previously addressed '-omics' techniques merely study components that finally contribute to it. The recording of respiration over time, however, adds a longitudinal dimension to the data. To optimally exploit this information, it must be extracted from the shapes of the recorded curves and displayed in analogy to conventional growth curves. The free software environment R was explored for both visualizing and fitting of PM respiration curves. Approaches using either a model fit (and commonly applied growth models) or a smoothing spline were evaluated. Their reliability in inferring curve parameters and confidence intervals was compared to the native OmniLog® PM analysis software. We consider the post-processing of the estimated parameters, the optimal classification of curve shapes and the detection of significant differences between them, as well as practically relevant questions such as detecting the impact of cultivation times and the minimum required number of experimental repeats. We provide a comprehensive framework for data visualization and parameter estimation according to user choices. A flexible graphical representation strategy for displaying the results is proposed, including 95% confidence intervals for the estimated parameters. The spline approach is less prone to irregular curve shapes than fitting any of the considered models or using the native PM software for calculating both point estimates and confidence intervals. These can serve as a starting point for the automated post-processing of PM data, providing much more information than the strict dichotomization into positive and negative reactions. Our results form the basis for a freely available R package for the analysis of PM data.

Visualization and Curve-Parameter Estimation Strategies for Efficient Exploration of Phenotype Microarray Kinetics

PubMed Central

Vaas, Lea A. I.; Sikorski, Johannes; Michael, Victoria; Göker, Markus; Klenk, Hans-Peter

2012-01-01

Background The Phenotype MicroArray (OmniLog® PM) system is able to simultaneously capture a large number of phenotypes by recording an organism's respiration over time on distinct substrates. This technique targets the object of natural selection itself, the phenotype, whereas previously addressed ‘-omics’ techniques merely study components that finally contribute to it. The recording of respiration over time, however, adds a longitudinal dimension to the data. To optimally exploit this information, it must be extracted from the shapes of the recorded curves and displayed in analogy to conventional growth curves. Methodology The free software environment R was explored for both visualizing and fitting of PM respiration curves. Approaches using either a model fit (and commonly applied growth models) or a smoothing spline were evaluated. Their reliability in inferring curve parameters and confidence intervals was compared to the native OmniLog® PM analysis software. We consider the post-processing of the estimated parameters, the optimal classification of curve shapes and the detection of significant differences between them, as well as practically relevant questions such as detecting the impact of cultivation times and the minimum required number of experimental repeats. Conclusions We provide a comprehensive framework for data visualization and parameter estimation according to user choices. A flexible graphical representation strategy for displaying the results is proposed, including 95% confidence intervals for the estimated parameters. The spline approach is less prone to irregular curve shapes than fitting any of the considered models or using the native PM software for calculating both point estimates and confidence intervals. These can serve as a starting point for the automated post-processing of PM data, providing much more information than the strict dichotomization into positive and negative reactions. Our results form the basis for a freely available R package for the analysis of PM data. PMID:22536335

Composite Partial Likelihood Estimation Under Length-Biased Sampling, With Application to a Prevalent Cohort Study of Dementia

PubMed Central

Huang, Chiung-Yu; Qin, Jing

2013-01-01

The Canadian Study of Health and Aging (CSHA) employed a prevalent cohort design to study survival after onset of dementia, where patients with dementia were sampled and the onset time of dementia was determined retrospectively. The prevalent cohort sampling scheme favors individuals who survive longer. Thus, the observed survival times are subject to length bias. In recent years, there has been a rising interest in developing estimation procedures for prevalent cohort survival data that not only account for length bias but also actually exploit the incidence distribution of the disease to improve efficiency. This article considers semiparametric estimation of the Cox model for the time from dementia onset to death under a stationarity assumption with respect to the disease incidence. Under the stationarity condition, the semiparametric maximum likelihood estimation is expected to be fully efficient yet difficult to perform for statistical practitioners, as the likelihood depends on the baseline hazard function in a complicated way. Moreover, the asymptotic properties of the semiparametric maximum likelihood estimator are not well-studied. Motivated by the composite likelihood method (Besag 1974), we develop a composite partial likelihood method that retains the simplicity of the popular partial likelihood estimator and can be easily performed using standard statistical software. When applied to the CSHA data, the proposed method estimates a significant difference in survival between the vascular dementia group and the possible Alzheimer’s disease group, while the partial likelihood method for left-truncated and right-censored data yields a greater standard error and a 95% confidence interval covering 0, thus highlighting the practical value of employing a more efficient methodology. To check the assumption of stable disease for the CSHA data, we also present new graphical and numerical tests in the article. The R code used to obtain the maximum composite partial likelihood estimator for the CSHA data is available in the online Supplementary Material, posted on the journal web site. PMID:24000265

A Probabilistic Method of Assessing Carbon Accumulation Rate at Imnavait Creek Peatland, Arctic Long Term Ecological Research Station, Alaska

NASA Technical Reports Server (NTRS)

Nichols, Jonathan E.; Peteet, Dorothy M.; Frolking, Steve; Karavias, John

2017-01-01

Arctic peatlands are an important part of the global carbon cycle, accumulating atmospheric carbon as organic matter since the Late glacial. Current methods for understanding the changing efficiency of the peatland carbon sink rely on peatlands with an undisturbed stratigraphy. Here we present a method of estimating primary carbon accumulation rate from a site where permafrost processes have either vertically or horizontally translocated nearby carbon-rich sediment out of stratigraphic order. Briefly, our new algorithm estimates the probability of the age of deposition of a random increment of sediment in the core. The method assumes that if sediment age is measured at even depth increments, dates are more likely to occur during intervals of higher accumulation rate and vice versa. Multiplying estimated sedimentation rate by measured carbon density yields carbon accumulation rate. We perform this analysis at the Imnavait Creek Peatland, near the Arctic Long Term Ecological Research network site at Toolik Lake, Alaska. Using classical radiocarbon age modeling, we find unreasonably high rates of carbon accumulation at various Holocene intervals. With our new method, we find accumulation rate changes that are in improved agreement within the context of other sites throughout Alaska and the rest of the Circum-Arctic region.

Monitoring the effective population size of a brown bear (Ursus arctos) population using new single-sample approaches.

PubMed

Skrbinšek, Tomaž; Jelenčič, Maja; Waits, Lisette; Kos, Ivan; Jerina, Klemen; Trontelj, Peter

2012-02-01

The effective population size (N(e) ) could be the ideal parameter for monitoring populations of conservation concern as it conveniently summarizes both the evolutionary potential of the population and its sensitivity to genetic stochasticity. However, tracing its change through time is difficult in natural populations. We applied four new methods for estimating N(e) from a single sample of genotypes to trace temporal change in N(e) for bears in the Northern Dinaric Mountains. We genotyped 510 bears using 20 microsatellite loci and determined their age. The samples were organized into cohorts with regard to the year when the animals were born and yearly samples with age categories for every year when they were alive. We used the Estimator by Parentage Assignment (EPA) to directly estimate both N(e) and generation interval for each yearly sample. For cohorts, we estimated the effective number of breeders (N(b) ) using linkage disequilibrium, sibship assignment and approximate Bayesian computation methods and extrapolated these estimates to N(e) using the generation interval. The N(e) estimate by EPA is 276 (183-350 95% CI), meeting the inbreeding-avoidance criterion of N(e) > 50 but short of the long-term minimum viable population goal of N(e) > 500. The results obtained by the other methods are highly consistent with this result, and all indicate a rapid increase in N(e) probably in the late 1990s and early 2000s. The new single-sample approaches to the estimation of N(e) provide efficient means for including N(e) in monitoring frameworks and will be of great importance for future management and conservation. © 2012 Blackwell Publishing Ltd.

Maximum likelihood estimation of correction for dilution bias in simple linear regression using replicates from subjects with extreme first measurements.

PubMed

Berglund, Lars; Garmo, Hans; Lindbäck, Johan; Svärdsudd, Kurt; Zethelius, Björn

2008-09-30

The least-squares estimator of the slope in a simple linear regression model is biased towards zero when the predictor is measured with random error. A corrected slope may be estimated by adding data from a reliability study, which comprises a subset of subjects from the main study. The precision of this corrected slope depends on the design of the reliability study and estimator choice. Previous work has assumed that the reliability study constitutes a random sample from the main study. A more efficient design is to use subjects with extreme values on their first measurement. Previously, we published a variance formula for the corrected slope, when the correction factor is the slope in the regression of the second measurement on the first. In this paper we show that both designs improve by maximum likelihood estimation (MLE). The precision gain is explained by the inclusion of data from all subjects for estimation of the predictor's variance and by the use of the second measurement for estimation of the covariance between response and predictor. The gain of MLE enhances with stronger true relationship between response and predictor and with lower precision in the predictor measurements. We present a real data example on the relationship between fasting insulin, a surrogate marker, and true insulin sensitivity measured by a gold-standard euglycaemic insulin clamp, and simulations, where the behavior of profile-likelihood-based confidence intervals is examined. MLE was shown to be a robust estimator for non-normal distributions and efficient for small sample situations. Copyright (c) 2008 John Wiley & Sons, Ltd.

Cross-cultural estimation of the human generation interval for use in genetics-based population divergence studies.

PubMed

Fenner, Jack N

2005-10-01

The length of the human generation interval is a key parameter when using genetics to date population divergence events. However, no consensus exists regarding the generation interval length, and a wide variety of interval lengths have been used in recent studies. This makes comparison between studies difficult, and questions the accuracy of divergence date estimations. Recent genealogy-based research suggests that the male generation interval is substantially longer than the female interval, and that both are greater than the values commonly used in genetics studies. This study evaluates each of these hypotheses in a broader cross-cultural context, using data from both nation states and recent hunter-gatherer societies. Both hypotheses are supported by this study; therefore, revised estimates of male, female, and overall human generation interval lengths are proposed. The nearly universal, cross-cultural nature of the evidence justifies using these proposed estimates in Y-chromosomal, mitochondrial, and autosomal DNA-based population divergence studies.

Age-dependent biochemical quantities: an approach for calculating reference intervals.

PubMed

Bjerner, J

2007-01-01

A parametric method is often preferred when calculating reference intervals for biochemical quantities, as non-parametric methods are less efficient and require more observations/study subjects. Parametric methods are complicated, however, because of three commonly encountered features. First, biochemical quantities seldom display a Gaussian distribution, and there must either be a transformation procedure to obtain such a distribution or a more complex distribution has to be used. Second, biochemical quantities are often dependent on a continuous covariate, exemplified by rising serum concentrations of MUC1 (episialin, CA15.3) with increasing age. Third, outliers often exert substantial influence on parametric estimations and therefore need to be excluded before calculations are made. The International Federation of Clinical Chemistry (IFCC) currently recommends that confidence intervals be calculated for the reference centiles obtained. However, common statistical packages allowing for the adjustment of a continuous covariate do not make this calculation. In the method described in the current study, Tukey's fence is used to eliminate outliers and two-stage transformations (modulus-exponential-normal) in order to render Gaussian distributions. Fractional polynomials are employed to model functions for mean and standard deviations dependent on a covariate, and the model is selected by maximum likelihood. Confidence intervals are calculated for the fitted centiles by combining parameter estimation and sampling uncertainties. Finally, the elimination of outliers was made dependent on covariates by reiteration. Though a good knowledge of statistical theory is needed when performing the analysis, the current method is rewarding because the results are of practical use in patient care.

Assessing Interval Estimation Methods for Hill Model ...

EPA Pesticide Factsheets

The Hill model of concentration-response is ubiquitous in toxicology, perhaps because its parameters directly relate to biologically significant metrics of toxicity such as efficacy and potency. Point estimates of these parameters obtained through least squares regression or maximum likelihood are commonly used in high-throughput risk assessment, but such estimates typically fail to include reliable information concerning confidence in (or precision of) the estimates. To address this issue, we examined methods for assessing uncertainty in Hill model parameter estimates derived from concentration-response data. In particular, using a sample of ToxCast concentration-response data sets, we applied four methods for obtaining interval estimates that are based on asymptotic theory, bootstrapping (two varieties), and Bayesian parameter estimation, and then compared the results. These interval estimation methods generally did not agree, so we devised a simulation study to assess their relative performance. We generated simulated data by constructing four statistical error models capable of producing concentration-response data sets comparable to those observed in ToxCast. We then applied the four interval estimation methods to the simulated data and compared the actual coverage of the interval estimates to the nominal coverage (e.g., 95%) in order to quantify performance of each of the methods in a variety of cases (i.e., different values of the true Hill model paramet

Efficiency of International Classification of Diseases, Ninth Revision, Billing Code Searches to Identify Emergency Department Visits for Blood or Body Fluid Exposures through a Statewide Multicenter Database

PubMed Central

Rosen, Lisa M.; Liu, Tao; Merchant, Roland C.

2016-01-01

BACKGROUND Blood and body fluid exposures are frequently evaluated in emergency departments (EDs). However, efficient and effective methods for estimating their incidence are not yet established. OBJECTIVE Evaluate the efficiency and accuracy of estimating statewide ED visits for blood or body fluid exposures using International Classification of Diseases, Ninth Revision (ICD-9), code searches. DESIGN Secondary analysis of a database of ED visits for blood or body fluid exposure. SETTING EDs of 11 civilian hospitals throughout Rhode Island from January 1, 1995, through June 30, 2001. PATIENTS Patients presenting to the ED for possible blood or body fluid exposure were included, as determined by prespecified ICD-9 codes. METHODS Positive predictive values (PPVs) were estimated to determine the ability of 10 ICD-9 codes to distinguish ED visits for blood or body fluid exposure from ED visits that were not for blood or body fluid exposure. Recursive partitioning was used to identify an optimal subset of ICD-9 codes for this purpose. Random-effects logistic regression modeling was used to examine variations in ICD-9 coding practices and styles across hospitals. Cluster analysis was used to assess whether the choice of ICD-9 codes was similar across hospitals. RESULTS The PPV for the original 10 ICD-9 codes was 74.4% (95% confidence interval [CI], 73.2%–75.7%), whereas the recursive partitioning analysis identified a subset of 5 ICD-9 codes with a PPV of 89.9% (95% CI, 88.9%–90.8%) and a misclassification rate of 10.1%. The ability, efficiency, and use of the ICD-9 codes to distinguish types of ED visits varied across hospitals. CONCLUSIONS Although an accurate subset of ICD-9 codes could be identified, variations across hospitals related to hospital coding style, efficiency, and accuracy greatly affected estimates of the number of ED visits for blood or body fluid exposure. PMID:22561713

Labor costs incurred by anesthesiology groups because of operating rooms not being allocated and cases not being scheduled to maximize operating room efficiency.

PubMed

Abouleish, Amr E; Dexter, Franklin; Epstein, Richard H; Lubarsky, David A; Whitten, Charles W; Prough, Donald S

2003-04-01

Determination of operating room (OR) block allocation and case scheduling is often not based on maximizing OR efficiency, but rather on tradition and surgeon convenience. As a result, anesthesiology groups often incur additional labor costs. When negotiating financial support, heads of anesthesiology departments are often challenged to justify the subsidy necessary to offset these additional labor costs. In this study, we describe a method for calculating a statistically sound estimate of the excess labor costs incurred by an anesthesiology group because of inefficient OR allocation and case scheduling. OR information system and anesthesia staffing data for 1 yr were obtained from two university hospitals. Optimal OR allocation for each surgical service was determined by maximizing the efficiency of use of the OR staff. Hourly costs were converted to dollar amounts by using the nationwide median compensation for academic and private-practice anesthesia providers. Differences between actual costs and the optimal OR allocation were determined. For Hospital A, estimated annual excess labor costs were $1.6 million (95% confidence interval, $1.5-$1.7 million) and $2.0 million ($1.89-$2.05 million) when academic and private-practice compensation, respectively, was calculated. For Hospital B, excess labor costs were $1.0 million ($1.08-$1.17 million) and $1.4 million ($1.32-1.43 million) for academic and private-practice compensation, respectively. This study demonstrates a methodology for an anesthesiology group to estimate its excess labor costs. The group can then use these estimates when negotiating for subsidies with its hospital, medical school, or multispecialty medical group. We describe a new application for a previously reported statistical method to calculate operating room (OR) allocations to maximize OR efficiency. When optimal OR allocations and case scheduling are not implemented, the resulting increase in labor costs can be used in negotiations as a statistically sound estimate for the increased labor cost to the anesthesiology department.

Electronic medical records and efficiency and productivity during office visits.

PubMed

Furukawa, Michael F

2011-04-01

To estimate the relationship between electronic medical record (EMR) use and efficiency of utilization and provider productivity during visits to US office-based physicians. Cross-sectional analysis of the 2006-2007 National Ambulatory Medical Care Survey. The sample included 62,710 patient visits to 2625 physicians. EMR systems included demographics, clinical notes, prescription orders, and laboratory and imaging results. Efficiency was measured as utilization of examinations, laboratory tests, radiology procedures, health education, nonmedication treatments, and medications. Productivity was measured as total services provided per 20-minute period. Survey-weighted regressions estimated association of EMR use with services provided, visit intensity/duration, and productivity. Marginal effects were estimated by averaging across all visits and by major reason for visit. EMR use was associated with higher probability of any examination (7.7%, 95% confidence interval [CI] = 2.4%, 13.1%); any laboratory test (5.7%, 95% CI = 2.6%, 8.8%); any health education (4.9%, 95% CI = 0.2%, 9.6%); and fewer laboratory tests (-7.1%, 95% CI = -14.2%, -0.1%). During pre/post surgery visits, EMR use was associated with 7.3% (95% CI= -12.9%, -1.8%) fewer radiology procedures. EMR use was not associated with utilization of nonmedication treatments and medications, or visit duration. During routine visits for a chronic problem, EMR use was associated with 11.2% (95% CI = 5.7%, 16.8%) more diagnostic/screening services provided per 20-minute period. EMR use had a mixed association with efficiency and productivity during office visits. EMRs may improve provider productivity, especially during visits for a new problem and routine chronic care.

Reference interval estimation: Methodological comparison using extensive simulations and empirical data.

PubMed

Daly, Caitlin H; Higgins, Victoria; Adeli, Khosrow; Grey, Vijay L; Hamid, Jemila S

2017-12-01

To statistically compare and evaluate commonly used methods of estimating reference intervals and to determine which method is best based on characteristics of the distribution of various data sets. Three approaches for estimating reference intervals, i.e. parametric, non-parametric, and robust, were compared with simulated Gaussian and non-Gaussian data. The hierarchy of the performances of each method was examined based on bias and measures of precision. The findings of the simulation study were illustrated through real data sets. In all Gaussian scenarios, the parametric approach provided the least biased and most precise estimates. In non-Gaussian scenarios, no single method provided the least biased and most precise estimates for both limits of a reference interval across all sample sizes, although the non-parametric approach performed the best for most scenarios. The hierarchy of the performances of the three methods was only impacted by sample size and skewness. Differences between reference interval estimates established by the three methods were inflated by variability. Whenever possible, laboratories should attempt to transform data to a Gaussian distribution and use the parametric approach to obtain the most optimal reference intervals. When this is not possible, laboratories should consider sample size and skewness as factors in their choice of reference interval estimation method. The consequences of false positives or false negatives may also serve as factors in this decision. Copyright © 2017 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

New Multi-objective Uncertainty-based Algorithm for Water Resource Models' Calibration

NASA Astrophysics Data System (ADS)

Keshavarz, Kasra; Alizadeh, Hossein

2017-04-01

Water resource models are powerful tools to support water management decision making process and are developed to deal with a broad range of issues including land use and climate change impacts analysis, water allocation, systems design and operation, waste load control and allocation, etc. These models are divided into two categories of simulation and optimization models whose calibration has been addressed in the literature where great relevant efforts in recent decades have led to two main categories of auto-calibration methods of uncertainty-based algorithms such as GLUE, MCMC and PEST and optimization-based algorithms including single-objective optimization such as SCE-UA and multi-objective optimization such as MOCOM-UA and MOSCEM-UA. Although algorithms which benefit from capabilities of both types, such as SUFI-2, were rather developed, this paper proposes a new auto-calibration algorithm which is capable of both finding optimal parameters values regarding multiple objectives like optimization-based algorithms and providing interval estimations of parameters like uncertainty-based algorithms. The algorithm is actually developed to improve quality of SUFI-2 results. Based on a single-objective, e.g. NSE and RMSE, SUFI-2 proposes a routine to find the best point and interval estimation of parameters and corresponding prediction intervals (95 PPU) of time series of interest. To assess the goodness of calibration, final results are presented using two uncertainty measures of p-factor quantifying percentage of observations covered by 95PPU and r-factor quantifying degree of uncertainty, and the analyst has to select the point and interval estimation of parameters which are actually non-dominated regarding both of the uncertainty measures. Based on the described properties of SUFI-2, two important questions are raised, answering of which are our research motivation: Given that in SUFI-2, final selection is based on the two measures or objectives and on the other hand, knowing that there is no multi-objective optimization mechanism in SUFI-2, are the final estimations Pareto-optimal? Can systematic methods be applied to select the final estimations? Dealing with these questions, a new auto-calibration algorithm was proposed where the uncertainty measures were considered as two objectives to find non-dominated interval estimations of parameters by means of coupling Monte Carlo simulation and Multi-Objective Particle Swarm Optimization. Both the proposed algorithm and SUFI-2 were applied to calibrate parameters of water resources planning model of Helleh river basin, Iran. The model is a comprehensive water quantity-quality model developed in the previous researches using WEAP software in order to analyze the impacts of different water resources management strategies including dam construction, increasing cultivation area, utilization of more efficient irrigation technologies, changing crop pattern, etc. Comparing the Pareto frontier resulted from the proposed auto-calibration algorithm with SUFI-2 results, it was revealed that the new algorithm leads to a better and also continuous Pareto frontier, even though it is more computationally expensive. Finally, Nash and Kalai-Smorodinsky bargaining methods were used to choose compromised interval estimation regarding Pareto frontier.

Machine learning approaches for estimation of prediction interval for the model output.

PubMed

Shrestha, Durga L; Solomatine, Dimitri P

2006-03-01

A novel method for estimating prediction uncertainty using machine learning techniques is presented. Uncertainty is expressed in the form of the two quantiles (constituting the prediction interval) of the underlying distribution of prediction errors. The idea is to partition the input space into different zones or clusters having similar model errors using fuzzy c-means clustering. The prediction interval is constructed for each cluster on the basis of empirical distributions of the errors associated with all instances belonging to the cluster under consideration and propagated from each cluster to the examples according to their membership grades in each cluster. Then a regression model is built for in-sample data using computed prediction limits as targets, and finally, this model is applied to estimate the prediction intervals (limits) for out-of-sample data. The method was tested on artificial and real hydrologic data sets using various machine learning techniques. Preliminary results show that the method is superior to other methods estimating the prediction interval. A new method for evaluating performance for estimating prediction interval is proposed as well.

Estimation of postmortem interval through albumin in CSF by simple dye binding method.

PubMed

Parmar, Ankita K; Menon, Shobhana K

2015-12-01

Estimation of postmortem interval is a very important question in some medicolegal investigations. For the precise estimation of postmortem interval, there is a need of a method which can give accurate estimation. Bromocresol green (BCG) is a simple dye binding method and widely used in routine practice. Application of this method in forensic practice may bring revolutionary changes. In this study, cerebrospinal fluid was aspirated from cisternal puncture from 100 autopsies. A study was carried out on concentration of albumin with respect to postmortem interval. After death, albumin present in CSF undergoes changes, after 72 h of death, concentration of albumin has become 0.012 mM, and this decrease was linear from 2 h to 72 h. An important relationship was found between albumin concentration and postmortem interval with an error of ± 1-4h. The study concludes that CSF albumin can be a useful and significant parameter in estimation of postmortem interval. Copyright © 2015 The Chartered Society of Forensic Sciences. Published by Elsevier Ireland Ltd. All rights reserved.

SINGLE-INTERVAL GAS PERMEABILITY ESTIMATION

EPA Science Inventory

Single-interval, steady-steady-state gas permeability testing requires estimation of pressure at a screened interval which in turn requires measurement of friction factors as a function of mass flow rate. Friction factors can be obtained by injecting air through a length of pipe...

Efficient Methods of Estimating the Operating Characteristics of Item Response Categories and Challenge to a New Model for the Multiple-Choice Item

DTIC Science & Technology

1981-11-01

i very little effort has been put upon the model validation, which is essential in any scientific research. T’-- -rientation we aim at in the present...better than the former to the target function. This implies that, although the interval of ability e of our interest is even a little smaller than [-3.0...approaches turned out to be similar, with some deviations, i.e., some of them are a little closer to the theoretical density function, and some of

Robust functional regression model for marginal mean and subject-specific inferences.

PubMed

Cao, Chunzheng; Shi, Jian Qing; Lee, Youngjo

2017-01-01

We introduce flexible robust functional regression models, using various heavy-tailed processes, including a Student t-process. We propose efficient algorithms in estimating parameters for the marginal mean inferences and in predicting conditional means as well as interpolation and extrapolation for the subject-specific inferences. We develop bootstrap prediction intervals (PIs) for conditional mean curves. Numerical studies show that the proposed model provides a robust approach against data contamination or distribution misspecification, and the proposed PIs maintain the nominal confidence levels. A real data application is presented as an illustrative example.

Racetrack resonator as a loss measurement platform for photonic components.

PubMed

Jones, Adam M; DeRose, Christopher T; Lentine, Anthony L; Starbuck, Andrew; Pomerene, Andrew T S; Norwood, Robert A

2015-11-02

This work represents the first complete analysis of the use of a racetrack resonator to measure the insertion loss of efficient, compact photonic components. Beginning with an in-depth analysis of potential error sources and a discussion of the calibration procedure, the technique is used to estimate the insertion loss of waveguide width tapers of varying geometry with a resulting 95% confidence interval of 0.007 dB. The work concludes with a performance comparison of the analyzed tapers with results presented for four taper profiles and three taper lengths.

Racetrack resonator as a loss measurement platform for photonic components

DOE PAGES

Jones, Adam M.; Univ. of Arizona, Tucson, AZ; DeRose, Christopher T.; ...

2015-10-27

This work represents the first complete analysis of the use of a racetrack resonator to measure the insertion loss of efficient, compact photonic components. Beginning with an in-depth analysis of potential error sources and a discussion of the calibration procedure, the technique is used to estimate the insertion loss of waveguide width tapers of varying geometry with a resulting 95% confidence interval of 0.007 dB. Furthermore, the work concludes with a performance comparison of the analyzed tapers with results presented for four taper profiles and three taper lengths.

Impact of reduced marker set estimation of genomic relationship matrices on genomic selection for feed efficiency in Angus cattle.

PubMed

Rolf, Megan M; Taylor, Jeremy F; Schnabel, Robert D; McKay, Stephanie D; McClure, Matthew C; Northcutt, Sally L; Kerley, Monty S; Weaber, Robert L

2010-04-19

Molecular estimates of breeding value are expected to increase selection response due to improvements in the accuracy of selection and a reduction in generation interval, particularly for traits that are difficult or expensive to record or are measured late in life. Several statistical methods for incorporating molecular data into breeding value estimation have been proposed, however, most studies have utilized simulated data in which the generated linkage disequilibrium may not represent the targeted livestock population. A genomic relationship matrix was developed for 698 Angus steers and 1,707 Angus sires using 41,028 single nucleotide polymorphisms and breeding values were estimated using feed efficiency phenotypes (average daily feed intake, residual feed intake, and average daily gain) recorded on the steers. The number of SNPs needed to accurately estimate a genomic relationship matrix was evaluated in this population. Results were compared to estimates produced from pedigree-based mixed model analysis of 862 Angus steers with 34,864 identified paternal relatives but no female ancestors. Estimates of additive genetic variance and breeding value accuracies were similar for AFI and RFI using the numerator and genomic relationship matrices despite fewer animals in the genomic analysis. Bootstrap analyses indicated that 2,500-10,000 markers are required for robust estimation of genomic relationship matrices in cattle. This research shows that breeding values and their accuracies may be estimated for commercially important sires for traits recorded in experimental populations without the need for pedigree data to establish identity by descent between members of the commercial and experimental populations when at least 2,500 SNPs are available for the generation of a genomic relationship matrix.

Estimation of postmortem interval based on colony development time for Anoplolepsis longipes (Hymenoptera: Formicidae).

PubMed

Goff, M L; Win, B H

1997-11-01

The postmortem interval for a set of human remains discovered inside a metal tool box was estimated using the development time required for a stratiomyid fly (Diptera: Stratiomyidae), Hermetia illucens, in combination with the time required to establish a colony of the ant Anoplolepsis longipes (Hymenoptera: Formicidae) capable of producing alate (winged) reproductives. This analysis resulted in a postmortem interval estimate of 14 + months, with a period of 14-18 months being the most probable time interval. The victim had been missing for approximately 18 months.

A novel technique for fetal heart rate estimation from Doppler ultrasound signal

PubMed Central

2011-01-01

Background The currently used fetal monitoring instrumentation that is based on Doppler ultrasound technique provides the fetal heart rate (FHR) signal with limited accuracy. It is particularly noticeable as significant decrease of clinically important feature - the variability of FHR signal. The aim of our work was to develop a novel efficient technique for processing of the ultrasound signal, which could estimate the cardiac cycle duration with accuracy comparable to a direct electrocardiography. Methods We have proposed a new technique which provides the true beat-to-beat values of the FHR signal through multiple measurement of a given cardiac cycle in the ultrasound signal. The method consists in three steps: the dynamic adjustment of autocorrelation window, the adaptive autocorrelation peak detection and determination of beat-to-beat intervals. The estimated fetal heart rate values and calculated indices describing variability of FHR, were compared to the reference data obtained from the direct fetal electrocardiogram, as well as to another method for FHR estimation. Results The results revealed that our method increases the accuracy in comparison to currently used fetal monitoring instrumentation, and thus enables to calculate reliable parameters describing the variability of FHR. Relating these results to the other method for FHR estimation we showed that in our approach a much lower number of measured cardiac cycles was rejected as being invalid. Conclusions The proposed method for fetal heart rate determination on a beat-to-beat basis offers a high accuracy of the heart interval measurement enabling reliable quantitative assessment of the FHR variability, at the same time reducing the number of invalid cardiac cycle measurements. PMID:21999764

An evaluation of inferential procedures for adaptive clinical trial designs with pre-specified rules for modifying the sample size.

PubMed

Levin, Gregory P; Emerson, Sarah C; Emerson, Scott S

2014-09-01

Many papers have introduced adaptive clinical trial methods that allow modifications to the sample size based on interim estimates of treatment effect. There has been extensive commentary on type I error control and efficiency considerations, but little research on estimation after an adaptive hypothesis test. We evaluate the reliability and precision of different inferential procedures in the presence of an adaptive design with pre-specified rules for modifying the sampling plan. We extend group sequential orderings of the outcome space based on the stage at stopping, likelihood ratio statistic, and sample mean to the adaptive setting in order to compute median-unbiased point estimates, exact confidence intervals, and P-values uniformly distributed under the null hypothesis. The likelihood ratio ordering is found to average shorter confidence intervals and produce higher probabilities of P-values below important thresholds than alternative approaches. The bias adjusted mean demonstrates the lowest mean squared error among candidate point estimates. A conditional error-based approach in the literature has the benefit of being the only method that accommodates unplanned adaptations. We compare the performance of this and other methods in order to quantify the cost of failing to plan ahead in settings where adaptations could realistically be pre-specified at the design stage. We find the cost to be meaningful for all designs and treatment effects considered, and to be substantial for designs frequently proposed in the literature. © 2014, The International Biometric Society.

Time estimation by patients with frontal lesions and by Korsakoff amnesics.

PubMed

Mimura, M; Kinsbourne, M; O'Connor, M

2000-07-01

We studied time estimation in patients with frontal damage (F) and alcoholic Korsakoff (K) patients in order to differentiate between the contributions of working memory and episodic memory to temporal cognition. In Experiment 1, F and K patients estimated time intervals between 10 and 120 s less accurately than matched normal and alcoholic control subjects. F patients were less accurate than K patients at short (< 1 min) time intervals whereas K patients increasingly underestimated durations as intervals grew longer. F patients overestimated short intervals in inverse proportion to their performance on the Wisconsin Card Sorting Test. As intervals grew longer, overestimation yielded to underestimation for F patients. Experiment 2 involved time estimation while counting at a subjective 1/s rate. F patients' subjective tempo, though relatively rapid, did not fully explain their overestimation of short intervals. In Experiment 3, participants produced predetermined time intervals by depressing a mouse key. K patients underproduced longer intervals. F patients produced comparably to normal participants, but were extremely variable. Findings suggest that both working memory and episodic memory play an individual role in temporal cognition. Turnover within a short-term working memory buffer provides a metric for temporal decisions. The depleted working memory that typically attends frontal dysfunction may result in quicker turnover, and this may inflate subjective duration. On the other hand, temporal estimation beyond 30 s requires episodic remembering, and this puts K patients at a disadvantage.

Evaluation of the Criollo breed Romosinuano as purebred and crossbred cows with Brahman and Angus in Florida. II. Maternal influence on calf traits, cow weight, and measures of maternal efficiency.

PubMed

Riley, D G; Chase, C C; Coleman, S W; Olson, T A

2014-05-01

The objectives of this work were to compare the Criollo breed Romosinuano as straightbred and crossbred cows with Angus and Brahman in subtropical Florida and to estimate heterosis for size traits of their calves, their own weight, and maternal efficiency traits. Cows (n = 404) were born from 2002 to 2005. After their first exposure to bulls as young cows, crossbred cows were bred to bulls of the third breed, and straightbred cows were bred in to bulls of the other 2 breeds. Calves were spring-born from 2005 through 2011. Evaluated calf (n = 1,254) traits included birth weight and weight, ADG, BCS, and hip height at weaning. Cow weight (n = 1,389) was recorded at weaning. Maternal efficiency traits evaluated included weaning weight per 100 kg cow weight, weaning weight per calving interval, and weaning weight per cow exposed to breeding (n = 1,442). Fixed effects and their interactions were investigated included sire and dam breed of cow, sire breed of calf, cow age, year, calf gender, and weaning age as a linear covariate (calf traits at weaning). Direct and maternal additive genetic effects were random in models for calf traits; only direct additive effects were modeled for cow traits. Cows sired by Angus bulls from outside the research herd had calves that were heavier at birth and weaning and greater ADG, BCS, and hip height (P < 0.05). Estimates of heterosis for weaning weight, BCS, and ADG ranged from 1.3 to 13.2% for all pairs of breeds (P < 0.05). Estimates of heterosis for birth weight (3.2 to 8.2%) and hip height (2.3%) were significant for Romosinuano-Angus and Brahman-Angus. Heterosis for cow weight was 65 ± 8 kg for Brahman-Angus (P < 0.001), and estimates for other pairs of breeds were approximately one-half that value. Heterosis for weaning weight/100 kg cow weight was 3.4 ± 0.75 kg for Romosinuano-Angus. Heterosis estimates for weaning weight/calving interval (P < 0.001) ranged from 0.08 ± 0.01 to 0.12 ± 0.01. Heterosis for weaning weight/cow exposed were 31.6 7.7, 36.9 ± 7.4, and 59.1 ± 7.5 kg for Romosinuano-Angus, Romosinuano-Brahman, and Brahman-Angus, respectively (P < 0.001). Most aspects of Romosinuano crossbred maternal performance were acceptable; maternal performance of Brahman-Angus cows excelled.

Obtaining appropriate interval estimates for age when multiple indicators are used: evaluation of an ad-hoc procedure.

PubMed

Fieuws, Steffen; Willems, Guy; Larsen-Tangmose, Sara; Lynnerup, Niels; Boldsen, Jesper; Thevissen, Patrick

2016-03-01

When an estimate of age is needed, typically multiple indicators are present as found in skeletal or dental information. There exists a vast literature on approaches to estimate age from such multivariate data. Application of Bayes' rule has been proposed to overcome drawbacks of classical regression models but becomes less trivial as soon as the number of indicators increases. Each of the age indicators can lead to a different point estimate ("the most plausible value for age") and a prediction interval ("the range of possible values"). The major challenge in the combination of multiple indicators is not the calculation of a combined point estimate for age but the construction of an appropriate prediction interval. Ignoring the correlation between the age indicators results in intervals being too small. Boldsen et al. (2002) presented an ad-hoc procedure to construct an approximate confidence interval without the need to model the multivariate correlation structure between the indicators. The aim of the present paper is to bring under attention this pragmatic approach and to evaluate its performance in a practical setting. This is all the more needed since recent publications ignore the need for interval estimation. To illustrate and evaluate the method, Köhler et al. (1995) third molar scores are used to estimate the age in a dataset of 3200 male subjects in the juvenile age range.

Estimating equivalence with quantile regression

USGS Publications Warehouse

Cade, B.S.

2011-01-01

Equivalence testing and corresponding confidence interval estimates are used to provide more enlightened statistical statements about parameter estimates by relating them to intervals of effect sizes deemed to be of scientific or practical importance rather than just to an effect size of zero. Equivalence tests and confidence interval estimates are based on a null hypothesis that a parameter estimate is either outside (inequivalence hypothesis) or inside (equivalence hypothesis) an equivalence region, depending on the question of interest and assignment of risk. The former approach, often referred to as bioequivalence testing, is often used in regulatory settings because it reverses the burden of proof compared to a standard test of significance, following a precautionary principle for environmental protection. Unfortunately, many applications of equivalence testing focus on establishing average equivalence by estimating differences in means of distributions that do not have homogeneous variances. I discuss how to compare equivalence across quantiles of distributions using confidence intervals on quantile regression estimates that detect differences in heterogeneous distributions missed by focusing on means. I used one-tailed confidence intervals based on inequivalence hypotheses in a two-group treatment-control design for estimating bioequivalence of arsenic concentrations in soils at an old ammunition testing site and bioequivalence of vegetation biomass at a reclaimed mining site. Two-tailed confidence intervals based both on inequivalence and equivalence hypotheses were used to examine quantile equivalence for negligible trends over time for a continuous exponential model of amphibian abundance. ?? 2011 by the Ecological Society of America.

Fast function-on-scalar regression with penalized basis expansions.

PubMed

Reiss, Philip T; Huang, Lei; Mennes, Maarten

2010-01-01

Regression models for functional responses and scalar predictors are often fitted by means of basis functions, with quadratic roughness penalties applied to avoid overfitting. The fitting approach described by Ramsay and Silverman in the 1990 s amounts to a penalized ordinary least squares (P-OLS) estimator of the coefficient functions. We recast this estimator as a generalized ridge regression estimator, and present a penalized generalized least squares (P-GLS) alternative. We describe algorithms by which both estimators can be implemented, with automatic selection of optimal smoothing parameters, in a more computationally efficient manner than has heretofore been available. We discuss pointwise confidence intervals for the coefficient functions, simultaneous inference by permutation tests, and model selection, including a novel notion of pointwise model selection. P-OLS and P-GLS are compared in a simulation study. Our methods are illustrated with an analysis of age effects in a functional magnetic resonance imaging data set, as well as a reanalysis of a now-classic Canadian weather data set. An R package implementing the methods is publicly available.

A method for paraplegic upper-body posture estimation during standing: a pilot study for rehabilitation purposes.

PubMed

Pages, Gaël; Ramdani, Nacim; Fraisse, Philippe; Guiraud, David

2009-06-01

This paper presents a contribution for restoring standing in paraplegia while using functional electrical stimulation (FES). Movement generation induced by FES remains mostly open looped and stimulus intensities are tuned empirically. To design an efficient closed-loop control, a preliminary study has been carried out to investigate the relationship between body posture and voluntary upper body movements. A methodology is proposed to estimate body posture in the sagittal plane using force measurements exerted on supporting handles during standing. This is done by setting up constraints related to the geometric equations of a two-dimensional closed chain model and the hand-handle interactions. All measured quantities are subject to an uncertainty assumed unknown but bounded. The set membership estimation problem is solved via interval analysis. Guaranteed uncertainty bounds are computed for the estimated postures. In order to test the feasibility of our methodology, experiments were carried out with complete spinal cord injured patients.

Estimating fluvial wood discharge from timelapse photography with varying sampling intervals

NASA Astrophysics Data System (ADS)

Anderson, N. K.

2013-12-01

There is recent focus on calculating wood budgets for streams and rivers to help inform management decisions, ecological studies and carbon/nutrient cycling models. Most work has measured in situ wood in temporary storage along stream banks or estimated wood inputs from banks. Little effort has been employed monitoring and quantifying wood in transport during high flows. This paper outlines a procedure for estimating total seasonal wood loads using non-continuous coarse interval sampling and examines differences in estimation between sampling at 1, 5, 10 and 15 minutes. Analysis is performed on wood transport for the Slave River in Northwest Territories, Canada. Relative to the 1 minute dataset, precision decreased by 23%, 46% and 60% for the 5, 10 and 15 minute datasets, respectively. Five and 10 minute sampling intervals provided unbiased equal variance estimates of 1 minute sampling, whereas 15 minute intervals were biased towards underestimation by 6%. Stratifying estimates by day and by discharge increased precision over non-stratification by 4% and 3%, respectively. Not including wood transported during ice break-up, the total minimum wood load estimated at this site is 3300 × 800$ m3 for the 2012 runoff season. The vast majority of the imprecision in total wood volumes came from variance in estimating average volume per log. Comparison of proportions and variance across sample intervals using bootstrap sampling to achieve equal n. Each trial was sampled for n=100, 10,000 times and averaged. All trials were then averaged to obtain an estimate for each sample interval. Dashed lines represent values from the one minute dataset.

Androgen profiling by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in healthy normal-weight ovulatory and anovulatory late adolescent and young women.

PubMed

Fanelli, Flaminia; Gambineri, Alessandra; Belluomo, Ilaria; Repaci, Andrea; Di Lallo, Valentina Diana; Di Dalmazi, Guido; Mezzullo, Marco; Prontera, Olga; Cuomo, Gaia; Zanotti, Laura; Paccapelo, Alexandro; Morselli-Labate, Antonio Maria; Pagotto, Uberto; Pasquali, Renato

2013-07-01

Physiological transient imbalance typical of adolescence needs to be distinguished from hyperandrogenism-related dysfunction. The accurate determination of circulating androgens is the best indicator of hyperandrogenism. However, reliable reference intervals for adolescent and young women are not available. The aim of the study was to define androgen reference intervals in young women and to analyze the impact of the menstrual phase and ovulation efficiency over the androgen profile as assessed by reliable liquid chromatography-tandem mass spectrometry (LC-MS/MS) technique. Female high school students aged 16-19 years were included in the study. The study was performed on reference subjects properly selected among an unbiased population. Normal-weight, drug and disease free, eumenorrheic females with no signs of hyperandrogenism were included. The steroid hormone profile was determined by a validated in-house LC-MS/MS method. A statistical estimation of overall and menstrual phase-specific reference intervals was performed. A subgroup of anovulatory females was identified based on progesterone circulating levels. The impact of ovulation efficiency over hormonal profile was analyzed. A total of 159 females satisfied healthy criteria. Androgen levels did not vary according to menstrual phase, but a significantly higher upper reference limit was found for T in the luteal phase compared to the follicular phase. Higher T and androstenedione levels were observed in anovulatory compared to ovulatory females, paralleled by higher LH and FSH and lower 17-hydroxyprogesterone and 17β-estradiol levels. This is the first study providing LC-MS/MS-based, menstrual phase-specific reference intervals for the circulating androgen profile in young females. We identified a subgroup of anovulatory healthy females characterized by androgen imbalance.

Genetic co-variance functions for live weight, feed intake, and efficiency measures in growing pigs.

PubMed

Coyne, J M; Berry, D P; Matilainen, K; Sevon-Aimonen, M-L; Mantysaari, E A; Juga, J; Serenius, T; McHugh, N

2017-09-01

The objective of the present study was to estimate genetic co-variance parameters pertaining to live weight, feed intake, and 2 efficiency traits (i.e., residual feed intake and residual daily gain) in a population of pigs over a defined growing phase using Legendre polynomial equations. The data set used consisted of 51,893 live weight records and 903,436 feed intake, residual feed intake (defined as the difference between an animal's actual feed intake and its expected feed intake), and residual daily gain (defined as the difference between an animal's actual growth rate and its expected growth rate) records from 10,201 growing pigs. Genetic co-variance parameters for all traits were estimated using random regression Legendre polynomials. Daily heritability estimates for live weight ranged from 0.25 ± 0.04 (d 73) to 0.50 ± 0.03 (d 122). Low to moderate heritability estimates were evident for feed intake, ranging from 0.07 ± 0.03 (d 66) to 0.25 ± 0.02 (d 170). The estimated heritability for residual feed intake was generally lower than those of both live weight and feed intake and ranged from 0.04 ± 0.01 (d 96) to 0.17 ± 0.02 (d 159). The heritability for feed intake and residual feed intake increased in the early stages of the test period and subsequently sharply declined, coinciding with older ages. Heritability estimates for residual daily gain ranged from 0.26 ± 0.03 (d 188) to 0.42 ± 0.03 (d 101). Genetic correlations within trait were strongest between adjacent ages but weakened as the interval between ages increased; however, the genetic correlations within all traits tended to strengthen between the extremes of the trajectory. Moderate to strong genetic correlations were evident among live weight, feed intake, and the efficiency traits, particularly in the early stage of the trial period (d 66 to 86), but weakened with age. Results from this study could be implemented into the national genetic evaluation for pigs, providing comprehensive information on the profile of growth and efficiency throughout the growing period of the animal's life, thus helping producers identify genetically superior animals.

Precision Interval Estimation of the Response Surface by Means of an Integrated Algorithm of Neural Network and Linear Regression

NASA Technical Reports Server (NTRS)

Lo, Ching F.

1999-01-01

The integration of Radial Basis Function Networks and Back Propagation Neural Networks with the Multiple Linear Regression has been accomplished to map nonlinear response surfaces over a wide range of independent variables in the process of the Modem Design of Experiments. The integrated method is capable to estimate the precision intervals including confidence and predicted intervals. The power of the innovative method has been demonstrated by applying to a set of wind tunnel test data in construction of response surface and estimation of precision interval.

Potential confounding in the association between short birth intervals and increased neonatal, infant, and child mortality

PubMed Central

Perin, Jamie; Walker, Neff

2015-01-01

Background Recent steep declines in child mortality have been attributed in part to increased use of contraceptives and the resulting change in fertility behaviour, including an increase in the time between births. Previous observational studies have documented strong associations between short birth spacing and an increase in the risk of neonatal, infant, and under-five mortality, compared to births with longer preceding birth intervals. In this analysis, we compare two methods to estimate the association between short birth intervals and mortality risk to better inform modelling efforts linking family planning and mortality in children. Objectives Our goal was to estimate the mortality risk for neonates, infants, and young children by preceding birth space using household survey data, controlling for mother-level factors and to compare the results to those from previous analyses with survey data. Design We assessed the potential for confounding when estimating the relative mortality risk by preceding birth interval and estimated mortality risk by birth interval in four categories: less than 18 months, 18–23 months, 24–35 months, and 36 months or longer. We estimated the relative risks among women who were 35 and older at the time of the survey with two methods: in a Cox proportional hazards regression adjusting for potential confounders and also by stratifying Cox regression by mother, to control for all factors that remain constant over a woman's childbearing years. We estimated the overall effects for birth spacing in a meta-analysis with random survey effects. Results We identified several factors known for their associations with neonatal, infant, and child mortality that are also associated with preceding birth interval. When estimating the effect of birth spacing on mortality, we found that regression adjustment for these factors does not substantially change the risk ratio for short birth intervals compared to an unadjusted mortality ratio. For birth intervals less than 18 months, standard regression adjustment for confounding factors estimated a risk ratio for neonatal mortality of 2.28 (95% confidence interval: 2.18–2.37). This same effect estimated within mother is 1.57 (95% confidence interval: 1.52–1.63), a decline of almost one-third in the effect on neonatal mortality. Conclusions Neonatal, infant, and child mortality are strongly and significantly related to preceding birth interval, where births within a short interval of time after the previous birth have increased mortality. Previous analyses have demonstrated this relationship on average across all births; however, women who have short spaces between births are different from women with long spaces. Among women 35 years and older where a comparison of birth spaces within mother is possible, we find a much reduced although still significant effect of short birth spaces on child mortality. PMID:26562139

Potential confounding in the association between short birth intervals and increased neonatal, infant, and child mortality.

PubMed

Perin, Jamie; Walker, Neff

2015-01-01

Recent steep declines in child mortality have been attributed in part to increased use of contraceptives and the resulting change in fertility behaviour, including an increase in the time between births. Previous observational studies have documented strong associations between short birth spacing and an increase in the risk of neonatal, infant, and under-five mortality, compared to births with longer preceding birth intervals. In this analysis, we compare two methods to estimate the association between short birth intervals and mortality risk to better inform modelling efforts linking family planning and mortality in children. Our goal was to estimate the mortality risk for neonates, infants, and young children by preceding birth space using household survey data, controlling for mother-level factors and to compare the results to those from previous analyses with survey data. We assessed the potential for confounding when estimating the relative mortality risk by preceding birth interval and estimated mortality risk by birth interval in four categories: less than 18 months, 18-23 months, 24-35 months, and 36 months or longer. We estimated the relative risks among women who were 35 and older at the time of the survey with two methods: in a Cox proportional hazards regression adjusting for potential confounders and also by stratifying Cox regression by mother, to control for all factors that remain constant over a woman's childbearing years. We estimated the overall effects for birth spacing in a meta-analysis with random survey effects. We identified several factors known for their associations with neonatal, infant, and child mortality that are also associated with preceding birth interval. When estimating the effect of birth spacing on mortality, we found that regression adjustment for these factors does not substantially change the risk ratio for short birth intervals compared to an unadjusted mortality ratio. For birth intervals less than 18 months, standard regression adjustment for confounding factors estimated a risk ratio for neonatal mortality of 2.28 (95% confidence interval: 2.18-2.37). This same effect estimated within mother is 1.57 (95% confidence interval: 1.52-1.63), a decline of almost one-third in the effect on neonatal mortality. Neonatal, infant, and child mortality are strongly and significantly related to preceding birth interval, where births within a short interval of time after the previous birth have increased mortality. Previous analyses have demonstrated this relationship on average across all births; however, women who have short spaces between births are different from women with long spaces. Among women 35 years and older where a comparison of birth spaces within mother is possible, we find a much reduced although still significant effect of short birth spaces on child mortality.

The exposure-crossover design is a new method for studying sustained changes in recurrent events.

PubMed

Redelmeier, Donald A

2013-09-01

To introduce a new design that explores how an acute exposure might lead to a sustained change in the risk of a recurrent outcome. The exposure-crossover design uses self-matching to control within-person confounding due to genetics, personality, and all other stable patient characteristics. The design is demonstrated using population-based individual-level health data from Ontario, Canada, for three separate medical conditions (n > 100,000 for each) related to the risk of a motor vehicle crash (total outcomes, >2,000 for each). The exposure-crossover design yields numerical risk estimates during the baseline interval before an intervention, the induction interval immediately ahead of the intervention, and the subsequent interval after the intervention. Accompanying graphs summarize results, provide an intuitive display to readers, and show risk comparisons (absolute and relative). Self-matching increases statistical efficiency, reduces selection bias, and yields quantitative analyses. The design has potential limitations related to confounding, artifacts, pragmatics, survivor bias, statistical models, potential misunderstandings, and serendipity. The exposure-crossover design may help in exploring selected questions in epidemiology science. Copyright © 2013 Elsevier Inc. All rights reserved.

Surveying drainage culvert use by carnivores: sampling design and cost-benefit analyzes of track-pads vs. video-surveillance methods.

PubMed

Mateus, Ana Rita A; Grilo, Clara; Santos-Reis, Margarida

2011-10-01

Environmental assessment studies often evaluate the effectiveness of drainage culverts as habitat linkages for species, however, the efficiency of the sampling designs and the survey methods are not known. Our main goal was to estimate the most cost-effective monitoring method for sampling carnivore culvert using track-pads and video-surveillance. We estimated the most efficient (lower costs and high detection success) interval between visits (days) when using track-pads and also determined the advantages of using each method. In 2006, we selected two highways in southern Portugal and sampled 15 culverts over two 10-day sampling periods (spring and summer). Using the track-pad method, 90% of the animal tracks were detected using a 2-day interval between visits. We recorded a higher number of crossings for most species using video-surveillance (n = 129) when compared with the track-pad technique (n = 102); however, the detection ability using the video-surveillance method varied with type of structure and species. More crossings were detected in circular culverts (1 m and 1.5 m diameter) than in box culverts (2 m to 4 m width), likely because video cameras had a reduced vision coverage area. On the other hand, carnivore species with small feet such as the common genet Genetta genetta were detected less often using the track-pad surveying method. The cost-benefit analyzes shows that the track-pad technique is the most appropriate technique, but video-surveillance allows year-round surveys as well as the behavior response analyzes of species using crossing structures.

Overconfidence in Interval Estimates: What Does Expertise Buy You?

ERIC Educational Resources Information Center

McKenzie, Craig R. M.; Liersch, Michael J.; Yaniv, Ilan

2008-01-01

People's 90% subjective confidence intervals typically contain the true value about 50% of the time, indicating extreme overconfidence. Previous results have been mixed regarding whether experts are as overconfident as novices. Experiment 1 examined interval estimates from information technology (IT) professionals and UC San Diego (UCSD) students…

Estimation of aquifer scale proportion using equal area grids: assessment of regional scale groundwater quality

USGS Publications Warehouse

Belitz, Kenneth; Jurgens, Bryant C.; Landon, Matthew K.; Fram, Miranda S.; Johnson, Tyler D.

2010-01-01

The proportion of an aquifer with constituent concentrations above a specified threshold (high concentrations) is taken as a nondimensional measure of regional scale water quality. If computed on the basis of area, it can be referred to as the aquifer scale proportion. A spatially unbiased estimate of aquifer scale proportion and a confidence interval for that estimate are obtained through the use of equal area grids and the binomial distribution. Traditionally, the confidence interval for a binomial proportion is computed using either the standard interval or the exact interval. Research from the statistics literature has shown that the standard interval should not be used and that the exact interval is overly conservative. On the basis of coverage probability and interval width, the Jeffreys interval is preferred. If more than one sample per cell is available, cell declustering is used to estimate the aquifer scale proportion, and Kish's design effect may be useful for estimating an effective number of samples. The binomial distribution is also used to quantify the adequacy of a grid with a given number of cells for identifying a small target, defined as a constituent that is present at high concentrations in a small proportion of the aquifer. Case studies illustrate a consistency between approaches that use one well per grid cell and many wells per cell. The methods presented in this paper provide a quantitative basis for designing a sampling program and for utilizing existing data.

Tutorial: Asteroseismic Stellar Modelling with AIMS

NASA Astrophysics Data System (ADS)

Lund, Mikkel N.; Reese, Daniel R.

The goal of aims (Asteroseismic Inference on a Massive Scale) is to estimate stellar parameters and credible intervals/error bars in a Bayesian manner from a set of asteroseismic frequency data and so-called classical constraints. To achieve reliable parameter estimates and computational efficiency, it searches through a grid of pre-computed models using an MCMC algorithm—interpolation within the grid of models is performed by first tessellating the grid using a Delaunay triangulation and then doing a linear barycentric interpolation on matching simplexes. Inputs for the modelling consist of individual frequencies from peak-bagging, which can be complemented with classical spectroscopic constraints. aims is mostly written in Python with a modular structure to facilitate contributions from the community. Only a few computationally intensive parts have been rewritten in Fortran in order to speed up calculations.

Efficiency and productivity assessment of public hospitals in Greece during the crisis period 2009-2012.

PubMed

Xenos, P; Yfantopoulos, J; Nektarios, M; Polyzos, N; Tinios, P; Constantopoulos, A

2017-01-01

This study is an initial effort to examine the dynamics of efficiency and productivity in Greek public hospitals during the first phase of the crisis 2009-2012. Data were collected by the Ministry of Health after several quality controls ensuring comparability and validity of hospital inputs and outputs. Productivity is estimated using the Malmquist Indicator, decomposing the estimated values into efficiency and technological change. Hospital efficiency and productivity growth are calculated by bootstrapping the non-parametric Malmquist analysis. The advantage of this method is the estimation efficiency and productivity through the corresponding confidence intervals. Additionally, a Random-effects Tobit model is explored to investigate the impact of contextual factors on the magnitude of efficiency. Findings reveal substantial variations in hospital productivity over the period from 2009 to 2012. The economic crisis of 2009 had a negative impact in productivity. The average Malmquist Productivity Indicator (MPI) score is 0.72 with unity signifying stable production. Approximately 91% of the hospitals score lower than unity. Substantial increase is observed between 2010 and 2011, as indicated by the average MPI score which fluctuates to 1.52. Moreover, technology change scored more than unity in more than 75% of hospitals. The last period (2011-2012) has shown stabilization in the expansionary process of productivity. The main factors contributing to overall productivity gains are increases in occupancy rates, type and size of the hospital. This paper attempts to offer insights in efficiency and productivity growth for public hospitals in Greece. The results suggest that the average hospital experienced substantial productivity growth between 2009 and 2012 as indicated by variations in MPI. Almost all of the productivity increase was due to technology change which could be explained by the concurrent managerial and financing healthcare reforms. Hospitals operating under decreasing returns to scale could achieve higher efficiency rates by reducing their capacity. However, certain social objectives should also be considered. Emphasis perhaps should be placed in utilizing and advancing managerial and organizational reforms, so that the benefits of technological improvements will have a continuing positive impact in the future.

How to assess the efficiency of synchronization experiments in tokamaks

NASA Astrophysics Data System (ADS)

Murari, A.; Craciunescu, T.; Peluso, E.; Gelfusa, M.; Lungaroni, M.; Garzotti, L.; Frigione, D.; Gaudio, P.; Contributors, JET

2016-07-01

Control of instabilities such as ELMs and sawteeth is considered an important ingredient in the development of reactor-relevant scenarios. Various forms of ELM pacing have been tried in the past to influence their behavior using external perturbations. One of the main problems with these synchronization experiments resides in the fact that ELMs are periodic or quasi-periodic in nature. Therefore, after any pulsed perturbation, if one waits long enough, an ELM is always bound to occur. To evaluate the effectiveness of ELM pacing techniques, it is crucial to determine an appropriate interval over which they can have a real influence and an effective triggering capability. In this paper, three independent statistical methods are described to address this issue: Granger causality, transfer entropy and recurrence plots. The obtained results for JET with the ITER-like wall (ILW) indicate that the proposed techniques agree very well and provide much better estimates than the traditional heuristic criteria reported in the literature. Moreover, their combined use allows for the improvement of the time resolution of the assessment and determination of the efficiency of the pellet triggering in different phases of the same discharge. Therefore, the developed methods can be used to provide a quantitative and statistically robust estimate of the triggering efficiency of ELM pacing under realistic experimental conditions.

Optimal Measurement Interval for Emergency Department Crowding Estimation Tools.

PubMed

Wang, Hao; Ojha, Rohit P; Robinson, Richard D; Jackson, Bradford E; Shaikh, Sajid A; Cowden, Chad D; Shyamanand, Rath; Leuck, JoAnna; Schrader, Chet D; Zenarosa, Nestor R

2017-11-01

Emergency department (ED) crowding is a barrier to timely care. Several crowding estimation tools have been developed to facilitate early identification of and intervention for crowding. Nevertheless, the ideal frequency is unclear for measuring ED crowding by using these tools. Short intervals may be resource intensive, whereas long ones may not be suitable for early identification. Therefore, we aim to assess whether outcomes vary by measurement interval for 4 crowding estimation tools. Our eligible population included all patients between July 1, 2015, and June 30, 2016, who were admitted to the JPS Health Network ED, which serves an urban population. We generated 1-, 2-, 3-, and 4-hour ED crowding scores for each patient, using 4 crowding estimation tools (National Emergency Department Overcrowding Scale [NEDOCS], Severely Overcrowded, Overcrowded, and Not Overcrowded Estimation Tool [SONET], Emergency Department Work Index [EDWIN], and ED Occupancy Rate). Our outcomes of interest included ED length of stay (minutes) and left without being seen or eloped within 4 hours. We used accelerated failure time models to estimate interval-specific time ratios and corresponding 95% confidence limits for length of stay, in which the 1-hour interval was the reference. In addition, we used binomial regression with a log link to estimate risk ratios (RRs) and corresponding confidence limit for left without being seen. Our study population comprised 117,442 patients. The time ratios for length of stay were similar across intervals for each crowding estimation tool (time ratio=1.37 to 1.30 for NEDOCS, 1.44 to 1.37 for SONET, 1.32 to 1.27 for EDWIN, and 1.28 to 1.23 for ED Occupancy Rate). The RRs of left without being seen differences were also similar across intervals for each tool (RR=2.92 to 2.56 for NEDOCS, 3.61 to 3.36 for SONET, 2.65 to 2.40 for EDWIN, and 2.44 to 2.14 for ED Occupancy Rate). Our findings suggest limited variation in length of stay or left without being seen between intervals (1 to 4 hours) regardless of which of the 4 crowding estimation tools were used. Consequently, 4 hours may be a reasonable interval for assessing crowding with these tools, which could substantially reduce the burden on ED personnel by requiring less frequent assessment of crowding. Copyright © 2017 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

Interval Timing Accuracy and Scalar Timing in C57BL/6 Mice

PubMed Central

Buhusi, Catalin V.; Aziz, Dyana; Winslow, David; Carter, Rickey E.; Swearingen, Joshua E.; Buhusi, Mona C.

2010-01-01

In many species, interval timing behavior is accurate—appropriate estimated durations—and scalar—errors vary linearly with estimated durations. While accuracy has been previously examined, scalar timing has not been yet clearly demonstrated in house mice (Mus musculus), raising concerns about mouse models of human disease. We estimated timing accuracy and precision in C57BL/6 mice, the most used background strain for genetic models of human disease, in a peak-interval procedure with multiple intervals. Both when timing two intervals (Experiment 1) or three intervals (Experiment 2), C57BL/6 mice demonstrated varying degrees of timing accuracy. Importantly, both at individual and group level, their precision varied linearly with the subjective estimated duration. Further evidence for scalar timing was obtained using an intraclass correlation statistic. This is the first report of consistent, reliable scalar timing in a sizable sample of house mice, thus validating the PI procedure as a valuable technique, the intraclass correlation statistic as a powerful test of the scalar property, and the C57BL/6 strain as a suitable background for behavioral investigations of genetically engineered mice modeling disorders of interval timing. PMID:19824777

Notes on testing equality and interval estimation in Poisson frequency data under a three-treatment three-period crossover trial.

PubMed

Lui, Kung-Jong; Chang, Kuang-Chao

2016-10-01

When the frequency of event occurrences follows a Poisson distribution, we develop procedures for testing equality of treatments and interval estimators for the ratio of mean frequencies between treatments under a three-treatment three-period crossover design. Using Monte Carlo simulations, we evaluate the performance of these test procedures and interval estimators in various situations. We note that all test procedures developed here can perform well with respect to Type I error even when the number of patients per group is moderate. We further note that the two weighted-least-squares (WLS) test procedures derived here are generally preferable to the other two commonly used test procedures in the contingency table analysis. We also demonstrate that both interval estimators based on the WLS method and interval estimators based on Mantel-Haenszel (MH) approach can perform well, and are essentially of equal precision with respect to the average length. We use a double-blind randomized three-treatment three-period crossover trial comparing salbutamol and salmeterol with a placebo with respect to the number of exacerbations of asthma to illustrate the use of these test procedures and estimators. © The Author(s) 2014.

Particle filter based hybrid prognostics for health monitoring of uncertain systems in bond graph framework

NASA Astrophysics Data System (ADS)

Jha, Mayank Shekhar; Dauphin-Tanguy, G.; Ould-Bouamama, B.

2016-06-01

The paper's main objective is to address the problem of health monitoring of system parameters in Bond Graph (BG) modeling framework, by exploiting its structural and causal properties. The system in feedback control loop is considered uncertain globally. Parametric uncertainty is modeled in interval form. The system parameter is undergoing degradation (prognostic candidate) and its degradation model is assumed to be known a priori. The detection of degradation commencement is done in a passive manner which involves interval valued robust adaptive thresholds over the nominal part of the uncertain BG-derived interval valued analytical redundancy relations (I-ARRs). The latter forms an efficient diagnostic module. The prognostics problem is cast as joint state-parameter estimation problem, a hybrid prognostic approach, wherein the fault model is constructed by considering the statistical degradation model of the system parameter (prognostic candidate). The observation equation is constructed from nominal part of the I-ARR. Using particle filter (PF) algorithms; the estimation of state of health (state of prognostic candidate) and associated hidden time-varying degradation progression parameters is achieved in probabilistic terms. A simplified variance adaptation scheme is proposed. Associated uncertainties which arise out of noisy measurements, parametric degradation process, environmental conditions etc. are effectively managed by PF. This allows the production of effective predictions of the remaining useful life of the prognostic candidate with suitable confidence bounds. The effectiveness of the novel methodology is demonstrated through simulations and experiments on a mechatronic system.

Characterization and Uncertainty Analysis of a Reference Pressure Measurement System for Wind Tunnels

NASA Technical Reports Server (NTRS)

Amer, Tahani; Tripp, John; Tcheng, Ping; Burkett, Cecil; Sealey, Bradley

2004-01-01

This paper presents the calibration results and uncertainty analysis of a high-precision reference pressure measurement system currently used in wind tunnels at the NASA Langley Research Center (LaRC). Sensors, calibration standards, and measurement instruments are subject to errors due to aging, drift with time, environment effects, transportation, the mathematical model, the calibration experimental design, and other factors. Errors occur at every link in the chain of measurements and data reduction from the sensor to the final computed results. At each link of the chain, bias and precision uncertainties must be separately estimated for facility use, and are combined to produce overall calibration and prediction confidence intervals for the instrument, typically at a 95% confidence level. The uncertainty analysis and calibration experimental designs used herein, based on techniques developed at LaRC, employ replicated experimental designs for efficiency, separate estimation of bias and precision uncertainties, and detection of significant parameter drift with time. Final results, including calibration confidence intervals and prediction intervals given as functions of the applied inputs, not as a fixed percentage of the full-scale value are presented. System uncertainties are propagated beginning with the initial reference pressure standard, to the calibrated instrument as a working standard in the facility. Among the several parameters that can affect the overall results are operating temperature, atmospheric pressure, humidity, and facility vibration. Effects of factors such as initial zeroing and temperature are investigated. The effects of the identified parameters on system performance and accuracy are discussed.

Estimation of density of mongooses with capture-recapture and distance sampling

USGS Publications Warehouse

Corn, J.L.; Conroy, M.J.

1998-01-01

We captured mongooses (Herpestes javanicus) in live traps arranged in trapping webs in Antigua, West Indies, and used capture-recapture and distance sampling to estimate density. Distance estimation and program DISTANCE were used to provide estimates of density from the trapping-web data. Mean density based on trapping webs was 9.5 mongooses/ha (range, 5.9-10.2/ha); estimates had coefficients of variation ranging from 29.82-31.58% (X?? = 30.46%). Mark-recapture models were used to estimate abundance, which was converted to density using estimates of effective trap area. Tests of model assumptions provided by CAPTURE indicated pronounced heterogeneity in capture probabilities and some indication of behavioral response and variation over time. Mean estimated density was 1.80 mongooses/ha (range, 1.37-2.15/ha) with estimated coefficients of variation of 4.68-11.92% (X?? = 7.46%). Estimates of density based on mark-recapture data depended heavily on assumptions about animal home ranges; variances of densities also may be underestimated, leading to unrealistically narrow confidence intervals. Estimates based on trap webs require fewer assumptions, and estimated variances may be a more realistic representation of sampling variation. Because trap webs are established easily and provide adequate data for estimation in a few sample occasions, the method should be efficient and reliable for estimating densities of mongooses.

The Covariance Adjustment Approaches for Combining Incomparable Cox Regressions Caused by Unbalanced Covariates Adjustment: A Multivariate Meta-Analysis Study.

PubMed

Dehesh, Tania; Zare, Najaf; Ayatollahi, Seyyed Mohammad Taghi

2015-01-01

Univariate meta-analysis (UM) procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS) method as a multivariate meta-analysis approach. We evaluated the efficiency of four new approaches including zero correlation (ZC), common correlation (CC), estimated correlation (EC), and multivariate multilevel correlation (MMC) on the estimation bias, mean square error (MSE), and 95% probability coverage of the confidence interval (CI) in the synthesis of Cox proportional hazard models coefficients in a simulation study. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.

On the appropriateness of applying chi-square distribution based confidence intervals to spectral estimates of helicopter flyover data

NASA Technical Reports Server (NTRS)

Rutledge, Charles K.

1988-01-01

The validity of applying chi-square based confidence intervals to far-field acoustic flyover spectral estimates was investigated. Simulated data, using a Kendall series and experimental acoustic data from the NASA/McDonnell Douglas 500E acoustics test, were analyzed. Statistical significance tests to determine the equality of distributions of the simulated and experimental data relative to theoretical chi-square distributions were performed. Bias and uncertainty errors associated with the spectral estimates were easily identified from the data sets. A model relating the uncertainty and bias errors to the estimates resulted, which aided in determining the appropriateness of the chi-square distribution based confidence intervals. Such confidence intervals were appropriate for nontonally associated frequencies of the experimental data but were inappropriate for tonally associated estimate distributions. The appropriateness at the tonally associated frequencies was indicated by the presence of bias error and noncomformity of the distributions to the theoretical chi-square distribution. A technique for determining appropriate confidence intervals at the tonally associated frequencies was suggested.

Partial-Interval Estimation of Count: Uncorrected and Poisson-Corrected Error Levels

ERIC Educational Resources Information Center

Yoder, Paul J.; Ledford, Jennifer R.; Harbison, Amy L.; Tapp, Jon T.

2018-01-01

A simulation study that used 3,000 computer-generated event streams with known behavior rates, interval durations, and session durations was conducted to test whether the main and interaction effects of true rate and interval duration affect the error level of uncorrected and Poisson-transformed (i.e., "corrected") count as estimated by…

Confidence Intervals for Effect Sizes: Applying Bootstrap Resampling

ERIC Educational Resources Information Center

Banjanovic, Erin S.; Osborne, Jason W.

2016-01-01

Confidence intervals for effect sizes (CIES) provide readers with an estimate of the strength of a reported statistic as well as the relative precision of the point estimate. These statistics offer more information and context than null hypothesis statistic testing. Although confidence intervals have been recommended by scholars for many years,…

Impact of heart disease and calibration interval on accuracy of pulse transit time-based blood pressure estimation.

PubMed

Ding, Xiaorong; Zhang, Yuanting; Tsang, Hon Ki

2016-02-01

Continuous blood pressure (BP) measurement without a cuff is advantageous for the early detection and prevention of hypertension. The pulse transit time (PTT) method has proven to be promising for continuous cuffless BP measurement. However, the problem of accuracy is one of the most challenging aspects before the large-scale clinical application of this method. Since PTT-based BP estimation relies primarily on the relationship between PTT and BP under certain assumptions, estimation accuracy will be affected by cardiovascular disorders that impair this relationship and by the calibration frequency, which may violate these assumptions. This study sought to examine the impact of heart disease and the calibration interval on the accuracy of PTT-based BP estimation. The accuracy of a PTT-BP algorithm was investigated in 37 healthy subjects and 48 patients with heart disease at different calibration intervals, namely 15 min, 2 weeks, and 1 month after initial calibration. The results showed that the overall accuracy of systolic BP estimation was significantly lower in subjects with heart disease than in healthy subjects, but diastolic BP estimation was more accurate in patients than in healthy subjects. The accuracy of systolic and diastolic BP estimation becomes less reliable with longer calibration intervals. These findings demonstrate that both heart disease and the calibration interval can influence the accuracy of PTT-based BP estimation and should be taken into consideration to improve estimation accuracy.

Image informative maps for component-wise estimating parameters of signal-dependent noise

NASA Astrophysics Data System (ADS)

Uss, Mykhail L.; Vozel, Benoit; Lukin, Vladimir V.; Chehdi, Kacem

2013-01-01

We deal with the problem of blind parameter estimation of signal-dependent noise from mono-component image data. Multispectral or color images can be processed in a component-wise manner. The main results obtained rest on the assumption that the image texture and noise parameters estimation problems are interdependent. A two-dimensional fractal Brownian motion (fBm) model is used for locally describing image texture. A polynomial model is assumed for the purpose of describing the signal-dependent noise variance dependence on image intensity. Using the maximum likelihood approach, estimates of both fBm-model and noise parameters are obtained. It is demonstrated that Fisher information (FI) on noise parameters contained in an image is distributed nonuniformly over intensity coordinates (an image intensity range). It is also shown how to find the most informative intensities and the corresponding image areas for a given noisy image. The proposed estimator benefits from these detected areas to improve the estimation accuracy of signal-dependent noise parameters. Finally, the potential estimation accuracy (Cramér-Rao Lower Bound, or CRLB) of noise parameters is derived, providing confidence intervals of these estimates for a given image. In the experiment, the proposed and existing state-of-the-art noise variance estimators are compared for a large image database using CRLB-based statistical efficiency criteria.

Estimation of sojourn time in chronic disease screening without data on interval cases.

PubMed

Chen, T H; Kuo, H S; Yen, M F; Lai, M S; Tabar, L; Duffy, S W

2000-03-01

Estimation of the sojourn time on the preclinical detectable period in disease screening or transition rates for the natural history of chronic disease usually rely on interval cases (diagnosed between screens). However, to ascertain such cases might be difficult in developing countries due to incomplete registration systems and difficulties in follow-up. To overcome this problem, we propose three Markov models to estimate parameters without using interval cases. A three-state Markov model, a five-state Markov model related to regional lymph node spread, and a five-state Markov model pertaining to tumor size are applied to data on breast cancer screening in female relatives of breast cancer cases in Taiwan. Results based on a three-state Markov model give mean sojourn time (MST) 1.90 (95% CI: 1.18-4.86) years for this high-risk group. Validation of these models on the basis of data on breast cancer screening in the age groups 50-59 and 60-69 years from the Swedish Two-County Trial shows the estimates from a three-state Markov model that does not use interval cases are very close to those from previous Markov models taking interval cancers into account. For the five-state Markov model, a reparameterized procedure using auxiliary information on clinically detected cancers is performed to estimate relevant parameters. A good fit of internal and external validation demonstrates the feasibility of using these models to estimate parameters that have previously required interval cancers. This method can be applied to other screening data in which there are no data on interval cases.

Interpretation of Flow Logs from Nevada Test Site Boreholes to Estimate Hydraulic Conductivity Using Numerical Simulations Constrained by Single-Well Aquifer Tests

USGS Publications Warehouse

Garcia, C. Amanda; Halford, Keith J.; Laczniak, Randell J.

2010-01-01

Hydraulic conductivities of volcanic and carbonate lithologic units at the Nevada Test Site were estimated from flow logs and aquifer-test data. Borehole flow and drawdown were integrated and interpreted using a radial, axisymmetric flow model, AnalyzeHOLE. This integrated approach is used because complex well completions and heterogeneous aquifers and confining units produce vertical flow in the annular space and aquifers adjacent to the wellbore. AnalyzeHOLE simulates vertical flow, in addition to horizontal flow, which accounts for converging flow toward screen ends and diverging flow toward transmissive intervals. Simulated aquifers and confining units uniformly are subdivided by depth into intervals in which the hydraulic conductivity is estimated with the Parameter ESTimation (PEST) software. Between 50 and 150 hydraulic-conductivity parameters were estimated by minimizing weighted differences between simulated and measured flow and drawdown. Transmissivity estimates from single-well or multiple-well aquifer tests were used to constrain estimates of hydraulic conductivity. The distribution of hydraulic conductivity within each lithology had a minimum variance because estimates were constrained with Tikhonov regularization. AnalyzeHOLE simulated hydraulic-conductivity estimates for lithologic units across screened and cased intervals are as much as 100 times less than those estimated using proportional flow-log analyses applied across screened intervals only. Smaller estimates of hydraulic conductivity for individual lithologic units are simulated because sections of the unit behind cased intervals of the wellbore are not assumed to be impermeable, and therefore, can contribute flow to the wellbore. Simulated hydraulic-conductivity estimates vary by more than three orders of magnitude across a lithologic unit, indicating a high degree of heterogeneity in volcanic and carbonate-rock units. The higher water transmitting potential of carbonate-rock units relative to volcanic-rock units is exemplified by the large difference in their estimated maximum hydraulic conductivity; 4,000 and 400 feet per day, respectively. Simulated minimum estimates of hydraulic conductivity are inexact and represent the lower detection limit of the method. Minimum thicknesses of lithologic intervals also were defined for comparing AnalyzeHOLE results to hydraulic properties in regional ground-water flow models.

Expression of Proteins Involved in Epithelial-Mesenchymal Transition as Predictors of Metastasis and Survival in Breast Cancer Patients

DTIC Science & Technology

2013-11-01

Ptrend 0.78 0.62 0.75 Unconditional logistic regression was used to estimate odds ratios (OR) and 95 % confidence intervals (CI) for risk of node...Ptrend 0.71 0.67 Unconditional logistic regression was used to estimate odds ratios (OR) and 95 % confidence intervals (CI) for risk of high-grade tumors... logistic regression was used to estimate odds ratios (OR) and 95 % confidence intervals (CI) for the associations between each of the seven SNPs and

Impulsive control of a financial model [rapid communication

NASA Astrophysics Data System (ADS)

Sun, Jitao; Qiao, Fei; Wu, Qidi

2005-02-01

In this Letter, several new theorems on the stability of impulsive control systems are presented. These theorem are then used to find the conditions under which an advertising strategy can be asymptotically control to the equilibrium point by using impulsive control. Given the parameters of the financial model and the impulsive control law, an estimation of the upper bound of the impulse interval is given, i.e., number of advert can been decreased (i.e., can decrease cost) for to obtain the equivalent advertising effect.The result is illustrated to be efficient through a numerical example.

Increasing point-count duration increases standard error

USGS Publications Warehouse

Smith, W.P.; Twedt, D.J.; Hamel, P.B.; Ford, R.P.; Wiedenfeld, D.A.; Cooper, R.J.

1998-01-01

We examined data from point counts of varying duration in bottomland forests of west Tennessee and the Mississippi Alluvial Valley to determine if counting interval influenced sampling efficiency. Estimates of standard error increased as point count duration increased both for cumulative number of individuals and species in both locations. Although point counts appear to yield data with standard errors proportional to means, a square root transformation of the data may stabilize the variance. Using long (>10 min) point counts may reduce sample size and increase sampling error, both of which diminish statistical power and thereby the ability to detect meaningful changes in avian populations.

Calculating Confidence Intervals for Regional Economic Impacts of Recreastion by Bootstrapping Visitor Expenditures

Treesearch

Donald B.K. English

2000-01-01

In this paper I use bootstrap procedures to develop confidence intervals for estimates of total industrial output generated per thousand tourist visits. Mean expenditures from replicated visitor expenditure data included weights to correct for response bias. Impacts were estimated with IMPLAN. Ninety percent interval endpoints were 6 to 16 percent above or below the...

Magnetic Resonance Fingerprinting with short relaxation intervals.

PubMed

Amthor, Thomas; Doneva, Mariya; Koken, Peter; Sommer, Karsten; Meineke, Jakob; Börnert, Peter

2017-09-01

The aim of this study was to investigate a technique for improving the performance of Magnetic Resonance Fingerprinting (MRF) in repetitive sampling schemes, in particular for 3D MRF acquisition, by shortening relaxation intervals between MRF pulse train repetitions. A calculation method for MRF dictionaries adapted to short relaxation intervals and non-relaxed initial spin states is presented, based on the concept of stationary fingerprints. The method is applicable to many different k-space sampling schemes in 2D and 3D. For accuracy analysis, T 1 and T 2 values of a phantom are determined by single-slice Cartesian MRF for different relaxation intervals and are compared with quantitative reference measurements. The relevance of slice profile effects is also investigated in this case. To further illustrate the capabilities of the method, an application to in-vivo spiral 3D MRF measurements is demonstrated. The proposed computation method enables accurate parameter estimation even for the shortest relaxation intervals, as investigated for different sampling patterns in 2D and 3D. In 2D Cartesian measurements, we achieved a scan acceleration of more than a factor of two, while maintaining acceptable accuracy: The largest T 1 values of a sample set deviated from their reference values by 0.3% (longest relaxation interval) and 2.4% (shortest relaxation interval). The largest T 2 values showed systematic deviations of up to 10% for all relaxation intervals, which is discussed. The influence of slice profile effects for multislice acquisition is shown to become increasingly relevant for short relaxation intervals. In 3D spiral measurements, a scan time reduction of 36% was achieved, maintaining the quality of in-vivo T1 and T2 maps. Reducing the relaxation interval between MRF sequence repetitions using stationary fingerprint dictionaries is a feasible method to improve the scan efficiency of MRF sequences. The method enables fast implementations of 3D spatially resolved MRF. Copyright © 2017 Elsevier Inc. All rights reserved.

Estimated preejection period (PEP) based on the detection of the R-wave and dZ/dt-min peaks in ECG and ICG

NASA Astrophysics Data System (ADS)

van Lien, René; Schutte, Nienke M.; Meijer, Jan H.; de Geus, Eco J. C.

2013-04-01

The validity of estimating the PEP from a fixed value for the Q-wave onset to the R-wave peak (QR) interval and from the R-wave peak to the dZ/dt-min peak (ISTI) interval is evaluated. Ninety-one subjects participated in a laboratory experiment in which a variety of physical and mental stressors were presented and 31 further subjects participated in a sequence of structured ambulatory activities in which large variation in posture and physical activity was induced. PEP, QR interval, and ISTI were scored. Across the diverse laboratory and ambulatory conditions the QR interval could be approximated by a fixed interval of 40 ms but 95% confidence intervals were large (25 to 54 ms). Multilevel analysis showed that 79% to 81% of the within and between-subject variation in the RB interval could be predicted by the ISTI. However, the optimal intercept and slope values varied significantly across subjects and study setting. Bland-Altman plots revealed a large discrepancy between the estimated PEP and the actual PEP based on the Q-wave onset and B-point. It is concluded that the estimated PEP can be a useful tool but cannot replace the actual PEP to index cardiac sympathetic control.

Valuation of National Park System Visitation: The Efficient Use of Count Data Models, Meta-Analysis, and Secondary Visitor Survey Data

NASA Astrophysics Data System (ADS)

Neher, Christopher; Duffield, John; Patterson, David

2013-09-01

The National Park Service (NPS) currently manages a large and diverse system of park units nationwide which received an estimated 279 million recreational visits in 2011. This article uses park visitor data collected by the NPS Visitor Services Project to estimate a consistent set of count data travel cost models of park visitor willingness to pay (WTP). Models were estimated using 58 different park unit survey datasets. WTP estimates for these 58 park surveys were used within a meta-regression analysis model to predict average and total WTP for NPS recreational visitation system-wide. Estimated WTP per NPS visit in 2011 averaged 102 system-wide, and ranged across park units from 67 to 288. Total 2011 visitor WTP for the NPS system is estimated at 28.5 billion with a 95% confidence interval of 19.7-43.1 billion. The estimation of a meta-regression model using consistently collected data and identical specification of visitor WTP models greatly reduces problems common to meta-regression models, including sample selection bias, primary data heterogeneity, and heteroskedasticity, as well as some aspects of panel effects. The article provides the first estimate of total annual NPS visitor WTP within the literature directly based on NPS visitor survey data.

Time estimation as a secondary task to measure workload: Summary of research

NASA Technical Reports Server (NTRS)

Hart, S. G.; Mcpherson, D.; Loomis, L. L.

1978-01-01

Actively produced intervals of time were found to increase in length and variability, whereas retrospectively produced intervals decreased in length although they also increased in variability with the addition of a variety of flight-related tasks. If pilots counted aloud while making a production, however, the impact of concurrent activity was minimized, at least for the moderately demanding primary tasks that were selected. The effects of feedback on estimation accuracy and consistency were greatly enhanced if a counting or tapping production technique was used. This compares with the minimal effect that feedback had when no overt timekeeping technique was used. Actively made verbal estimates of sessions filled with different activities performed during the interval were increased. Retrospectively made verbal estimates, however, increased in length as the amount and complexity of activities performed during the interval were increased.

Binary Interval Search: a scalable algorithm for counting interval intersections.

PubMed

Layer, Ryan M; Skadron, Kevin; Robins, Gabriel; Hall, Ira M; Quinlan, Aaron R

2013-01-01

The comparison of diverse genomic datasets is fundamental to understand genome biology. Researchers must explore many large datasets of genome intervals (e.g. genes, sequence alignments) to place their experimental results in a broader context and to make new discoveries. Relationships between genomic datasets are typically measured by identifying intervals that intersect, that is, they overlap and thus share a common genome interval. Given the continued advances in DNA sequencing technologies, efficient methods for measuring statistically significant relationships between many sets of genomic features are crucial for future discovery. We introduce the Binary Interval Search (BITS) algorithm, a novel and scalable approach to interval set intersection. We demonstrate that BITS outperforms existing methods at counting interval intersections. Moreover, we show that BITS is intrinsically suited to parallel computing architectures, such as graphics processing units by illustrating its utility for efficient Monte Carlo simulations measuring the significance of relationships between sets of genomic intervals. https://github.com/arq5x/bits.

[Reflection of estimating postmortem interval in forensic entomology and the Daubert standard].

PubMed

Xie, Dan; Peng, Yu-Long; Guo, Ya-Dong; Cai, Ji-Feng

2013-08-01

Estimating postmortem interval (PMI) is always the emphasis and difficulty in forensic practice. Forensic entomology plays a significant indispensable role. Recently, the theories and technologies of forensic entomology are increasingly rich. But many problems remain in the research and practice. With proposing the Daubert standard, the reliability and accuracy of estimation PMI by forensic entomology need more demands. This review summarizes the application of the Daubert standard in several aspects of ecology, quantitative genetics, population genetics, molecular biology, and microbiology in the practice of forensic entomology. It builds a bridge for basic research and forensic practice to provide higher accuracy for estimating postmortem interval by forensic entomology.

Application of biological simulation models in estimating feed efficiency of finishing steers.

PubMed

Williams, C B

2010-07-01

Data on individual daily feed intake, BW at 28-d intervals, and carcass composition were obtained on 1,212 crossbred steers. Within-animal regressions of cumulative feed intake and BW on linear and quadratic days on feed were used to quantify initial and ending BW, average daily observed feed intake (OFI), and ADG over a 120-d finishing period. Feed intake was predicted (PFI) with 3 biological simulation models (BSM): a) Decision Evaluator for the Cattle Industry, b) Cornell Value Discovery System, and c) NRC update 2000, using observed growth and carcass data as input. Residual feed intake (RFI) was estimated using OFI (RFI(EL)) in a linear statistical model (LSM), and feed conversion ratio (FCR) was estimated as OFI/ADG (FCR(E)). Output from the BSM was used to estimate RFI by using PFI in place of OFI with the same LSM, and FCR was estimated as PFI/ADG. These estimates were evaluated against RFI(EL) and FCR(E). In a second analysis, estimates of RFI were obtained for the 3 BSM as the difference between OFI and PFI, and these estimates were evaluated against RFI(EL). The residual variation was extremely small when PFI was used in the LSM to estimate RFI, and this was mainly due to the fact that the same input variables (initial BW, days on feed, and ADG) were used in the BSM and LSM. Hence, the use of PFI obtained with BSM as a replacement for OFI in a LSM to characterize individual animals for RFI was not feasible. This conclusion was also supported by weak correlations (<0.4) between RFI(EL) and RFI obtained with PFI in the LSM, and very weak correlations (<0.13) between RFI(EL) and FCR obtained with PFI. In the second analysis, correlations (>0.89) for RFI(EL) with the other RFI estimates suggest little difference between RFI(EL) and any of these RFI estimates. In addition, results suggest that the RFI estimates calculated with PFI would be better able to identify animals with low OFI and small ADG as inefficient compared with RFI(EL). These results may be due to the fact that computer models predict performance on an individual-animal basis in contrast to a LSM, which estimates a fixed relationship for all animals; hence, the BSM may provide RFI estimates that are closer to the true biological efficiency of animals. In addition, BSM may facilitate comparisons across different data sets and provide more accurate estimates of efficiency in small data sets where errors would be greater with a LSM.

Genetics of alternative definitions of feed efficiency in grazing lactating dairy cows.

PubMed

Hurley, A M; López-Villalobos, N; McParland, S; Lewis, E; Kennedy, E; O'Donovan, M; Burke, J L; Berry, D P

2017-07-01

The objective of the present study was to estimate genetic parameters across lactation for measures of energy balance (EB) and a range of feed efficiency variables as well as to quantify the genetic inter-relationships between them. Net energy intake (NEI) from pasture and concentrate intake was estimated up to 8 times per lactation for 2,481 lactations from 1,274 Holstein-Friesian cows. A total of 8,134 individual feed intake measurements were used. Efficiency traits were either ratio based or residual based; the latter were derived from least squares regression models. Residual energy intake (REI) was defined as NEI minus predicted energy requirements [e.g., net energy of lactation (NE L ), maintenance, and body tissue anabolism] or supplied from body tissue mobilization; residual energy production was defined as the difference between actual NE L and predicted NE L based on NEI, maintenance, and body tissue anabolism/catabolism. Energy conversion efficiency was defined as NE L divided by NEI. Random regression animal models were used to estimate residual, additive genetic, and permanent environmental (co)variances across lactation. Heritability across lactation stages varied from 0.03 to 0.36 for all efficiency traits. Within-trait genetic correlations tended to weaken as the interval between lactation stages compared lengthened for EB, REI, residual energy production, and NEI. Analysis of eigenvalues and associated eigenfunctions for EB and the efficiency traits indicate the ability to genetically alter the profile of these lactation curves to potentially improve dairy cow efficiency differently at different stages of lactation. Residual energy intake and EB were moderately to strongly genetically correlated with each other across lactation (genetic correlations ranged from 0.45 to 0.90), indicating that selection for lower REI alone (i.e., deemed efficient cows) would favor cows with a compromised energy status; nevertheless, selection for REI within a holistic breeding goal could be used to overcome such antagonisms. The smallest (8.90% of genetic variance) and middle (11.22% of genetic variance) eigenfunctions for REI changed sign during lactation, indicating the potential to alter the shape of the REI lactation profile. Results from the present study suggest exploitable genetic variation exists for a range of efficiency traits, and the magnitude of this variation is sufficiently large to justify consideration of the feed efficiency complex in future dairy breeding goals. Moreover, it is possible to alter the trajectories of the efficiency traits to suit a particular breeding objective, although this relies on very precise across-parity genetic parameter estimates, including genetic correlations with health and fertility traits (as well as other traits). Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Can differentiated care models solve the crisis in HIV treatment financing? Analysis of prospects for 38 countries in sub-Saharan Africa.

PubMed

Barker, Catherine; Dutta, Arin; Klein, Kate

2017-07-21

Rapid scale-up of antiretroviral therapy (ART) in the context of financial and health system constraints has resulted in calls to maximize efficiency in ART service delivery. Adopting differentiated care models (DCMs) for ART could potentially be more cost-efficient and improve outcomes. However, no study comprehensively projects the cost savings across countries. We model the potential reduction in facility-level costs and number of health workers needed when implementing two types of DCMs while attempting to reach 90-90-90 targets in 38 sub-Saharan African countries from 2016 to 2020. We estimated the costs of three service delivery models: (1) undifferentiated care, (2) differentiated care by patient age and stability, and (3) differentiated care by patient age, stability, key vs. general population status, and urban vs. rural location. Frequency of facility visits, type and frequency of laboratory testing, and coverage of community ART support vary by patient subgroup. For each model, we estimated the total costs of antiretroviral drugs, laboratory commodities, and facility-level personnel and overhead. Certain groups under four-criteria differentiation require more intensive inputs. Community-based ART costs were included in the DCMs. We take into account underlying uncertainty in the projected numbers on ART and unit costs. Total five-year facility-based ART costs for undifferentiated care are estimated to be US$23.33 billion (95% confidence interval [CI]: $23.3-$23.5 billion). An estimated 17.5% (95% CI: 17.4%-17.7%) and 16.8% (95% CI: 16.7%-17.0%) could be saved from 2016 to 2020 from implementing the age and stability DCM and four-criteria DCM, respectively, with annual cost savings increasing over time. DCMs decrease the full-time equivalent (FTE) health workforce requirements for ART. An estimated 46.4% (95% CI: 46.1%-46.7%) fewer FTE health workers are needed in 2020 for the age and stability DCM compared with undifferentiated care. Adopting DCMs can result in significant efficiency gains in terms of reduced costs and health workforce needs, even with the costs of scaling up community-based ART support under DCMs. Efficiency gains remained flat with increased differentiation. More evidence is needed on how to translate analyzed efficiency gains into implemented cost reductions at the facility level.

A Comparison of Methods for Estimating Confidence Intervals for Omega-Squared Effect Size

ERIC Educational Resources Information Center

Finch, W. Holmes; French, Brian F.

2012-01-01

Effect size use has been increasing in the past decade in many research areas. Confidence intervals associated with effect sizes are encouraged to be reported. Prior work has investigated the performance of confidence interval estimation with Cohen's d. This study extends this line of work to the analysis of variance case with more than two…

"Ersatz" and "hybrid" NMR spectral estimates using the filter diagonalization method.

PubMed

Ridge, Clark D; Shaka, A J

2009-03-12

The filter diagonalization method (FDM) is an efficient and elegant way to make a spectral estimate purely in terms of Lorentzian peaks. As NMR spectral peaks of liquids conform quite well to this model, the FDM spectral estimate can be accurate with far fewer time domain points than conventional discrete Fourier transform (DFT) processing. However, noise is not efficiently characterized by a finite number of Lorentzian peaks, or by any other analytical form, for that matter. As a result, noise can affect the FDM spectrum in different ways than it does the DFT spectrum, and the effect depends on the dimensionality of the spectrum. Regularization to suppress (or control) the influence of noise to give an "ersatz", or EFDM, spectrum is shown to sometimes miss weak features, prompting a more conservative implementation of filter diagonalization. The spectra obtained, called "hybrid" or HFDM spectra, are acquired by using regularized FDM to obtain an "infinite time" spectral estimate and then adding to it the difference between the DFT of the data and the finite time FDM estimate, over the same time interval. HFDM has a number of advantages compared to the EFDM spectra, where all features must be Lorentzian. They also show better resolution than DFT spectra. The HFDM spectrum is a reliable and robust way to try to extract more information from noisy, truncated data records and is less sensitive to the choice of regularization parameter. In multidimensional NMR of liquids, HFDM is a conservative way to handle the problems of noise, truncation, and spectral peaks that depart significantly from the model of a multidimensional Lorentzian peak.

Overconfidence in Interval Estimates

ERIC Educational Resources Information Center

Soll, Jack B.; Klayman, Joshua

2004-01-01

Judges were asked to make numerical estimates (e.g., "In what year was the first flight of a hot air balloon?"). Judges provided high and low estimates such that they were X% sure that the correct answer lay between them. They exhibited substantial overconfidence: The correct answer fell inside their intervals much less than X% of the time. This…

Statistical inference for remote sensing-based estimates of net deforestation

Treesearch

Ronald E. McRoberts; Brian F. Walters

2012-01-01

Statistical inference requires expression of an estimate in probabilistic terms, usually in the form of a confidence interval. An approach to constructing confidence intervals for remote sensing-based estimates of net deforestation is illustrated. The approach is based on post-classification methods using two independent forest/non-forest classifications because...

Hospital ownership and financial performance: what explains the different findings in the empirical literature?

PubMed

Shen, Yu-Chu; Eggleston, Karen; Lau, Joseph; Schmid, Christopher H

2007-01-01

This study applies meta-analytic methods to conduct a quantitative review of the empirical literature on hospital ownership since 1990. We examine four financial outcomes across 40 studies: cost, revenue, profit margin, and efficiency. We find that variation in the magnitudes of ownership effects can be explained by a study's research focus and methodology. Studies using empirical methods that control for few confounding factors tend to find larger differences between for-profit and not-for-profit hospitals than studies that control for a wider range of confounding factors. Functional form and sample size also matter. Failure to apply log transformation to highly skewed expenditure data yields misleadingly large estimated differences between for-profits and not-for-profits. Studies with fewer than 200 observations also produce larger point estimates and wide confidence intervals.

Simple estimation procedures for regression analysis of interval-censored failure time data under the proportional hazards model.

PubMed

Sun, Jianguo; Feng, Yanqin; Zhao, Hui

2015-01-01

Interval-censored failure time data occur in many fields including epidemiological and medical studies as well as financial and sociological studies, and many authors have investigated their analysis (Sun, The statistical analysis of interval-censored failure time data, 2006; Zhang, Stat Modeling 9:321-343, 2009). In particular, a number of procedures have been developed for regression analysis of interval-censored data arising from the proportional hazards model (Finkelstein, Biometrics 42:845-854, 1986; Huang, Ann Stat 24:540-568, 1996; Pan, Biometrics 56:199-203, 2000). For most of these procedures, however, one drawback is that they involve estimation of both regression parameters and baseline cumulative hazard function. In this paper, we propose two simple estimation approaches that do not need estimation of the baseline cumulative hazard function. The asymptotic properties of the resulting estimates are given, and an extensive simulation study is conducted and indicates that they work well for practical situations.

Estimation of the Undiagnosed Intervals of HIV-Infected Individuals by a Modified Back-Calculation Method for Reconstructing the Epidemic Curves.

PubMed

Wong, Ngai Sze; Wong, Ka Hing; Lee, Man Po; Tsang, Owen T Y; Chan, Denise P C; Lee, Shui Shan

2016-01-01

Undiagnosed infections accounted for the hidden proportion of HIV cases that have escaped from public health surveillance. To assess the population risk of HIV transmission, we estimated the undiagnosed interval of each known infection for constructing the HIV incidence curves. We used modified back-calculation methods to estimate the seroconversion year for each diagnosed patient attending any one of the 3 HIV specialist clinics in Hong Kong. Three approaches were used, depending on the adequacy of CD4 data: (A) estimating one's pre-treatment CD4 depletion rate in multilevel model;(B) projecting one's seroconversion year by referencing seroconverters' CD4 depletion rate; or (C) projecting from the distribution of estimated undiagnosed intervals in (B). Factors associated with long undiagnosed interval (>2 years) were examined in univariate analyses. Epidemic curves constructed from estimated seroconversion data were evaluated by modes of transmission. Between 1991 and 2010, a total of 3695 adult HIV patients were diagnosed. The undiagnosed intervals were derived from method (A) (28%), (B) (61%) and (C) (11%) respectively. The intervals ranged from 0 to 10 years, and were shortened from 2001. Heterosexual infection, female, Chinese and age >64 at diagnosis were associated with long undiagnosed interval. Overall, the peaks of the new incidence curves were reached 4-6 years ahead of reported diagnoses, while their contours varied by mode of transmission. Characteristically, the epidemic growth of heterosexual male and female declined after 1998 with slight rebound in 2004-2006, but that of MSM continued to rise after 1998. By determining the time of seroconversion, HIV epidemic curves could be reconstructed from clinical data to better illustrate the trends of new infections. With the increasing coverage of antiretroviral therapy, the undiagnosed interval can add to the measures for assessing HIV transmission risk in the population.

Statistical models for estimating daily streamflow in Michigan

USGS Publications Warehouse

Holtschlag, D.J.; Salehi, Habib

1992-01-01

Statistical models for estimating daily streamflow were analyzed for 25 pairs of streamflow-gaging stations in Michigan. Stations were paired by randomly choosing a station operated in 1989 at which 10 or more years of continuous flow data had been collected and at which flow is virtually unregulated; a nearby station was chosen where flow characteristics are similar. Streamflow data from the 25 randomly selected stations were used as the response variables; streamflow data at the nearby stations were used to generate a set of explanatory variables. Ordinary-least squares regression (OLSR) equations, autoregressive integrated moving-average (ARIMA) equations, and transfer function-noise (TFN) equations were developed to estimate the log transform of flow for the 25 randomly selected stations. The precision of each type of equation was evaluated on the basis of the standard deviation of the estimation errors. OLSR equations produce one set of estimation errors; ARIMA and TFN models each produce l sets of estimation errors corresponding to the forecast lead. The lead-l forecast is the estimate of flow l days ahead of the most recent streamflow used as a response variable in the estimation. In this analysis, the standard deviation of lead l ARIMA and TFN forecast errors were generally lower than the standard deviation of OLSR errors for l < 2 days and l < 9 days, respectively. Composite estimates were computed as a weighted average of forecasts based on TFN equations and backcasts (forecasts of the reverse-ordered series) based on ARIMA equations. The standard deviation of composite errors varied throughout the length of the estimation interval and generally was at maximum near the center of the interval. For comparison with OLSR errors, the mean standard deviation of composite errors were computed for intervals of length 1 to 40 days. The mean standard deviation of length-l composite errors were generally less than the standard deviation of the OLSR errors for l < 32 days. In addition, the composite estimates ensure a gradual transition between periods of estimated and measured flows. Model performance among stations of differing model error magnitudes were compared by computing ratios of the mean standard deviation of the length l composite errors to the standard deviation of OLSR errors. The mean error ratio for the set of 25 selected stations was less than 1 for intervals l < 32 days. Considering the frequency characteristics of the length of intervals of estimated record in Michigan, the effective mean error ratio for intervals < 30 days was 0.52. Thus, for intervals of estimation of 1 month or less, the error of the composite estimate is substantially lower than error of the OLSR estimate.

On the selection of ordinary differential equation models with application to predator-prey dynamical models.

PubMed

Zhang, Xinyu; Cao, Jiguo; Carroll, Raymond J

2015-03-01

We consider model selection and estimation in a context where there are competing ordinary differential equation (ODE) models, and all the models are special cases of a "full" model. We propose a computationally inexpensive approach that employs statistical estimation of the full model, followed by a combination of a least squares approximation (LSA) and the adaptive Lasso. We show the resulting method, here called the LSA method, to be an (asymptotically) oracle model selection method. The finite sample performance of the proposed LSA method is investigated with Monte Carlo simulations, in which we examine the percentage of selecting true ODE models, the efficiency of the parameter estimation compared to simply using the full and true models, and coverage probabilities of the estimated confidence intervals for ODE parameters, all of which have satisfactory performances. Our method is also demonstrated by selecting the best predator-prey ODE to model a lynx and hare population dynamical system among some well-known and biologically interpretable ODE models. © 2014, The International Biometric Society.

Efficient Queries of Stand-off Annotations for Natural Language Processing on Electronic Medical Records.

PubMed

Luo, Yuan; Szolovits, Peter

2016-01-01

In natural language processing, stand-off annotation uses the starting and ending positions of an annotation to anchor it to the text and stores the annotation content separately from the text. We address the fundamental problem of efficiently storing stand-off annotations when applying natural language processing on narrative clinical notes in electronic medical records (EMRs) and efficiently retrieving such annotations that satisfy position constraints. Efficient storage and retrieval of stand-off annotations can facilitate tasks such as mapping unstructured text to electronic medical record ontologies. We first formulate this problem into the interval query problem, for which optimal query/update time is in general logarithm. We next perform a tight time complexity analysis on the basic interval tree query algorithm and show its nonoptimality when being applied to a collection of 13 query types from Allen's interval algebra. We then study two closely related state-of-the-art interval query algorithms, proposed query reformulations, and augmentations to the second algorithm. Our proposed algorithm achieves logarithmic time stabbing-max query time complexity and solves the stabbing-interval query tasks on all of Allen's relations in logarithmic time, attaining the theoretic lower bound. Updating time is kept logarithmic and the space requirement is kept linear at the same time. We also discuss interval management in external memory models and higher dimensions.

Efficient Queries of Stand-off Annotations for Natural Language Processing on Electronic Medical Records

PubMed Central

Luo, Yuan; Szolovits, Peter

2016-01-01

In natural language processing, stand-off annotation uses the starting and ending positions of an annotation to anchor it to the text and stores the annotation content separately from the text. We address the fundamental problem of efficiently storing stand-off annotations when applying natural language processing on narrative clinical notes in electronic medical records (EMRs) and efficiently retrieving such annotations that satisfy position constraints. Efficient storage and retrieval of stand-off annotations can facilitate tasks such as mapping unstructured text to electronic medical record ontologies. We first formulate this problem into the interval query problem, for which optimal query/update time is in general logarithm. We next perform a tight time complexity analysis on the basic interval tree query algorithm and show its nonoptimality when being applied to a collection of 13 query types from Allen’s interval algebra. We then study two closely related state-of-the-art interval query algorithms, proposed query reformulations, and augmentations to the second algorithm. Our proposed algorithm achieves logarithmic time stabbing-max query time complexity and solves the stabbing-interval query tasks on all of Allen’s relations in logarithmic time, attaining the theoretic lower bound. Updating time is kept logarithmic and the space requirement is kept linear at the same time. We also discuss interval management in external memory models and higher dimensions. PMID:27478379

New Estimates of Intergenerational Time Intervals for the Calculation of Age and Origins of Mutations

PubMed Central

Tremblay, Marc; Vézina, Hélène

2000-01-01

Summary Intergenerational time intervals are frequently used in human population-genetics studies concerned with the ages and origins of mutations. In most cases, mean intervals of 20 or 25 years are used, regardless of the demographic characteristics of the population under study. Although these characteristics may vary from prehistoric to historical times, we suggest that this value is probably too low, and that the ages of some mutations may have been underestimated. Analyses were performed by using the BALSAC Population Register (Quebec, Canada), from which several intergenerational comparisons can be made. Family reconstitutions were used to measure interval lengths and variations in descending lineages. Various parameters were considered, such as spouse age at marriage, parental age, and reproduction levels. Mother-child and father-child intervals were compared. Intergenerational male and female intervals were also analyzed in 100 extended ascending genealogies. Results showed that a mean value of 30 years is a better estimate of intergenerational intervals than 20 or 25 years. As marked differences between male and female interval length were observed, specific values are proposed for mtDNA, autosomal, X-chromosomal, and Y-chromosomal loci. The applicability of these results for age estimates of mutations is discussed. PMID:10677323

Set-theoretic estimation of hybrid system configurations.

PubMed

Benazera, Emmanuel; Travé-Massuyès, Louise

2009-10-01

Hybrid systems serve as a powerful modeling paradigm for representing complex continuous controlled systems that exhibit discrete switches in their dynamics. The system and the models of the system are nondeterministic due to operation in uncertain environment. Bayesian belief update approaches to stochastic hybrid system state estimation face a blow up in the number of state estimates. Therefore, most popular techniques try to maintain an approximation of the true belief state by either sampling or maintaining a limited number of trajectories. These limitations can be avoided by using bounded intervals to represent the state uncertainty. This alternative leads to splitting the continuous state space into a finite set of possibly overlapping geometrical regions that together with the system modes form configurations of the hybrid system. As a consequence, the true system state can be captured by a finite number of hybrid configurations. A set of dedicated algorithms that can efficiently compute these configurations is detailed. Results are presented on two systems of the hybrid system literature.

Residue analysis of fipronil and difenoconazole in okra by liquid chromatography tandem mass spectrometry and their food safety evaluation.

PubMed

Hingmire, Sandip; Oulkar, Dasharath P; Utture, Sagar C; Ahammed Shabeer, T P; Banerjee, Kaushik

2015-06-01

A liquid chromatography tandem mass spectrometry (LC-MS/MS) based method is reported for simultaneous analysis of fipronil (plus its metabolites) and difenoconazole residues in okra. The sample preparation method involving extraction with ethyl acetate provided 80-107% recoveries for both the pesticides with precision RSD within 4-17% estimated at the limits of quantification (LOQ, fipronil=1ngg(-1), difenoconazole=5ngg(-1)) and higher fortification levels. In field, both the pesticides dissipated with half-life of 2.5days. The estimated pre-harvest intervals (PHI) for fipronil and difenoconazole were 15 and 19.5days, and 4 and 6.5days at single and double dose of field applications, respectively. Decontamination of incurred residues by washing and different cooking treatments was quite efficient in minimizing the residue load of both the chemicals. Okra samples harvested after the estimated PHIs were found safe for human consumption. Copyright © 2014 Elsevier Ltd. All rights reserved.

Quantitative imaging biomarkers: Effect of sample size and bias on confidence interval coverage.

PubMed

Obuchowski, Nancy A; Bullen, Jennifer

2017-01-01

Introduction Quantitative imaging biomarkers (QIBs) are being increasingly used in medical practice and clinical trials. An essential first step in the adoption of a quantitative imaging biomarker is the characterization of its technical performance, i.e. precision and bias, through one or more performance studies. Then, given the technical performance, a confidence interval for a new patient's true biomarker value can be constructed. Estimating bias and precision can be problematic because rarely are both estimated in the same study, precision studies are usually quite small, and bias cannot be measured when there is no reference standard. Methods A Monte Carlo simulation study was conducted to assess factors affecting nominal coverage of confidence intervals for a new patient's quantitative imaging biomarker measurement and for change in the quantitative imaging biomarker over time. Factors considered include sample size for estimating bias and precision, effect of fixed and non-proportional bias, clustered data, and absence of a reference standard. Results Technical performance studies of a quantitative imaging biomarker should include at least 35 test-retest subjects to estimate precision and 65 cases to estimate bias. Confidence intervals for a new patient's quantitative imaging biomarker measurement constructed under the no-bias assumption provide nominal coverage as long as the fixed bias is <12%. For confidence intervals of the true change over time, linearity must hold and the slope of the regression of the measurements vs. true values should be between 0.95 and 1.05. The regression slope can be assessed adequately as long as fixed multiples of the measurand can be generated. Even small non-proportional bias greatly reduces confidence interval coverage. Multiple lesions in the same subject can be treated as independent when estimating precision. Conclusion Technical performance studies of quantitative imaging biomarkers require moderate sample sizes in order to provide robust estimates of bias and precision for constructing confidence intervals for new patients. Assumptions of linearity and non-proportional bias should be assessed thoroughly.

Analysis of data collected from right and left limbs: Accounting for dependence and improving statistical efficiency in musculoskeletal research.

PubMed

Stewart, Sarah; Pearson, Janet; Rome, Keith; Dalbeth, Nicola; Vandal, Alain C

2018-01-01

Statistical techniques currently used in musculoskeletal research often inefficiently account for paired-limb measurements or the relationship between measurements taken from multiple regions within limbs. This study compared three commonly used analysis methods with a mixed-models approach that appropriately accounted for the association between limbs, regions, and trials and that utilised all information available from repeated trials. Four analysis were applied to an existing data set containing plantar pressure data, which was collected for seven masked regions on right and left feet, over three trials, across three participant groups. Methods 1-3 averaged data over trials and analysed right foot data (Method 1), data from a randomly selected foot (Method 2), and averaged right and left foot data (Method 3). Method 4 used all available data in a mixed-effects regression that accounted for repeated measures taken for each foot, foot region and trial. Confidence interval widths for the mean differences between groups for each foot region were used as a criterion for comparison of statistical efficiency. Mean differences in pressure between groups were similar across methods for each foot region, while the confidence interval widths were consistently smaller for Method 4. Method 4 also revealed significant between-group differences that were not detected by Methods 1-3. A mixed effects linear model approach generates improved efficiency and power by producing more precise estimates compared to alternative approaches that discard information in the process of accounting for paired-limb measurements. This approach is recommended in generating more clinically sound and statistically efficient research outputs. Copyright © 2017 Elsevier B.V. All rights reserved.

Low-flow characteristics of streams in Virginia

USGS Publications Warehouse

Hayes, Donald C.

1991-01-01

Streamflow data were collected and low-flow characteristics computed for 715 gaged sites in Virginia Annual minimum average 7-consecutive-day flows range from 0 to 2,195 cubic feet per second for a 2-year recurrence interval and from 0 to 1,423 cubic feet per second for a 10-year recurrence interval. Drainage areas range from 0.17 to 7,320 square miles. Existing and discontinued gaged sites are separated into three types: long-term continuous-record sites, short-term continuous-record sites, and partial-record sites. Low-flow characteristics for long-term continuous-record sites are determined from frequency curves of annual minimum average 7-consecutive-day flows . Low-flow characteristics for short-term continuous-record sites are estimated by relating daily mean base-flow discharge values at a short-term site to concurrent daily mean discharge values at nearby long-term continuous-record sites having similar basin characteristics . Low-flow characteristics for partial-record sites are estimated by relating base-flow measurements to daily mean discharge values at long-term continuous-record sites. Information from the continuous-record sites and partial-record sites in Virginia are used to develop two techniques for estimating low-flow characteristics at ungaged sites. A flow-routing method is developed to estimate low-flow values at ungaged sites on gaged streams. Regional regression equations are developed for estimating low-flow values at ungaged sites on ungaged streams. The flow-routing method consists of transferring low-flow characteristics from a gaged site, either upstream or downstream, to a desired ungaged site. A simple drainage-area proration is used to transfer values when there are no major tributaries between the gaged and ungaged sites. Standard errors of estimate for108 test sites are 19 percent of the mean for estimates of low-flow characteristics having a 2-year recurrence interval and 52 percent of the mean for estimates of low-flow characteristics having a 10-year recurrence interval . A more complex transfer method must be used when major tributaries enter the stream between the gaged and ungaged sites. Twenty-four stream networks are analyzed, and predictions are made for 84 sites. Standard errors of estimate are 15 percent of the mean for estimates of low-flow characteristics having a 2-year recurrence interval and 22 percent of the mean for estimates of low-flow characteristics having a 10-year recurrence interval. Regional regression equations were developed for estimating low-flow values at ungaged sites on ungaged streams. The State was divided into eight regions on the basis of physiography and geographic grouping of the residuals computed in regression analyses . Basin characteristics that were significant in the regression analysis were drainage area, rock type, and strip-mined area. Standard errors of prediction range from 60 to139 percent for estimates of low-flow characteristics having a 2-year recurrence interval and 90 percent to 172 percent for estimates of low-flow characteristics having a 10-year recurrence interval.

Trade-offs between effectiveness and efficiency in stroke rehabilitation.

PubMed

Koh, Gerald Choon-Huat; Chen, Cynthia; Cheong, Angela; Choo, Tai Bee; Pui, Choi Kwok; Phoon, Fong Ngan; Ming, Chan Kin; Yeow, Tan Boon; Petrella, Robert; Thind, Amardeep; Koh, David; Seng, Chia Kee

2012-12-01

Most stroke research has studied rehabilitation effectiveness and rehabilitation efficiency separately and not investigated the potential trade-offs between these two indices of rehabilitation. To determine whether there is a trade-off between independent factors of rehabilitation effectiveness and rehabilitation efficiency. Using a retrospective cohort study design, we studied all stroke patients (n = 2810) from two sub-acute rehabilitation hospitals from 1996 to 2005, representing 87·5% of national bed-years during the same period. Independent predictors of poorer rehabilitation effectiveness and log rehabilitation efficiency were • older age • race-ethnicity • caregiver availability • ischemic stroke • longer time to admission • dementia • admission Barthel Index score, and • length of stay. Rehabilitation effectiveness was lower in females, and the gender differences were significantly lower in those aged ≤70 years (β -4·7 (95% confidence interval -7·4 to -2·0)). There were trade-offs between effectiveness and efficiency with respect to admission Barthel Index score and length of stay. An increase of 10 in admission Barthel Index score predicted an increase of 3·6% (95% confidence interval 3·2-4·0) in effectiveness but a decrease of 0·04 (95% confidence interval -0·05 to -0·02) in log efficiency (a reduction of efficiency by 1·0 per 30 days). An increase in log length of stay by 1 (length of stay of 2·7 days) predicted an increase of 8·0% (95% confidence interval 5·7-10·3) in effectiveness but a decrease of 0·82 (95% confidence interval -0·90 to -0·74) in log efficiency (equivalent to a reduction in efficiency by 2·3 per 30 days). For optimal rehabilitation effectiveness and rehabilitation efficiency, the admission Barthel Index score was 30-62 and length of stay was 37-41 days. There are trade-offs between effectiveness and efficiency during inpatient sub-acute stroke rehabilitation with respect to admission functional status and length of stay. © 2011 The Authors. International Journal of Stroke © 2011 World Stroke Organization.

Quantifying Contaminant Mass for the Feasibility Study of the DuPont Chambers Works FUSRAP Site - 13510

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, Carl; Rahman, Mahmudur; Johnson, Ann

2013-07-01

The U.S. Army Corps of Engineers (USACE) - Philadelphia District is conducting an environmental restoration at the DuPont Chambers Works in Deepwater, New Jersey under the Formerly Utilized Sites Remedial Action Program (FUSRAP). Discrete locations are contaminated with natural uranium, thorium-230 and radium-226. The USACE is proposing a preferred remedial alternative consisting of excavation and offsite disposal to address soil contamination followed by monitored natural attenuation to address residual groundwater contamination. Methods were developed to quantify the error associated with contaminant volume estimates and use mass balance calculations of the uranium plume to estimate the removal efficiency of the proposedmore » alternative. During the remedial investigation, the USACE collected approximately 500 soil samples at various depths. As the first step of contaminant mass estimation, soil analytical data was segmented into several depth intervals. Second, using contouring software, analytical data for each depth interval was contoured to determine lateral extent of contamination. Six different contouring algorithms were used to generate alternative interpretations of the lateral extent of the soil contamination. Finally, geographical information system software was used to produce a three dimensional model in order to present both lateral and vertical extent of the soil contamination and to estimate the volume of impacted soil for each depth interval. The average soil volume from all six contouring methods was used to determine the estimated volume of impacted soil. This method also allowed an estimate of a standard deviation of the waste volume estimate. It was determined that the margin of error for the method was plus or minus 17% of the waste volume, which is within the acceptable construction contingency for cost estimation. USACE collected approximately 190 groundwater samples from 40 monitor wells. It is expected that excavation and disposal of contaminated soil will remove the contaminant source zone and significantly reduce contaminant concentrations in groundwater. To test this assumption, a mass balance evaluation was performed to estimate the amount of dissolved uranium that would remain in the groundwater after completion of soil excavation. As part of this evaluation, average groundwater concentrations for the pre-excavation and post-excavation aquifer plume area were calculated to determine the percentage of plume removed during excavation activities. In addition, the volume of the plume removed during excavation dewatering was estimated. The results of the evaluation show that approximately 98% of the aqueous uranium would be removed during the excavation phase. The USACE expects that residual levels of contamination will remain in groundwater after excavation of soil but at levels well suited for the selection of excavation combined with monitored natural attenuation as a preferred alternative. (authors)« less

Estimates of genetic and environmental (co)variances for first lactation on milk yield, survival, and calving interval.

PubMed

Dong, M C; van Vleck, L D

1989-03-01

Variance and covariance components for milk yield, survival to second freshening, calving interval in first lactation were estimated by REML with the expectation and maximization algorithm for an animal model which included herd-year-season effects. Cows without calving interval but with milk yield were included. Each of the four data sets of 15 herds included about 3000 Holstein cows. Relationships across herds were ignored to enable inversion of the coefficient matrix of mixed model equations. Quadratics and their expectations were accumulated herd by herd. Heritability of milk yield (.32) agrees with reports by same methods. Heritabilities of survival (.11) and calving interval(.15) are slightly larger and genetic correlations smaller than results from different methods of estimation. Genetic correlation between milk yield and calving interval (.09) indicates genetic ability to produce more milk is lightly associated with decreased fertility.

Plasma-laser ion discrimination by TOF technique applied to coupled SiC detectors.

NASA Astrophysics Data System (ADS)

Cavallaro, Salvatore

2018-01-01

The rate estimation of nuclear reactions induced in high intensity laser-target interaction (≥1016 W/cm2), is strongly depending on the neutron detection efficiency and ion charge discrimination, according to particles involved in exit open-channels. Ion discrimination is basically performed by means of analysis of pits observed on track detector, which is critically dependent on calibration and/or fast TOF devices based on SiC and diamond detectors. Last setup is used to determine the ion energy and to obtain a rough estimation of yields. However, for each TOF interval, the dependence of yield from the energy deposited in the detector sensitive region, introduces a distortion in the ion spectra. Moreover, if two ion species are present in the same spectrum, the discrimination of their contribution is not attainable. In this paper a new method is described which allows to discriminate the contribution of two ion species in the wide energy range of nuclear reactions induced in laser-target interactions. The method is based on charge response of two TOF-SiC detectors, of suitable thicknesses, placed in adjacent positions. In presence of two ion species, the response of the detectors, associated with different energy losses, can determine the ion specific contribution to each TOF interval.

Polynomials to model the growth of young bulls in performance tests.

PubMed

Scalez, D C B; Fragomeni, B O; Passafaro, T L; Pereira, I G; Toral, F L B

2014-03-01

The use of polynomial functions to describe the average growth trajectory and covariance functions of Nellore and MA (21/32 Charolais+11/32 Nellore) young bulls in performance tests was studied. The average growth trajectories and additive genetic and permanent environmental covariance functions were fit with Legendre (linear through quintic) and quadratic B-spline (with two to four intervals) polynomials. In general, the Legendre and quadratic B-spline models that included more covariance parameters provided a better fit with the data. When comparing models with the same number of parameters, the quadratic B-spline provided a better fit than the Legendre polynomials. The quadratic B-spline with four intervals provided the best fit for the Nellore and MA groups. The fitting of random regression models with different types of polynomials (Legendre polynomials or B-spline) affected neither the genetic parameters estimates nor the ranking of the Nellore young bulls. However, fitting different type of polynomials affected the genetic parameters estimates and the ranking of the MA young bulls. Parsimonious Legendre or quadratic B-spline models could be used for genetic evaluation of body weight of Nellore young bulls in performance tests, whereas these parsimonious models were less efficient for animals of the MA genetic group owing to limited data at the extreme ages.

Bootstrap confidence intervals and bias correction in the estimation of HIV incidence from surveillance data with testing for recent infection.

PubMed

Carnegie, Nicole Bohme

2011-04-15

The incidence of new infections is a key measure of the status of the HIV epidemic, but accurate measurement of incidence is often constrained by limited data. Karon et al. (Statist. Med. 2008; 27:4617–4633) developed a model to estimate the incidence of HIV infection from surveillance data with biologic testing for recent infection for newly diagnosed cases. This method has been implemented by public health departments across the United States and is behind the new national incidence estimates, which are about 40 per cent higher than previous estimates. We show that the delta method approximation given for the variance of the estimator is incomplete, leading to an inflated variance estimate. This contributes to the generation of overly conservative confidence intervals, potentially obscuring important differences between populations. We demonstrate via simulation that an innovative model-based bootstrap method using the specified model for the infection and surveillance process improves confidence interval coverage and adjusts for the bias in the point estimate. Confidence interval coverage is about 94–97 per cent after correction, compared with 96–99 per cent before. The simulated bias in the estimate of incidence ranges from −6.3 to +14.6 per cent under the original model but is consistently under 1 per cent after correction by the model-based bootstrap. In an application to data from King County, Washington in 2007 we observe correction of 7.2 per cent relative bias in the incidence estimate and a 66 per cent reduction in the width of the 95 per cent confidence interval using this method. We provide open-source software to implement the method that can also be extended for alternate models.

Modeling water quality in an urban river using hydrological factors--data driven approaches.

PubMed

Chang, Fi-John; Tsai, Yu-Hsuan; Chen, Pin-An; Coynel, Alexandra; Vachaud, Georges

2015-03-15

Contrasting seasonal variations occur in river flow and water quality as a result of short duration, severe intensity storms and typhoons in Taiwan. Sudden changes in river flow caused by impending extreme events may impose serious degradation on river water quality and fateful impacts on ecosystems. Water quality is measured in a monthly/quarterly scale, and therefore an estimation of water quality in a daily scale would be of good help for timely river pollution management. This study proposes a systematic analysis scheme (SAS) to assess the spatio-temporal interrelation of water quality in an urban river and construct water quality estimation models using two static and one dynamic artificial neural networks (ANNs) coupled with the Gamma test (GT) based on water quality, hydrological and economic data. The Dahan River basin in Taiwan is the study area. Ammonia nitrogen (NH3-N) is considered as the representative parameter, a correlative indicator in judging the contamination level over the study. Key factors the most closely related to the representative parameter (NH3-N) are extracted by the Gamma test for modeling NH3-N concentration, and as a result, four hydrological factors (discharge, days w/o discharge, water temperature and rainfall) are identified as model inputs. The modeling results demonstrate that the nonlinear autoregressive with exogenous input (NARX) network furnished with recurrent connections can accurately estimate NH3-N concentration with a very high coefficient of efficiency value (0.926) and a low RMSE value (0.386 mg/l). Besides, the NARX network can suitably catch peak values that mainly occur in dry periods (September-April in the study area), which is particularly important to water pollution treatment. The proposed SAS suggests a promising approach to reliably modeling the spatio-temporal NH3-N concentration based solely on hydrological data, without using water quality sampling data. It is worth noticing that such estimation can be made in a much shorter time interval of interest (span from a monthly scale to a daily scale) because hydrological data are long-term collected in a daily scale. The proposed SAS favorably makes NH3-N concentration estimation much easier (with only hydrological field sampling) and more efficient (in shorter time intervals), which can substantially help river managers interpret and estimate water quality responses to natural and/or manmade pollution in a more effective and timely way for river pollution management. Copyright © 2014 Elsevier Ltd. All rights reserved.

Estimating background-subtracted fluorescence transients in calcium imaging experiments: a quantitative approach.

PubMed

Joucla, Sébastien; Franconville, Romain; Pippow, Andreas; Kloppenburg, Peter; Pouzat, Christophe

2013-08-01

Calcium imaging has become a routine technique in neuroscience for subcellular to network level investigations. The fast progresses in the development of new indicators and imaging techniques call for dedicated reliable analysis methods. In particular, efficient and quantitative background fluorescence subtraction routines would be beneficial to most of the calcium imaging research field. A background-subtracted fluorescence transients estimation method that does not require any independent background measurement is therefore developed. This method is based on a fluorescence model fitted to single-trial data using a classical nonlinear regression approach. The model includes an appropriate probabilistic description of the acquisition system's noise leading to accurate confidence intervals on all quantities of interest (background fluorescence, normalized background-subtracted fluorescence time course) when background fluorescence is homogeneous. An automatic procedure detecting background inhomogeneities inside the region of interest is also developed and is shown to be efficient on simulated data. The implementation and performances of the proposed method on experimental recordings from the mouse hypothalamus are presented in details. This method, which applies to both single-cell and bulk-stained tissues recordings, should help improving the statistical comparison of fluorescence calcium signals between experiments and studies. Copyright © 2013 Elsevier Ltd. All rights reserved.

Binary Interval Search: a scalable algorithm for counting interval intersections

PubMed Central

Layer, Ryan M.; Skadron, Kevin; Robins, Gabriel; Hall, Ira M.; Quinlan, Aaron R.

2013-01-01

Motivation: The comparison of diverse genomic datasets is fundamental to understand genome biology. Researchers must explore many large datasets of genome intervals (e.g. genes, sequence alignments) to place their experimental results in a broader context and to make new discoveries. Relationships between genomic datasets are typically measured by identifying intervals that intersect, that is, they overlap and thus share a common genome interval. Given the continued advances in DNA sequencing technologies, efficient methods for measuring statistically significant relationships between many sets of genomic features are crucial for future discovery. Results: We introduce the Binary Interval Search (BITS) algorithm, a novel and scalable approach to interval set intersection. We demonstrate that BITS outperforms existing methods at counting interval intersections. Moreover, we show that BITS is intrinsically suited to parallel computing architectures, such as graphics processing units by illustrating its utility for efficient Monte Carlo simulations measuring the significance of relationships between sets of genomic intervals. Availability: https://github.com/arq5x/bits. Contact: arq5x@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23129298

The Applicability of Confidence Intervals of Quantiles for the Generalized Logistic Distribution

NASA Astrophysics Data System (ADS)

Shin, H.; Heo, J.; Kim, T.; Jung, Y.

2007-12-01

The generalized logistic (GL) distribution has been widely used for frequency analysis. However, there is a little study related to the confidence intervals that indicate the prediction accuracy of distribution for the GL distribution. In this paper, the estimation of the confidence intervals of quantiles for the GL distribution is presented based on the method of moments (MOM), maximum likelihood (ML), and probability weighted moments (PWM) and the asymptotic variances of each quantile estimator are derived as functions of the sample sizes, return periods, and parameters. Monte Carlo simulation experiments are also performed to verify the applicability of the derived confidence intervals of quantile. As the results, the relative bias (RBIAS) and relative root mean square error (RRMSE) of the confidence intervals generally increase as return period increases and reverse as sample size increases. And PWM for estimating the confidence intervals performs better than the other methods in terms of RRMSE when the data is almost symmetric while ML shows the smallest RBIAS and RRMSE when the data is more skewed and sample size is moderately large. The GL model was applied to fit the distribution of annual maximum rainfall data. The results show that there are little differences in the estimated quantiles between ML and PWM while distinct differences in MOM.

More accurate, calibrated bootstrap confidence intervals for correlating two autocorrelated climate time series

NASA Astrophysics Data System (ADS)

Olafsdottir, Kristin B.; Mudelsee, Manfred

2013-04-01

Estimation of the Pearson's correlation coefficient between two time series to evaluate the influences of one time depended variable on another is one of the most often used statistical method in climate sciences. Various methods are used to estimate confidence interval to support the correlation point estimate. Many of them make strong mathematical assumptions regarding distributional shape and serial correlation, which are rarely met. More robust statistical methods are needed to increase the accuracy of the confidence intervals. Bootstrap confidence intervals are estimated in the Fortran 90 program PearsonT (Mudelsee, 2003), where the main intention was to get an accurate confidence interval for correlation coefficient between two time series by taking the serial dependence of the process that generated the data into account. However, Monte Carlo experiments show that the coverage accuracy for smaller data sizes can be improved. Here we adapt the PearsonT program into a new version called PearsonT3, by calibrating the confidence interval to increase the coverage accuracy. Calibration is a bootstrap resampling technique, which basically performs a second bootstrap loop or resamples from the bootstrap resamples. It offers, like the non-calibrated bootstrap confidence intervals, robustness against the data distribution. Pairwise moving block bootstrap is used to preserve the serial correlation of both time series. The calibration is applied to standard error based bootstrap Student's t confidence intervals. The performances of the calibrated confidence intervals are examined with Monte Carlo simulations, and compared with the performances of confidence intervals without calibration, that is, PearsonT. The coverage accuracy is evidently better for the calibrated confidence intervals where the coverage error is acceptably small (i.e., within a few percentage points) already for data sizes as small as 20. One form of climate time series is output from numerical models which simulate the climate system. The method is applied to model data from the high resolution ocean model, INALT01 where the relationship between the Agulhas Leakage and the North Brazil Current is evaluated. Preliminary results show significant correlation between the two variables when there is 10 year lag between them, which is more or less the time that takes the Agulhas Leakage water to reach the North Brazil Current. Mudelsee, M., 2003. Estimating Pearson's correlation coefficient with bootstrap confidence interval from serially dependent time series. Mathematical Geology 35, 651-665.

Estimation of the lower and upper bounds on the probability of failure using subset simulation and random set theory

NASA Astrophysics Data System (ADS)

Alvarez, Diego A.; Uribe, Felipe; Hurtado, Jorge E.

2018-02-01

Random set theory is a general framework which comprises uncertainty in the form of probability boxes, possibility distributions, cumulative distribution functions, Dempster-Shafer structures or intervals; in addition, the dependence between the input variables can be expressed using copulas. In this paper, the lower and upper bounds on the probability of failure are calculated by means of random set theory. In order to accelerate the calculation, a well-known and efficient probability-based reliability method known as subset simulation is employed. This method is especially useful for finding small failure probabilities in both low- and high-dimensional spaces, disjoint failure domains and nonlinear limit state functions. The proposed methodology represents a drastic reduction of the computational labor implied by plain Monte Carlo simulation for problems defined with a mixture of representations for the input variables, while delivering similar results. Numerical examples illustrate the efficiency of the proposed approach.

Optimal estimation of suspended-sediment concentrations in streams

USGS Publications Warehouse

Holtschlag, D.J.

2001-01-01

Optimal estimators are developed for computation of suspended-sediment concentrations in streams. The estimators are a function of parameters, computed by use of generalized least squares, which simultaneously account for effects of streamflow, seasonal variations in average sediment concentrations, a dynamic error component, and the uncertainty in concentration measurements. The parameters are used in a Kalman filter for on-line estimation and an associated smoother for off-line estimation of suspended-sediment concentrations. The accuracies of the optimal estimators are compared with alternative time-averaging interpolators and flow-weighting regression estimators by use of long-term daily-mean suspended-sediment concentration and streamflow data from 10 sites within the United States. For sampling intervals from 3 to 48 days, the standard errors of on-line and off-line optimal estimators ranged from 52.7 to 107%, and from 39.5 to 93.0%, respectively. The corresponding standard errors of linear and cubic-spline interpolators ranged from 48.8 to 158%, and from 50.6 to 176%, respectively. The standard errors of simple and multiple regression estimators, which did not vary with the sampling interval, were 124 and 105%, respectively. Thus, the optimal off-line estimator (Kalman smoother) had the lowest error characteristics of those evaluated. Because suspended-sediment concentrations are typically measured at less than 3-day intervals, use of optimal estimators will likely result in significant improvements in the accuracy of continuous suspended-sediment concentration records. Additional research on the integration of direct suspended-sediment concentration measurements and optimal estimators applied at hourly or shorter intervals is needed.

Estimating length of avian incubation and nestling stages in afrotropical forest birds from interval-censored nest records

USGS Publications Warehouse

Stanley, T.R.; Newmark, W.D.

2010-01-01

In the East Usambara Mountains in northeast Tanzania, research on the effects of forest fragmentation and disturbance on nest survival in understory birds resulted in the accumulation of 1,002 nest records between 2003 and 2008 for 8 poorly studied species. Because information on the length of the incubation and nestling stages in these species is nonexistent or sparse, our objectives in this study were (1) to estimate the length of the incubation and nestling stage and (2) to compute nest survival using these estimates in combination with calculated daily survival probability. Because our data were interval censored, we developed and applied two new statistical methods to estimate stage length. In the 8 species studied, the incubation stage lasted 9.6-21.8 days and the nestling stage 13.9-21.2 days. Combining these results with estimates of daily survival probability, we found that nest survival ranged from 6.0% to 12.5%. We conclude that our methodology for estimating stage lengths from interval-censored nest records is a reasonable and practical approach in the presence of interval-censored data. ?? 2010 The American Ornithologists' Union.

Cognitive timing: neuropsychology and anatomic basis.

PubMed

Coslett, H Branch; Shenton, Jeff; Dyer, Tamarah; Wiener, Martin

2009-02-13

We report data from 31 subjects with focal hemisphere lesions (15 left hemisphere) as well as 16 normal controls on a battery of tasks assessing the estimation, production and reproduction of time intervals ranging from 2-12 s. Both visual and auditory stimuli were employed for the estimation and production tasks. First, ANOVAs were performed to assess the effect of stimulus modality on estimation and production tasks; a significant effect of stimulus modality was observed for the production but not the estimation task. Second, accuracy was significantly different for the 2 s interval as compared to longer intervals. Subsequent analyses of the data from 4-12 s stimuli demonstrated that patients with brain lesions were more variable than controls on the estimation and reproduction tasks. Additionally, patients with brain lesions but not controls exhibited significant differences in performance on the different tasks; patients with brain lesions under-produced but over-estimated time intervals of 4-12 s but performed relatively well on the reproduction task, a pattern of performance consistent with a "fast clock". There was a significant correlation between impaired performance and lesions of the parietal lobe but there was no effect of laterality of lesion or correlation between lateral frontal lobe lesions and impairment on any task.

A note on windowing for the waveform relaxation

NASA Technical Reports Server (NTRS)

Zhang, Hong

1994-01-01

The technique of windowing has been often used in the implementation of the waveform relaxations for solving ODE's or time dependent PDE's. Its efficiency depends upon problem stiffness and operator splitting. Using model problems, the estimates for window length and convergence rate are derived. The electiveness of windowing is then investigated for non-stiff and stiff cases respectively. lt concludes that for the former, windowing is highly recommended when a large discrepancy exists between the convergence rate on a time interval and the ones on its subintervals. For the latter, windowing does not provide any computational advantage if machine features are disregarded. The discussion is supported by experimental results.

The influence of sampling interval on the accuracy of trail impact assessment

USGS Publications Warehouse

Leung, Y.-F.; Marion, J.L.

1999-01-01

Trail impact assessment and monitoring (IA&M) programs have been growing in importance and application in recreation resource management at protected areas. Census-based and sampling-based approaches have been developed in such programs, with systematic point sampling being the most common survey design. This paper examines the influence of sampling interval on the accuracy of estimates for selected trail impact problems. A complete census of four impact types on 70 trails in Great Smoky Mountains National Park was utilized as the base data set for the analyses. The census data were resampled at increasing intervals to create a series of simulated point data sets. Estimates of frequency of occurrence and lineal extent for the four impact types were compared with the census data set. The responses of accuracy loss on lineal extent estimates to increasing sampling intervals varied across different impact types, while the responses on frequency of occurrence estimates were consistent, approximating an inverse asymptotic curve. These findings suggest that systematic point sampling may be an appropriate method for estimating the lineal extent but not the frequency of trail impacts. Sample intervals of less than 100 m appear to yield an excellent level of accuracy for the four impact types evaluated. Multiple regression analysis results suggest that appropriate sampling intervals are more likely to be determined by the type of impact in question rather than the length of trail. The census-based trail survey and the resampling-simulation method developed in this study can be a valuable first step in establishing long-term trail IA&M programs, in which an optimal sampling interval range with acceptable accuracy is determined before investing efforts in data collection.

Estimation of reference intervals from small samples: an example using canine plasma creatinine.

PubMed

Geffré, A; Braun, J P; Trumel, C; Concordet, D

2009-12-01

According to international recommendations, reference intervals should be determined from at least 120 reference individuals, which often are impossible to achieve in veterinary clinical pathology, especially for wild animals. When only a small number of reference subjects is available, the possible bias cannot be known and the normality of the distribution cannot be evaluated. A comparison of reference intervals estimated by different methods could be helpful. The purpose of this study was to compare reference limits determined from a large set of canine plasma creatinine reference values, and large subsets of this data, with estimates obtained from small samples selected randomly. Twenty sets each of 120 and 27 samples were randomly selected from a set of 1439 plasma creatinine results obtained from healthy dogs in another study. Reference intervals for the whole sample and for the large samples were determined by a nonparametric method. The estimated reference limits for the small samples were minimum and maximum, mean +/- 2 SD of native and Box-Cox-transformed values, 2.5th and 97.5th percentiles by a robust method on native and Box-Cox-transformed values, and estimates from diagrams of cumulative distribution functions. The whole sample had a heavily skewed distribution, which approached Gaussian after Box-Cox transformation. The reference limits estimated from small samples were highly variable. The closest estimates to the 1439-result reference interval for 27-result subsamples were obtained by both parametric and robust methods after Box-Cox transformation but were grossly erroneous in some cases. For small samples, it is recommended that all values be reported graphically in a dot plot or histogram and that estimates of the reference limits be compared using different methods.

Association between GFR Estimated by Multiple Methods at Dialysis Commencement and Patient Survival

PubMed Central

Wong, Muh Geot; Pollock, Carol A.; Cooper, Bruce A.; Branley, Pauline; Collins, John F.; Craig, Jonathan C.; Kesselhut, Joan; Luxton, Grant; Pilmore, Andrew; Harris, David C.

2014-01-01

Summary Background and objectives The Initiating Dialysis Early and Late study showed that planned early or late initiation of dialysis, based on the Cockcroft and Gault estimation of GFR, was associated with identical clinical outcomes. This study examined the association of all-cause mortality with estimated GFR at dialysis commencement, which was determined using multiple formulas. Design, setting, participants, & measurements Initiating Dialysis Early and Late trial participants were stratified into tertiles according to the estimated GFR measured by Cockcroft and Gault, Modification of Diet in Renal Disease, or Chronic Kidney Disease-Epidemiology Collaboration formula at dialysis commencement. Patient survival was determined using multivariable Cox proportional hazards model regression. Results Only Initiating Dialysis Early and Late trial participants who commenced on dialysis were included in this study (n=768). A total of 275 patients died during the study. After adjustment for age, sex, racial origin, body mass index, diabetes, and cardiovascular disease, no significant differences in survival were observed between estimated GFR tertiles determined by Cockcroft and Gault (lowest tertile adjusted hazard ratio, 1.11; 95% confidence interval, 0.82 to 1.49; middle tertile hazard ratio, 1.29; 95% confidence interval, 0.96 to 1.74; highest tertile reference), Modification of Diet in Renal Disease (lowest tertile hazard ratio, 0.88; 95% confidence interval, 0.63 to 1.24; middle tertile hazard ratio, 1.20; 95% confidence interval, 0.90 to 1.61; highest tertile reference), and Chronic Kidney Disease-Epidemiology Collaboration equations (lowest tertile hazard ratio, 0.93; 95% confidence interval, 0.67 to 1.27; middle tertile hazard ratio, 1.15; 95% confidence interval, 0.86 to 1.54; highest tertile reference). Conclusion Estimated GFR at dialysis commencement was not significantly associated with patient survival, regardless of the formula used. However, a clinically important association cannot be excluded, because observed confidence intervals were wide. PMID:24178976

Commentary on Holmes et al. (2007): resolving the debate on when extinction risk is predictable.

PubMed

Ellner, Stephen P; Holmes, Elizabeth E

2008-08-01

We reconcile the findings of Holmes et al. (Ecology Letters, 10, 2007, 1182) that 95% confidence intervals for quasi-extinction risk were narrow for many vertebrates of conservation concern, with previous theory predicting wide confidence intervals. We extend previous theory, concerning the precision of quasi-extinction estimates as a function of population dynamic parameters, prediction intervals and quasi-extinction thresholds, and provide an approximation that specifies the prediction interval and threshold combinations where quasi-extinction estimates are precise (vs. imprecise). This allows PVA practitioners to define the prediction interval and threshold regions of safety (low risk with high confidence), danger (high risk with high confidence), and uncertainty.

Estimation of fecundability from survey data.

PubMed

Goldman, N; Westoff, C F; Paul, L E

1985-01-01

The estimation of fecundability from survey data is plagued by methodological problems such as misreporting of dates of birth and marriage and the occurrence of premarital exposure to the risk of conception. Nevertheless, estimates of fecundability from World Fertility Survey data for women married in recent years appear to be plausible for most of the surveys analyzed here and are quite consistent with estimates reported in earlier studies. The estimates presented in this article are all derived from the first interval, the interval between marriage or consensual union and the first live birth conception.

A Role for Memory in Prospective Timing informs Timing in Prospective Memory

PubMed Central

Waldum, Emily R; Sahakyan, Lili

2014-01-01

Time-based prospective memory (TBPM) tasks require the estimation of time in passing – known as prospective timing. Prospective timing is said to depend on an attentionally-driven internal clock mechanism, and is thought to be unaffected by memory for interval information (for reviews see, Block, Hancock, & Zakay, 2010; Block & Zakay, 1997). A prospective timing task that required a verbal estimate following the entire interval (Experiment 1) and a TBPM task that required production of a target response during the interval (Experiment 2) were used to test an alternative view that episodic memory does influence prospective timing. In both experiments, participants performed an ongoing lexical decision task of fixed duration while a varying number of songs were played in the background. Experiment 1 results revealed that verbal time estimates became longer the more songs participants remembered from the interval, suggesting that memory for interval information influences prospective time estimates. In Experiment 2, participants who were asked to perform the TBPM task without the aid of an external clock made their target responses earlier as the number of songs increased, indicating that prospective estimates of elapsed time increased as more songs were experienced. For participants who had access to a clock, changes in clock-checking coincided with the occurrence of song boundaries, indicating that participants used both song information and clock information to estimate time. Finally, ongoing task performance and verbal reports in both experiments further substantiate a role for episodic memory in prospective timing. PMID:22984950

Confidence Intervals for the Probability of Superiority Effect Size Measure and the Area under a Receiver Operating Characteristic Curve

ERIC Educational Resources Information Center

Ruscio, John; Mullen, Tara

2012-01-01

It is good scientific practice to the report an appropriate estimate of effect size and a confidence interval (CI) to indicate the precision with which a population effect was estimated. For comparisons of 2 independent groups, a probability-based effect size estimator (A) that is equal to the area under a receiver operating characteristic curve…

Coefficient Alpha Bootstrap Confidence Interval under Nonnormality

ERIC Educational Resources Information Center

Padilla, Miguel A.; Divers, Jasmin; Newton, Matthew

2012-01-01

Three different bootstrap methods for estimating confidence intervals (CIs) for coefficient alpha were investigated. In addition, the bootstrap methods were compared with the most promising coefficient alpha CI estimation methods reported in the literature. The CI methods were assessed through a Monte Carlo simulation utilizing conditions…

A convenient method of obtaining percentile norms and accompanying interval estimates for self-report mood scales (DASS, DASS-21, HADS, PANAS, and sAD).

PubMed

Crawford, John R; Garthwaite, Paul H; Lawrie, Caroline J; Henry, Julie D; MacDonald, Marie A; Sutherland, Jane; Sinha, Priyanka

2009-06-01

A series of recent papers have reported normative data from the general adult population for commonly used self-report mood scales. To bring together and supplement these data in order to provide a convenient means of obtaining percentile norms for the mood scales. A computer program was developed that provides point and interval estimates of the percentile rank corresponding to raw scores on the various self-report scales. The program can be used to obtain point and interval estimates of the percentile rank of an individual's raw scores on the DASS, DASS-21, HADS, PANAS, and sAD mood scales, based on normative sample sizes ranging from 758 to 3822. The interval estimates can be obtained using either classical or Bayesian methods as preferred. The computer program (which can be downloaded at www.abdn.ac.uk/~psy086/dept/MoodScore.htm) provides a convenient and reliable means of supplementing existing cut-off scores for self-report mood scales.

Observed Ozone Production Efficiencies at Rural New York State Locations from 1997-2016

NASA Astrophysics Data System (ADS)

Ninneman, M.; Demerjian, K. L.; Schwab, J. J.

2017-12-01

The ozone production efficiency (OPE) has long been used to assess the effectiveness of ozone (O3)-producing oxidation cycles. However, most previous studies have examined the OPE during summer field intensives, rather than for multiple summers. To address this research gap, this study estimated the empirical OPE (ΔO3 / ΔNOz) at two rural locations in New York State (NYS) during photo-chemically productive hours (11 a.m.-4 p.m. Eastern Standard Time (EST)) in summer (June-August) from 1997-2016. The two rural NYS locations of interest were (1) Pinnacle State Park (PSP) in Addison, New York (NY), and (2) Whiteface Mountain Summit (WFMS) in Wilmington, NY. Hourly-averaged measurements of oxides of nitrogen (NOx), reactive odd nitrogen (NOy), and O3 from the Atmospheric Sciences Research Center (ASRC) at the University at Albany, State University of New York (SUNY) and the New York State Department of Environmental Conservation (NYS DEC) were used to estimate the observed OPE at both sites. Species data was filtered by temperature and solar radiation since the OPEs at PSP and WFMS were found to be sensitive to both meteorological parameters. Observed OPEs at both sites were estimated on a monthly and annual basis over the 20-year period. The OPEs from 1997-2016 at PSP and WFMS vary from year-to-year. This is due in part to the annual variation of the meteorological parameters - such as precipitation, temperature, and solar radiation - that influence the OPE estimate. Therefore, OPEs were also estimated over four 5-year intervals at each site to (1) remove some of the meteorological variability, and (2) further understand how the OPE changed over time with decreasing NOx levels.

Background music as a quasi clock in retrospective duration judgments.

PubMed

Bailey, Nicole; Areni, Charles S

2006-04-01

The segmentation-change model of time perception proposes that individuals engaged in cognitive tasks during a given interval of time retrospectively estimate duration by recalling events that occurred during the interval and inferring each event's duration. Previous research suggests that individuals can recall the number of songs heard during an interval and infer the length of each song, exactly the conditions that foster estimates of duration based on the segmentation-change model. The results of a laboratory experiment indicated that subjects who solved word-search puzzles for 20 min. estimated the duration of the interval to be longer when 8 short songs (<3 min.) as opposed to 4 long songs (6+ min.) were played in the background, regardless of whether the musical format was Contemporary Dance or New Age. Assuming each song represented a distinct segment in memory, these results are consistent with the segmentation-change model. These results suggest that background music may not always reduce estimates of duration by drawing attention away from the passage of time. Instead, background music may actually expand the subjective length of an interval by creating accessible traces in memory, which are retrospectively used to infer duration.

Genetic response to selection for weaning weight or yearling weight or yearling weight and muscle score in Hereford cattle: efficiency of gain, growth, and carcass characteristics.

PubMed

Koch, R M; Cundiff, L V; Gregory, K E; Van Vleck, L D

2004-03-01

An experiment involving crosses among selection and control lines was conducted to partition direct and maternal additive genetic response to 20 yr of selection for 1) weaning weight, 2) yearling weight, and 3) index of yearling weight and muscle score. Selection response was evaluated for efficiency of gain, growth from birth through market weight, and carcass characteristics. Heritability and genetic correlations among traits were estimated using animal model analyses. Over a time-constant interval, selected lines were heavier, gained more weight, consumed more ME, and had more gain/ME than the control. Over a weight-constant interval, selected lines required fewer days, consumed less ME, had more efficient gains, and required less energy for maintenance than control. Direct and maternal responses were estimated from reciprocal crosses among unselected sires and dams of control and selection lines. Most of the genetic response to selection in all three lines was associated with direct genetic effects, and the highest proportion was from postweaning gain. Indirect responses of carcass characteristics to selection over the 20 yr were increased weight of carcasses that had more lean meat, produced with less feed per unit of gain. At a constant carcass weight, selected lines had 1.32 to 1.85% more retail product and 1.62 to 2.24% less fat trim and 10/100 to 25/100 degrees less marbling than control. At a constant age, heritability of direct and maternal effects and correlations between them were as follows: market weight, 0.36, 0.14, and 0.10; carcass weight, 0.26, 0.15, and 0.03; longissimus muscle area, 0.33, 0.00, and 0.00; marbling, 0.36, 0.07, and -0.35; fat thickness, 0.41, 0.05, and -0.18; percentage of kidney, pelvic, and heart fat, 0.12, 0.08, and -0.76; percentage of retail product, 0.46, 0.05, and -0.29; retail product weight, 0.44, 0.08, -0.14; and muscle score, 0.37, 0.14, and -0.54. Selection criteria in all lines improved efficiency of postweaning gain and increased the amount of salable lean meat on an age- or weight-constant basis, but carcasses had slightly lower marbling scores.

Probabilistic migration modelling focused on functional barrier efficiency and low migration concepts in support of risk assessment.

PubMed

Brandsch, Rainer

2017-10-01

Migration modelling provides reliable migration estimates from food-contact materials (FCM) to food or food simulants based on mass-transfer parameters like diffusion and partition coefficients related to individual materials. In most cases, mass-transfer parameters are not readily available from the literature and for this reason are estimated with a given uncertainty. Historically, uncertainty was accounted for by introducing upper limit concepts first, turning out to be of limited applicability due to highly overestimated migration results. Probabilistic migration modelling gives the possibility to consider uncertainty of the mass-transfer parameters as well as other model inputs. With respect to a functional barrier, the most important parameters among others are the diffusion properties of the functional barrier and its thickness. A software tool that accepts distribution as inputs and is capable of applying Monte Carlo methods, i.e., random sampling from the input distributions of the relevant parameters (i.e., diffusion coefficient and layer thickness), predicts migration results with related uncertainty and confidence intervals. The capabilities of probabilistic migration modelling are presented in the view of three case studies (1) sensitivity analysis, (2) functional barrier efficiency and (3) validation by experimental testing. Based on the predicted migration by probabilistic migration modelling and related exposure estimates, safety evaluation of new materials in the context of existing or new packaging concepts is possible. Identifying associated migration risk and potential safety concerns in the early stage of packaging development is possible. Furthermore, dedicated material selection exhibiting required functional barrier efficiency under application conditions becomes feasible. Validation of the migration risk assessment by probabilistic migration modelling through a minimum of dedicated experimental testing is strongly recommended.

Determination of prospective displacement-based gate threshold for respiratory-gated radiation delivery from retrospective phase-based gate threshold selected at 4D CT simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vedam, S.; Archambault, L.; Starkschall, G.

2007-11-15

Four-dimensional (4D) computed tomography (CT) imaging has found increasing importance in the localization of tumor and surrounding normal structures throughout the respiratory cycle. Based on such tumor motion information, it is possible to identify the appropriate phase interval for respiratory gated treatment planning and delivery. Such a gating phase interval is determined retrospectively based on tumor motion from internal tumor displacement. However, respiratory-gated treatment is delivered prospectively based on motion determined predominantly from an external monitor. Therefore, the simulation gate threshold determined from the retrospective phase interval selected for gating at 4D CT simulation may not correspond to the deliverymore » gate threshold that is determined from the prospective external monitor displacement at treatment delivery. The purpose of the present work is to establish a relationship between the thresholds for respiratory gating determined at CT simulation and treatment delivery, respectively. One hundred fifty external respiratory motion traces, from 90 patients, with and without audio-visual biofeedback, are analyzed. Two respiratory phase intervals, 40%-60% and 30%-70%, are chosen for respiratory gating from the 4D CT-derived tumor motion trajectory. From residual tumor displacements within each such gating phase interval, a simulation gate threshold is defined based on (a) the average and (b) the maximum respiratory displacement within the phase interval. The duty cycle for prospective gated delivery is estimated from the proportion of external monitor displacement data points within both the selected phase interval and the simulation gate threshold. The delivery gate threshold is then determined iteratively to match the above determined duty cycle. The magnitude of the difference between such gate thresholds determined at simulation and treatment delivery is quantified in each case. Phantom motion tests yielded coincidence of simulation and delivery gate thresholds to within 0.3%. For patient data analysis, differences between simulation and delivery gate thresholds are reported as a fraction of the total respiratory motion range. For the smaller phase interval, the differences between simulation and delivery gate thresholds are 8{+-}11% and 14{+-}21% with and without audio-visual biofeedback, respectively, when the simulation gate threshold is determined based on the mean respiratory displacement within the 40%-60% gating phase interval. For the longer phase interval, corresponding differences are 4{+-}7% and 8{+-}15% with and without audio-visual biofeedback, respectively. Alternatively, when the simulation gate threshold is determined based on the maximum average respiratory displacement within the gating phase interval, greater differences between simulation and delivery gate thresholds are observed. A relationship between retrospective simulation gate threshold and prospective delivery gate threshold for respiratory gating is established and validated for regular and nonregular respiratory motion. Using this relationship, the delivery gate threshold can be reliably estimated at the time of 4D CT simulation, thereby improving the accuracy and efficiency of respiratory-gated radiation delivery.« less

Determination of prospective displacement-based gate threshold for respiratory-gated radiation delivery from retrospective phase-based gate threshold selected at 4D CT simulation.

PubMed

Vedam, S; Archambault, L; Starkschall, G; Mohan, R; Beddar, S

2007-11-01

Four-dimensional (4D) computed tomography (CT) imaging has found increasing importance in the localization of tumor and surrounding normal structures throughout the respiratory cycle. Based on such tumor motion information, it is possible to identify the appropriate phase interval for respiratory gated treatment planning and delivery. Such a gating phase interval is determined retrospectively based on tumor motion from internal tumor displacement. However, respiratory-gated treatment is delivered prospectively based on motion determined predominantly from an external monitor. Therefore, the simulation gate threshold determined from the retrospective phase interval selected for gating at 4D CT simulation may not correspond to the delivery gate threshold that is determined from the prospective external monitor displacement at treatment delivery. The purpose of the present work is to establish a relationship between the thresholds for respiratory gating determined at CT simulation and treatment delivery, respectively. One hundred fifty external respiratory motion traces, from 90 patients, with and without audio-visual biofeedback, are analyzed. Two respiratory phase intervals, 40%-60% and 30%-70%, are chosen for respiratory gating from the 4D CT-derived tumor motion trajectory. From residual tumor displacements within each such gating phase interval, a simulation gate threshold is defined based on (a) the average and (b) the maximum respiratory displacement within the phase interval. The duty cycle for prospective gated delivery is estimated from the proportion of external monitor displacement data points within both the selected phase interval and the simulation gate threshold. The delivery gate threshold is then determined iteratively to match the above determined duty cycle. The magnitude of the difference between such gate thresholds determined at simulation and treatment delivery is quantified in each case. Phantom motion tests yielded coincidence of simulation and delivery gate thresholds to within 0.3%. For patient data analysis, differences between simulation and delivery gate thresholds are reported as a fraction of the total respiratory motion range. For the smaller phase interval, the differences between simulation and delivery gate thresholds are 8 +/- 11% and 14 +/- 21% with and without audio-visual biofeedback, respectively, when the simulation gate threshold is determined based on the mean respiratory displacement within the 40%-60% gating phase interval. For the longer phase interval, corresponding differences are 4 +/- 7% and 8 +/- 15% with and without audiovisual biofeedback, respectively. Alternatively, when the simulation gate threshold is determined based on the maximum average respiratory displacement within the gating phase interval, greater differences between simulation and delivery gate thresholds are observed. A relationship between retrospective simulation gate threshold and prospective delivery gate threshold for respiratory gating is established and validated for regular and nonregular respiratory motion. Using this relationship, the delivery gate threshold can be reliably estimated at the time of 4D CT simulation, thereby improving the accuracy and efficiency of respiratory-gated radiation delivery.

Development of Guidelines for Identification of SCC Sites and Estimation of Re-Inspection Intervals for SCC Direct Assessment

DOT National Transportation Integrated Search

2010-05-31

This report describes the development of a series of guidelines for the identification of SCC sites and the estimation of re-inspection intervals. These SCC Guidelines are designed to complement and supplement existing SCC Direct Assessment protocols...

Profile-likelihood Confidence Intervals in Item Response Theory Models.

PubMed

Chalmers, R Philip; Pek, Jolynn; Liu, Yang

2017-01-01

Confidence intervals (CIs) are fundamental inferential devices which quantify the sampling variability of parameter estimates. In item response theory, CIs have been primarily obtained from large-sample Wald-type approaches based on standard error estimates, derived from the observed or expected information matrix, after parameters have been estimated via maximum likelihood. An alternative approach to constructing CIs is to quantify sampling variability directly from the likelihood function with a technique known as profile-likelihood confidence intervals (PL CIs). In this article, we introduce PL CIs for item response theory models, compare PL CIs to classical large-sample Wald-type CIs, and demonstrate important distinctions among these CIs. CIs are then constructed for parameters directly estimated in the specified model and for transformed parameters which are often obtained post-estimation. Monte Carlo simulation results suggest that PL CIs perform consistently better than Wald-type CIs for both non-transformed and transformed parameters.

Robust guaranteed-cost adaptive quantum phase estimation

NASA Astrophysics Data System (ADS)

Roy, Shibdas; Berry, Dominic W.; Petersen, Ian R.; Huntington, Elanor H.

2017-05-01

Quantum parameter estimation plays a key role in many fields like quantum computation, communication, and metrology. Optimal estimation allows one to achieve the most precise parameter estimates, but requires accurate knowledge of the model. Any inevitable uncertainty in the model parameters may heavily degrade the quality of the estimate. It is therefore desired to make the estimation process robust to such uncertainties. Robust estimation was previously studied for a varying phase, where the goal was to estimate the phase at some time in the past, using the measurement results from both before and after that time within a fixed time interval up to current time. Here, we consider a robust guaranteed-cost filter yielding robust estimates of a varying phase in real time, where the current phase is estimated using only past measurements. Our filter minimizes the largest (worst-case) variance in the allowable range of the uncertain model parameter(s) and this determines its guaranteed cost. It outperforms in the worst case the optimal Kalman filter designed for the model with no uncertainty, which corresponds to the center of the possible range of the uncertain parameter(s). Moreover, unlike the Kalman filter, our filter in the worst case always performs better than the best achievable variance for heterodyne measurements, which we consider as the tolerable threshold for our system. Furthermore, we consider effective quantum efficiency and effective noise power, and show that our filter provides the best results by these measures in the worst case.

Estimating short-run and long-run interaction mechanisms in interictal state.

PubMed

Ozkaya, Ata; Korürek, Mehmet

2010-04-01

We address the issue of analyzing electroencephalogram (EEG) from seizure patients in order to test, model and determine the statistical properties that distinguish between EEG states (interictal, pre-ictal, ictal) by introducing a new class of time series analysis methods. In the present study: firstly, we employ statistical methods to determine the non-stationary behavior of focal interictal epileptiform series within very short time intervals; secondly, for such intervals that are deemed non-stationary we suggest the concept of Autoregressive Integrated Moving Average (ARIMA) process modelling, well known in time series analysis. We finally address the queries of causal relationships between epileptic states and between brain areas during epileptiform activity. We estimate the interaction between different EEG series (channels) in short time intervals by performing Granger-causality analysis and also estimate such interaction in long time intervals by employing Cointegration analysis, both analysis methods are well-known in econometrics. Here we find: first, that the causal relationship between neuronal assemblies can be identified according to the duration and the direction of their possible mutual influences; second, that although the estimated bidirectional causality in short time intervals yields that the neuronal ensembles positively affect each other, in long time intervals neither of them is affected (increasing amplitudes) from this relationship. Moreover, Cointegration analysis of the EEG series enables us to identify whether there is a causal link from the interictal state to ictal state.

Estimation of recurrence interval of large earthquakes on the central Longmen Shan fault zone based on seismic moment accumulation/release model.

PubMed

Ren, Junjie; Zhang, Shimin

2013-01-01

Recurrence interval of large earthquake on an active fault zone is an important parameter in assessing seismic hazard. The 2008 Wenchuan earthquake (Mw 7.9) occurred on the central Longmen Shan fault zone and ruptured the Yingxiu-Beichuan fault (YBF) and the Guanxian-Jiangyou fault (GJF). However, there is a considerable discrepancy among recurrence intervals of large earthquake in preseismic and postseismic estimates based on slip rate and paleoseismologic results. Post-seismic trenches showed that the central Longmen Shan fault zone probably undertakes an event similar to the 2008 quake, suggesting a characteristic earthquake model. In this paper, we use the published seismogenic model of the 2008 earthquake based on Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR) data and construct a characteristic seismic moment accumulation/release model to estimate recurrence interval of large earthquakes on the central Longmen Shan fault zone. Our results show that the seismogenic zone accommodates a moment rate of (2.7 ± 0.3) × 10¹⁷ N m/yr, and a recurrence interval of 3900 ± 400 yrs is necessary for accumulation of strain energy equivalent to the 2008 earthquake. This study provides a preferred interval estimation of large earthquakes for seismic hazard analysis in the Longmen Shan region.

Estimation of Recurrence Interval of Large Earthquakes on the Central Longmen Shan Fault Zone Based on Seismic Moment Accumulation/Release Model

PubMed Central

Zhang, Shimin

2013-01-01

Recurrence interval of large earthquake on an active fault zone is an important parameter in assessing seismic hazard. The 2008 Wenchuan earthquake (Mw 7.9) occurred on the central Longmen Shan fault zone and ruptured the Yingxiu-Beichuan fault (YBF) and the Guanxian-Jiangyou fault (GJF). However, there is a considerable discrepancy among recurrence intervals of large earthquake in preseismic and postseismic estimates based on slip rate and paleoseismologic results. Post-seismic trenches showed that the central Longmen Shan fault zone probably undertakes an event similar to the 2008 quake, suggesting a characteristic earthquake model. In this paper, we use the published seismogenic model of the 2008 earthquake based on Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR) data and construct a characteristic seismic moment accumulation/release model to estimate recurrence interval of large earthquakes on the central Longmen Shan fault zone. Our results show that the seismogenic zone accommodates a moment rate of (2.7 ± 0.3) × 1017 N m/yr, and a recurrence interval of 3900 ± 400 yrs is necessary for accumulation of strain energy equivalent to the 2008 earthquake. This study provides a preferred interval estimation of large earthquakes for seismic hazard analysis in the Longmen Shan region. PMID:23878524

Reducing the width of confidence intervals for the difference between two population means by inverting adaptive tests.

PubMed

O'Gorman, Thomas W

2018-05-01

In the last decade, it has been shown that an adaptive testing method could be used, along with the Robbins-Monro search procedure, to obtain confidence intervals that are often narrower than traditional confidence intervals. However, these confidence interval limits require a great deal of computation and some familiarity with stochastic search methods. We propose a method for estimating the limits of confidence intervals that uses only a few tests of significance. We compare these limits to those obtained by a lengthy Robbins-Monro stochastic search and find that the proposed method is nearly as accurate as the Robbins-Monro search. Adaptive confidence intervals that are produced by the proposed method are often narrower than traditional confidence intervals when the distributions are long-tailed, skewed, or bimodal. Moreover, the proposed method of estimating confidence interval limits is easy to understand, because it is based solely on the p-values from a few tests of significance.

Evaluating the Impact of Guessing and Its Interactions With Other Test Characteristics on Confidence Interval Procedures for Coefficient Alpha

PubMed Central

Paek, Insu

2015-01-01

The effect of guessing on the point estimate of coefficient alpha has been studied in the literature, but the impact of guessing and its interactions with other test characteristics on the interval estimators for coefficient alpha has not been fully investigated. This study examined the impact of guessing and its interactions with other test characteristics on four confidence interval (CI) procedures for coefficient alpha in terms of coverage rate (CR), length, and the degree of asymmetry of CI estimates. In addition, interval estimates of coefficient alpha when data follow the essentially tau-equivalent condition were investigated as a supplement to the case of dichotomous data with examinee guessing. For dichotomous data with guessing, the results did not reveal salient negative effects of guessing and its interactions with other test characteristics (sample size, test length, coefficient alpha levels) on CR and the degree of asymmetry, but the effect of guessing was salient as a main effect and an interaction effect with sample size on the length of the CI estimates, making longer CI estimates as guessing increases, especially when combined with a small sample size. Other important effects (e.g., CI procedures on CR) are also discussed. PMID:29795863

Case studies in Bayesian microbial risk assessments.

PubMed

Kennedy, Marc C; Clough, Helen E; Turner, Joanne

2009-12-21

The quantification of uncertainty and variability is a key component of quantitative risk analysis. Recent advances in Bayesian statistics make it ideal for integrating multiple sources of information, of different types and quality, and providing a realistic estimate of the combined uncertainty in the final risk estimates. We present two case studies related to foodborne microbial risks. In the first, we combine models to describe the sequence of events resulting in illness from consumption of milk contaminated with VTEC O157. We used Monte Carlo simulation to propagate uncertainty in some of the inputs to computer models describing the farm and pasteurisation process. Resulting simulated contamination levels were then assigned to consumption events from a dietary survey. Finally we accounted for uncertainty in the dose-response relationship and uncertainty due to limited incidence data to derive uncertainty about yearly incidences of illness in young children. Options for altering the risk were considered by running the model with different hypothetical policy-driven exposure scenarios. In the second case study we illustrate an efficient Bayesian sensitivity analysis for identifying the most important parameters of a complex computer code that simulated VTEC O157 prevalence within a managed dairy herd. This was carried out in 2 stages, first to screen out the unimportant inputs, then to perform a more detailed analysis on the remaining inputs. The method works by building a Bayesian statistical approximation to the computer code using a number of known code input/output pairs (training runs). We estimated that the expected total number of children aged 1.5-4.5 who become ill due to VTEC O157 in milk is 8.6 per year, with 95% uncertainty interval (0,11.5). The most extreme policy we considered was banning on-farm pasteurisation of milk, which reduced the estimate to 6.4 with 95% interval (0,11). In the second case study the effective number of inputs was reduced from 30 to 7 in the screening stage, and just 2 inputs were found to explain 82.8% of the output variance. A combined total of 500 runs of the computer code were used. These case studies illustrate the use of Bayesian statistics to perform detailed uncertainty and sensitivity analyses, integrating multiple information sources in a way that is both rigorous and efficient.

78 FR 6232 - Energy Conservation Program: Test Procedures for Conventional Cooking Products With Induction...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-30

... Surface efficiency deviation interval technology unit % % ( ) % Large A Electric Coil... 1 69.79 1.59 1.97... Surface efficiency deviation interval technology unit % % ( ) % Large A Electric Coil... 1 64.52 0.87 1.08... technology unit % % ( ) % Large A Electric Coil... 1 79.81 1.66 2.06 B Electric........ 1 61.81 2.83 3.52...

Four applications of permutation methods to testing a single-mediator model.

PubMed

Taylor, Aaron B; MacKinnon, David P

2012-09-01

Four applications of permutation tests to the single-mediator model are described and evaluated in this study. Permutation tests work by rearranging data in many possible ways in order to estimate the sampling distribution for the test statistic. The four applications to mediation evaluated here are the permutation test of ab, the permutation joint significance test, and the noniterative and iterative permutation confidence intervals for ab. A Monte Carlo simulation study was used to compare these four tests with the four best available tests for mediation found in previous research: the joint significance test, the distribution of the product test, and the percentile and bias-corrected bootstrap tests. We compared the different methods on Type I error, power, and confidence interval coverage. The noniterative permutation confidence interval for ab was the best performer among the new methods. It successfully controlled Type I error, had power nearly as good as the most powerful existing methods, and had better coverage than any existing method. The iterative permutation confidence interval for ab had lower power than do some existing methods, but it performed better than any other method in terms of coverage. The permutation confidence interval methods are recommended when estimating a confidence interval is a primary concern. SPSS and SAS macros that estimate these confidence intervals are provided.

[Age- and sex-specific reference intervals for 10 health examination items: mega-data from a Japanese Health Service Association].

PubMed

Suka, Machi; Yoshida, Katsumi; Kawai, Tadashi; Aoki, Yoshikazu; Yamane, Noriyuki; Yamauchi, Kuniaki

2005-07-01

To determine age- and sex-specific reference intervals for 10 health examination items in Japanese adults. Health examination data were accumulated from 24 different prefectural health service associations affiliated with the Japan Association of Health Service. Those who were non-smokers, drank less than 7 days/week, and had a body mass index of 18.5-24.9kg/m2 were sampled as a reference population (n = 737,538; 224,947 men and 512,591 women). After classified by age and sex, reference intervals for 10 health examination items (systolic blood pressure, diastolic blood pressure, total cholesterol, triglyceride, glucose, uric acid, AST, ALT, gamma-GT, and hemoglobin) were estimated using the parametric and nonparametric methods. In every item except for hemoglobin, men had higher reference intervals than women. Systolic blood pressure, total cholesterol, and glucose showed an upward trend in values with increasing age. Hemoglobin showed a downward trend in values with increasing age. Triglyceride, ALT, and gamma-GT reached a peak in middle age. Overall, parametric estimates showed narrower reference intervals than non-parametric estimates. Reference intervals vary with age and sex. Age- and sex-specific reference intervals may contribute to better assessment of health examination data.

Estimates of density, detection probability, and factors influencing detection of burrowing owls in the Mojave Desert

USGS Publications Warehouse

Crowe, D.E.; Longshore, K.M.

2010-01-01

We estimated relative abundance and density of Western Burrowing Owls (Athene cunicularia hypugaea) at two sites in the Mojave Desert (200304). We made modifications to previously established Burrowing Owl survey techniques for use in desert shrublands and evaluated several factors that might influence the detection of owls. We tested the effectiveness of the call-broadcast technique for surveying this species, the efficiency of this technique at early and late breeding stages, and the effectiveness of various numbers of vocalization intervals during broadcasting sessions. Only 1 (3) of 31 initial (new) owl responses was detected during passive-listening sessions. We found that surveying early in the nesting season was more likely to produce new owl detections compared to surveying later in the nesting season. New owls detected during each of the three vocalization intervals (each consisting of 30 sec of vocalizations followed by 30 sec of silence) of our broadcasting session were similar (37, 40, and 23; n 30). We used a combination of detection trials (sighting probability) and double-observer method to estimate the components of detection probability, i.e., availability and perception. Availability for all sites and years, as determined by detection trials, ranged from 46.158.2. Relative abundance, measured as frequency of occurrence and defined as the proportion of surveys with at least one owl, ranged from 19.232.0 for both sites and years. Density at our eastern Mojave Desert site was estimated at 0.09 ?? 0.01 (SE) owl territories/km2 and 0.16 ?? 0.02 (SE) owl territories/km2 during 2003 and 2004, respectively. In our southern Mojave Desert site, density estimates were 0.09 ?? 0.02 (SE) owl territories/km2 and 0.08 ?? 0.02 (SE) owl territories/km 2 during 2004 and 2005, respectively. ?? 2010 The Raptor Research Foundation, Inc.

Hawksbill satellite-tracking case study: Implications for remigration interval and population estimates

USGS Publications Warehouse

Sartain-Iverson, Autumn R.; Hart, Kristen M.; Fujisaki, Ikuko; Cherkiss, Michael S.; Pollock, Clayton; Lundgren, Ian; Hillis-Starr, Zandy

2016-01-01

Hawksbill sea turtles (Eretmochelys imbricata) are circumtropically distributed and listed as Critically Endangered by the IUCN (Meylan & Donnelly 1999; NMFS & USFWS 1993). To aid in population recovery and protection, the Hawksbill Recovery Plan identified the need to determine demographic information for hawksbills, such as distribution, abundance, seasonal movements, foraging areas (sections 121 and 2211), growth rates, and survivorship (section 2213, NMFS & USFWS 1993). Mark-recapture analyses are helpful in estimating demographic parameters and have been used for hawksbills throughout the Caribbean (e.g., Richardson et al. 1999; Velez-Zuazo et al. 2008); integral to these studies are recaptures at the nesting site as well as remigration interval estimates (Hays 2000). Estimates of remigration intervals (the duration between nesting seasons) are critical to marine turtle population estimates and measures of nesting success (Hays 2000; Richardson et al. 1999). Although hawksbills in the Caribbean generally show natal philopatry and nesting-site fidelity (Bass et al. 1996; Bowen et al. 2007), exceptions to this have been observed for hawksbills and other marine turtles (Bowen & Karl 2007; Diamond 1976; Esteban et al. 2015; Hart et al. 2013). This flexibility in choosing a nesting beach could therefore affect the apparent remigration interval and subsequently, region-wide population counts.

Recognition memory across the lifespan: the impact of word frequency and study-test interval on estimates of familiarity and recollection

PubMed Central

Meier, Beat; Rey-Mermet, Alodie; Rothen, Nicolas; Graf, Peter

2013-01-01

The goal of this study was to investigate recognition memory performance across the lifespan and to determine how estimates of recollection and familiarity contribute to performance. In each of three experiments, participants from five groups from 14 up to 85 years of age (children, young adults, middle-aged adults, young-old adults, and old-old adults) were presented with high- and low-frequency words in a study phase and were tested immediately afterwards and/or after a one day retention interval. The results showed that word frequency and retention interval affected recognition memory performance as well as estimates of recollection and familiarity. Across the lifespan, the trajectory of recognition memory followed an inverse u-shape function that was neither affected by word frequency nor by retention interval. The trajectory of estimates of recollection also followed an inverse u-shape function, and was especially pronounced for low-frequency words. In contrast, estimates of familiarity did not differ across the lifespan. The results indicate that age differences in recognition memory are mainly due to differences in processes related to recollection while the contribution of familiarity-based processes seems to be age-invariant. PMID:24198796

Bayesian dynamic regression models for interval censored survival data with application to children dental health.

PubMed

Wang, Xiaojing; Chen, Ming-Hui; Yan, Jun

2013-07-01

Cox models with time-varying coefficients offer great flexibility in capturing the temporal dynamics of covariate effects on event times, which could be hidden from a Cox proportional hazards model. Methodology development for varying coefficient Cox models, however, has been largely limited to right censored data; only limited work on interval censored data has been done. In most existing methods for varying coefficient models, analysts need to specify which covariate coefficients are time-varying and which are not at the time of fitting. We propose a dynamic Cox regression model for interval censored data in a Bayesian framework, where the coefficient curves are piecewise constant but the number of pieces and the jump points are covariate specific and estimated from the data. The model automatically determines the extent to which the temporal dynamics is needed for each covariate, resulting in smoother and more stable curve estimates. The posterior computation is carried out via an efficient reversible jump Markov chain Monte Carlo algorithm. Inference of each coefficient is based on an average of models with different number of pieces and jump points. A simulation study with three covariates, each with a coefficient of different degree in temporal dynamics, confirmed that the dynamic model is preferred to the existing time-varying model in terms of model comparison criteria through conditional predictive ordinate. When applied to a dental health data of children with age between 7 and 12 years, the dynamic model reveals that the relative risk of emergence of permanent tooth 24 between children with and without an infected primary predecessor is the highest at around age 7.5, and that it gradually reduces to one after age 11. These findings were not seen from the existing studies with Cox proportional hazards models.

Statistical methods for estimating normal blood chemistry ranges and variance in rainbow trout (Salmo gairdneri), Shasta Strain

USGS Publications Warehouse

Wedemeyer, Gary A.; Nelson, Nancy C.

1975-01-01

Gaussian and nonparametric (percentile estimate and tolerance interval) statistical methods were used to estimate normal ranges for blood chemistry (bicarbonate, bilirubin, calcium, hematocrit, hemoglobin, magnesium, mean cell hemoglobin concentration, osmolality, inorganic phosphorus, and pH for juvenile rainbow (Salmo gairdneri, Shasta strain) trout held under defined environmental conditions. The percentile estimate and Gaussian methods gave similar normal ranges, whereas the tolerance interval method gave consistently wider ranges for all blood variables except hemoglobin. If the underlying frequency distribution is unknown, the percentile estimate procedure would be the method of choice.

Interval estimation of the overall treatment effect in a meta-analysis of a few small studies with zero events.

PubMed

Pateras, Konstantinos; Nikolakopoulos, Stavros; Mavridis, Dimitris; Roes, Kit C B

2018-03-01

When a meta-analysis consists of a few small trials that report zero events, accounting for heterogeneity in the (interval) estimation of the overall effect is challenging. Typically, we predefine meta-analytical methods to be employed. In practice, data poses restrictions that lead to deviations from the pre-planned analysis, such as the presence of zero events in at least one study arm. We aim to explore heterogeneity estimators behaviour in estimating the overall effect across different levels of sparsity of events. We performed a simulation study that consists of two evaluations. We considered an overall comparison of estimators unconditional on the number of observed zero cells and an additional one by conditioning on the number of observed zero cells. Estimators that performed modestly robust when (interval) estimating the overall treatment effect across a range of heterogeneity assumptions were the Sidik-Jonkman, Hartung-Makambi and improved Paul-Mandel. The relative performance of estimators did not materially differ between making a predefined or data-driven choice. Our investigations confirmed that heterogeneity in such settings cannot be estimated reliably. Estimators whose performance depends strongly on the presence of heterogeneity should be avoided. The choice of estimator does not need to depend on whether or not zero cells are observed.

Estimation of energetic efficiency of heat supply in front of the aircraft at supersonic accelerated flight. Part 1. Mathematical models

NASA Astrophysics Data System (ADS)

Latypov, A. F.

2008-12-01

Fuel economy at boost trajectory of the aerospace plane was estimated during energy supply to the free stream. Initial and final flight velocities were specified. The model of a gliding flight above cold air in an infinite isobaric thermal wake was used. The fuel consumption rates were compared at optimal trajectory. The calculations were carried out using a combined power plant consisting of ramjet and liquid-propellant engine. An exergy model was built in the first part of the paper to estimate the ramjet thrust and specific impulse. A quadratic dependence on aerodynamic lift was used to estimate the aerodynamic drag of aircraft. The energy for flow heating was obtained at the expense of an equivalent reduction of the exergy of combustion products. The dependencies were obtained for increasing the range coefficient of cruise flight for different Mach numbers. The second part of the paper presents a mathematical model for the boost interval of the aircraft flight trajectory and the computational results for the reduction of fuel consumption at the boost trajectory for a given value of the energy supplied in front of the aircraft.

[Age and time estimation during different types of activity].

PubMed

Gareev, E M; Osipova, L G

1980-01-01

The study was concerned with the age characteristics of verbal and operative estimation of time intervals filled with different types of mental and physical activity as well as those free of it. The experiment was conducted on 85 subjects, 7--24 years of age. In all age groups and in both forms of time estimation (except verbal estimation in 10--12 years old children) there was a significant connection between the interval estimation and the type of activity. In adults and in 7--8 years old children, the connection was significantly tighter in operative estimations than in verbal ones. Unlike senior school children and adults, in 7--12 years old children there were sharp differences in precision between operative and verbal estimations and a discordance of their changes under the influence of activity. Precision and variability were rather similar in all age groups. It is suggested that the obtained data show heterochronity and a different rate of development of the higher nervous activity mechanisms providing for reflection of time in the form of verbal and voluntary motor reactions to the given interval.

On the estimation of risk associated with an attenuation prediction

NASA Technical Reports Server (NTRS)

Crane, R. K.

1992-01-01

Viewgraphs from a presentation on the estimation of risk associated with an attenuation prediction is presented. Topics covered include: link failure - attenuation exceeding a specified threshold for a specified time interval or intervals; risk - the probability of one or more failures during the lifetime of the link or during a specified accounting interval; the problem - modeling the probability of attenuation by rainfall to provide a prediction of the attenuation threshold for a specified risk; and an accounting for the inadequacy of a model or models.

Detectability of auditory signals presented without defined observation intervals

NASA Technical Reports Server (NTRS)

Watson, C. S.; Nichols, T. L.

1976-01-01

Ability to detect tones in noise was measured without defined observation intervals. Latency density functions were estimated for the first response following a signal and, separately, for the first response following randomly distributed instances of background noise. Detection performance was measured by the maximum separation between the cumulative latency density functions for signal-plus-noise and for noise alone. Values of the index of detectability, estimated by this procedure, were approximately those obtained with a 2-dB weaker signal and defined observation intervals. Simulation of defined- and non-defined-interval tasks with an energy detector showed that this device performs very similarly to the human listener in both cases.

Re-evaluation of link between interpregnancy interval and adverse birth outcomes: retrospective cohort study matching two intervals per mother

PubMed Central

Pereira, Gavin; Jacoby, Peter; de Klerk, Nicholas; Stanley, Fiona J

2014-01-01

Objective To re-evaluate the causal effect of interpregnancy interval on adverse birth outcomes, on the basis that previous studies relying on between mother comparisons may have inadequately adjusted for confounding by maternal risk factors. Design Retrospective cohort study using conditional logistic regression (matching two intervals per mother so each mother acts as her own control) to model the incidence of adverse birth outcomes as a function of interpregnancy interval; additional unconditional logistic regression with adjustment for confounders enabled comparison with the unmatched design of previous studies. Setting Perth, Western Australia, 1980-2010. Participants 40 441 mothers who each delivered three liveborn singleton neonates. Main outcome measures Preterm birth (<37 weeks), small for gestational age birth (<10th centile of birth weight by sex and gestational age), and low birth weight (<2500 g). Results Within mother analysis of interpregnancy intervals indicated a much weaker effect of short intervals on the odds of preterm birth and low birth weight compared with estimates generated using a traditional between mother analysis. The traditional unmatched design estimated an adjusted odds ratio for an interpregnancy interval of 0-5 months (relative to the reference category of 18-23 months) of 1.41 (95% confidence interval 1.31 to 1.51) for preterm birth, 1.26 (1.15 to 1.37) for low birth weight, and 0.98 (0.92 to 1.06) for small for gestational age birth. In comparison, the matched design showed a much weaker effect of short interpregnancy interval on preterm birth (odds ratio 1.07, 0.86 to 1.34) and low birth weight (1.03, 0.79 to 1.34), and the effect for small for gestational age birth remained small (1.08, 0.87 to 1.34). Both the unmatched and matched models estimated a high odds of small for gestational age birth and low birth weight for long interpregnancy intervals (longer than 59 months), but the estimated effect of long interpregnancy intervals on the odds of preterm birth was much weaker in the matched model than in the unmatched model. Conclusion This study questions the causal effect of short interpregnancy intervals on adverse birth outcomes and points to the possibility of unmeasured or inadequately specified maternal factors in previous studies. PMID:25056260

Constructing Confidence Intervals for Reliability Coefficients Using Central and Noncentral Distributions.

ERIC Educational Resources Information Center

Weber, Deborah A.

Greater understanding and use of confidence intervals is central to changes in statistical practice (G. Cumming and S. Finch, 2001). Reliability coefficients and confidence intervals for reliability coefficients can be computed using a variety of methods. Estimating confidence intervals includes both central and noncentral distribution approaches.…

A Highly Efficient Design Strategy for Regression with Outcome Pooling

PubMed Central

Mitchell, Emily M.; Lyles, Robert H.; Manatunga, Amita K.; Perkins, Neil J.; Schisterman, Enrique F.

2014-01-01

The potential for research involving biospecimens can be hindered by the prohibitive cost of performing laboratory assays on individual samples. To mitigate this cost, strategies such as randomly selecting a portion of specimens for analysis or randomly pooling specimens prior to performing laboratory assays may be employed. These techniques, while effective in reducing cost, are often accompanied by a considerable loss of statistical efficiency. We propose a novel pooling strategy based on the k-means clustering algorithm to reduce laboratory costs while maintaining a high level of statistical efficiency when predictor variables are measured on all subjects, but the outcome of interest is assessed in pools. We perform simulations motivated by the BioCycle study to compare this k-means pooling strategy with current pooling and selection techniques under simple and multiple linear regression models. While all of the methods considered produce unbiased estimates and confidence intervals with appropriate coverage, pooling under k-means clustering provides the most precise estimates, closely approximating results from the full data and losing minimal precision as the total number of pools decreases. The benefits of k-means clustering evident in the simulation study are then applied to an analysis of the BioCycle dataset. In conclusion, when the number of lab tests is limited by budget, pooling specimens based on k-means clustering prior to performing lab assays can be an effective way to save money with minimal information loss in a regression setting. PMID:25220822

A highly efficient design strategy for regression with outcome pooling.

PubMed

Mitchell, Emily M; Lyles, Robert H; Manatunga, Amita K; Perkins, Neil J; Schisterman, Enrique F

2014-12-10

The potential for research involving biospecimens can be hindered by the prohibitive cost of performing laboratory assays on individual samples. To mitigate this cost, strategies such as randomly selecting a portion of specimens for analysis or randomly pooling specimens prior to performing laboratory assays may be employed. These techniques, while effective in reducing cost, are often accompanied by a considerable loss of statistical efficiency. We propose a novel pooling strategy based on the k-means clustering algorithm to reduce laboratory costs while maintaining a high level of statistical efficiency when predictor variables are measured on all subjects, but the outcome of interest is assessed in pools. We perform simulations motivated by the BioCycle study to compare this k-means pooling strategy with current pooling and selection techniques under simple and multiple linear regression models. While all of the methods considered produce unbiased estimates and confidence intervals with appropriate coverage, pooling under k-means clustering provides the most precise estimates, closely approximating results from the full data and losing minimal precision as the total number of pools decreases. The benefits of k-means clustering evident in the simulation study are then applied to an analysis of the BioCycle dataset. In conclusion, when the number of lab tests is limited by budget, pooling specimens based on k-means clustering prior to performing lab assays can be an effective way to save money with minimal information loss in a regression setting. Copyright © 2014 John Wiley & Sons, Ltd.

Comparison of polar cap potential drops estimated from solar wind and ground magnetometer data - CDAW 6

NASA Technical Reports Server (NTRS)

Reiff, P. H.; Spiro, R. W.; Wolf, R. A.; Kamide, Y.; King, J. H.

1985-01-01

It is pointed out that the maximum electrostatic potential difference across the polar cap, Phi, is a fundamental measure of the coupling between the solar wind and the earth's magnetosphere/ionosphere sytem. During the Coordinated Data Analysis Workshop (CDAW) 6 intervals, no suitably instrumented spacecraft was in an appropriate orbit to determine the polar-cap potential drop directly. However, two recently developed independent techniques make it possible to estimate the polar-cap potential drop for times when direct spacecraft data are not available. The present investigation is concerned with a comparison of cross-polar-cap potential drop estimates calculated for the two CDAW 6 intervals on the basis of these two techniques. In the case of one interval, the agreement between the potential drops and Joule heating rates is relatively good. In the second interval, however, the agreement is not very good. Explanations for this discrepancy are discussed.

Estimation of the incubation period of invasive aspergillosis by survival models in acute myeloid leukemia patients.

PubMed

Bénet, Thomas; Voirin, Nicolas; Nicolle, Marie-Christine; Picot, Stephane; Michallet, Mauricette; Vanhems, Philippe

2013-02-01

The duration of the incubation of invasive aspergillosis (IA) remains unknown. The objective of this investigation was to estimate the time interval between aplasia onset and that of IA symptoms in acute myeloid leukemia (AML) patients. A single-centre prospective survey (2004-2009) included all patients with AML and probable/proven IA. Parametric survival models were fitted to the distribution of the time intervals between aplasia onset and IA. Overall, 53 patients had IA after aplasia, with the median observed time interval between the two being 15 days. Based on log-normal distribution, the median estimated IA incubation period was 14.6 days (95% CI; 12.8-16.5 days).

Estimation of urban runoff and water quality using remote sensing and artificial intelligence.

PubMed

Ha, S R; Park, S Y; Park, D H

2003-01-01

Water quality and quantity of runoff are strongly dependent on the landuse and landcover (LULC) criteria. In this study, we developed a more improved parameter estimation procedure for the environmental model using remote sensing (RS) and artificial intelligence (AI) techniques. Landsat TM multi-band (7bands) and Korea Multi-Purpose Satellite (KOMPSAT) panchromatic data were selected for input data processing. We employed two kinds of artificial intelligence techniques, RBF-NN (radial-basis-function neural network) and ANN (artificial neural network), to classify LULC of the study area. A bootstrap resampling method, a statistical technique, was employed to generate the confidence intervals and distribution of the unit load. SWMM was used to simulate the urban runoff and water quality and applied to the study watershed. The condition of urban flow and non-point contaminations was simulated with rainfall-runoff and measured water quality data. The estimated total runoff, peak time, and pollutant generation varied considerably according to the classification accuracy and percentile unit load applied. The proposed procedure would efficiently be applied to water quality and runoff simulation in a rapidly changing urban area.

Estimation of economic values for traits of dairy sheep: I. Model development.

PubMed

Wolfová, M; Wolf, J; Krupová, Z; Kica, J

2009-05-01

A bioeconomic model was developed to estimate effects of change in production and functional traits on profit of dairy or dual-purpose milked sheep under alternative management systems. The flock structure was described in terms of animal categories and probabilities of transitions among them, and a Markov chain approach was used to calculate the stationary state of the resultant ewe flock. The model included both deterministic and stochastic components. Performance for most traits was simulated as the population average, but variation in several traits was taken into account. Management options included lambing intervals, mating system, and culling strategy for ewes, weaning and marketing strategy for progeny, and feeding system. The present value of profit computed as the difference between total revenues and total costs per ewe per year, both discounted to the birth date of the animals, was used as the criterion for economic efficiency of the production system in the stationary state. Economic values (change in system profit per unit change in the trait) of up to 35 milk production, growth, carcass, wool, and functional traits may be estimated.

Epidemic risk from cholera introductions into Mexico.

PubMed

Moore, Sean M; Shannon, Kerry L; Zelaya, Carla E; Azman, Andrew S; Lessler, Justin

2014-02-21

Stemming from the 2010 cholera outbreak in Haiti, cholera transmission in Hispaniola continues with over 40,000 cases in 2013. The presence of an ongoing cholera outbreak in the region poses substantial risks to countries throughout the Americas, particularly in areas with poor infrastructure. Since September 9, 2013 nearly 200 cholera cases have been reported in Mexico, as a result of introductions from Hispaniola or Cuba. There appear to have been multiple introductions into Mexico resulting in outbreaks of 2 to over 150 people. Using publicly available data, we attempt to estimate the reproductive number (R) of cholera in Mexico, and thereby assess the potential of continued introductions to establish a sustained epidemic. We estimate R for cholera in Mexico to be between 0.8 to 1.1, depending on the number of introductions, with the confidence intervals for the most plausible estimates crossing 1. These results suggest that the efficiency of cholera transmission in some regions of Mexico is near that necessary for a large epidemic. Intensive surveillance, evaluation of water and sanitation infrastructure, and planning for rapid response are warranted steps to avoid potential large epidemics in the region.

Likelihood inference for COM-Poisson cure rate model with interval-censored data and Weibull lifetimes.

PubMed

Pal, Suvra; Balakrishnan, N

2017-10-01

In this paper, we consider a competing cause scenario and assume the number of competing causes to follow a Conway-Maxwell Poisson distribution which can capture both over and under dispersion that is usually encountered in discrete data. Assuming the population of interest having a component cure and the form of the data to be interval censored, as opposed to the usually considered right-censored data, the main contribution is in developing the steps of the expectation maximization algorithm for the determination of the maximum likelihood estimates of the model parameters of the flexible Conway-Maxwell Poisson cure rate model with Weibull lifetimes. An extensive Monte Carlo simulation study is carried out to demonstrate the performance of the proposed estimation method. Model discrimination within the Conway-Maxwell Poisson distribution is addressed using the likelihood ratio test and information-based criteria to select a suitable competing cause distribution that provides the best fit to the data. A simulation study is also carried out to demonstrate the loss in efficiency when selecting an improper competing cause distribution which justifies the use of a flexible family of distributions for the number of competing causes. Finally, the proposed methodology and the flexibility of the Conway-Maxwell Poisson distribution are illustrated with two known data sets from the literature: smoking cessation data and breast cosmesis data.

Efficient visual grasping alignment for cylinders

NASA Technical Reports Server (NTRS)

Nicewarner, Keith E.; Kelley, Robert B.

1992-01-01

Monocular information from a gripper-mounted camera is used to servo the robot gripper to grasp a cylinder. The fundamental concept for rapid pose estimation is to reduce the amount of information that needs to be processed during each vision update interval. The grasping procedure is divided into four phases: learn, recognition, alignment, and approach. In the learn phase, a cylinder is placed in the gripper and the pose estimate is stored and later used as the servo target. This is performed once as a calibration step. The recognition phase verifies the presence of a cylinder in the camera field of view. An initial pose estimate is computed and uncluttered scan regions are selected. The radius of the cylinder is estimated by moving the robot a fixed distance toward the cylinder and observing the change in the image. The alignment phase processes only the scan regions obtained previously. Rapid pose estimates are used to align the robot with the cylinder at a fixed distance from it. The relative motion of the cylinder is used to generate an extrapolated pose-based trajectory for the robot controller. The approach phase guides the robot gripper to a grasping position. The cylinder can be grasped with a minimal reaction force and torque when only rough global pose information is initially available.

Efficient visual grasping alignment for cylinders

NASA Technical Reports Server (NTRS)

Nicewarner, Keith E.; Kelley, Robert B.

1991-01-01

Monocular information from a gripper-mounted camera is used to servo the robot gripper to grasp a cylinder. The fundamental concept for rapid pose estimation is to reduce the amount of information that needs to be processed during each vision update interval. The grasping procedure is divided into four phases: learn, recognition, alignment, and approach. In the learn phase, a cylinder is placed in the gripper and the pose estimate is stored and later used as the servo target. This is performed once as a calibration step. The recognition phase verifies the presence of a cylinder in the camera field of view. An initial pose estimate is computed and uncluttered scan regions are selected. The radius of the cylinder is estimated by moving the robot a fixed distance toward the cylinder and observing the change in the image. The alignment phase processes only the scan regions obtained previously. Rapid pose estimates are used to align the robot with the cylinder at a fixed distance from it. The relative motion of the cylinder is used to generate an extrapolated pose-based trajectory for the robot controller. The approach phase guides the robot gripper to a grasping position. The cylinder can be grasped with a minimal reaction force and torque when only rough global pose information is initially available.

Receptive Field Inference with Localized Priors

PubMed Central

Park, Mijung; Pillow, Jonathan W.

2011-01-01

The linear receptive field describes a mapping from sensory stimuli to a one-dimensional variable governing a neuron's spike response. However, traditional receptive field estimators such as the spike-triggered average converge slowly and often require large amounts of data. Bayesian methods seek to overcome this problem by biasing estimates towards solutions that are more likely a priori, typically those with small, smooth, or sparse coefficients. Here we introduce a novel Bayesian receptive field estimator designed to incorporate locality, a powerful form of prior information about receptive field structure. The key to our approach is a hierarchical receptive field model that flexibly adapts to localized structure in both spacetime and spatiotemporal frequency, using an inference method known as empirical Bayes. We refer to our method as automatic locality determination (ALD), and show that it can accurately recover various types of smooth, sparse, and localized receptive fields. We apply ALD to neural data from retinal ganglion cells and V1 simple cells, and find it achieves error rates several times lower than standard estimators. Thus, estimates of comparable accuracy can be achieved with substantially less data. Finally, we introduce a computationally efficient Markov Chain Monte Carlo (MCMC) algorithm for fully Bayesian inference under the ALD prior, yielding accurate Bayesian confidence intervals for small or noisy datasets. PMID:22046110

Energy-efficient quantum frequency estimation

NASA Astrophysics Data System (ADS)

Liuzzo-Scorpo, Pietro; Correa, Luis A.; Pollock, Felix A.; Górecka, Agnieszka; Modi, Kavan; Adesso, Gerardo

2018-06-01

The problem of estimating the frequency of a two-level atom in a noisy environment is studied. Our interest is to minimise both the energetic cost of the protocol and the statistical uncertainty of the estimate. In particular, we prepare a probe in a ‘GHZ-diagonal’ state by means of a sequence of qubit gates applied on an ensemble of n atoms in thermal equilibrium. Noise is introduced via a phenomenological time-non-local quantum master equation, which gives rise to a phase-covariant dissipative dynamics. After an interval of free evolution, the n-atom probe is globally measured at an interrogation time chosen to minimise the error bars of the final estimate. We model explicitly a measurement scheme which becomes optimal in a suitable parameter range, and are thus able to calculate the total energetic expenditure of the protocol. Interestingly, we observe that scaling up our multipartite entangled probes offers no precision enhancement when the total available energy {\\boldsymbol{ \\mathcal E }} is limited. This is at stark contrast with standard frequency estimation, where larger probes—more sensitive but also more ‘expensive’ to prepare—are always preferred. Replacing {\\boldsymbol{ \\mathcal E }} by the resource that places the most stringent limitation on each specific experimental setup, would thus help to formulate more realistic metrological prescriptions.

A single-loop optimization method for reliability analysis with second order uncertainty

NASA Astrophysics Data System (ADS)

Xie, Shaojun; Pan, Baisong; Du, Xiaoping

2015-08-01

Reliability analysis may involve random variables and interval variables. In addition, some of the random variables may have interval distribution parameters owing to limited information. This kind of uncertainty is called second order uncertainty. This article develops an efficient reliability method for problems involving the three aforementioned types of uncertain input variables. The analysis produces the maximum and minimum reliability and is computationally demanding because two loops are needed: a reliability analysis loop with respect to random variables and an interval analysis loop for extreme responses with respect to interval variables. The first order reliability method and nonlinear optimization are used for the two loops, respectively. For computational efficiency, the two loops are combined into a single loop by treating the Karush-Kuhn-Tucker (KKT) optimal conditions of the interval analysis as constraints. Three examples are presented to demonstrate the proposed method.

Estimation and confidence intervals for empirical mixing distributions

USGS Publications Warehouse

Link, W.A.; Sauer, J.R.

1995-01-01

Questions regarding collections of parameter estimates can frequently be expressed in terms of an empirical mixing distribution (EMD). This report discusses empirical Bayes estimation of an EMD, with emphasis on the construction of interval estimates. Estimation of the EMD is accomplished by substitution of estimates of prior parameters in the posterior mean of the EMD. This procedure is examined in a parametric model (the normal-normal mixture) and in a semi-parametric model. In both cases, the empirical Bayes bootstrap of Laird and Louis (1987, Journal of the American Statistical Association 82, 739-757) is used to assess the variability of the estimated EMD arising from the estimation of prior parameters. The proposed methods are applied to a meta-analysis of population trend estimates for groups of birds.

Effect of the revisit interval on the accuracy of remote sensing-based estimates of evapotranspiration at field scales

USDA-ARS?s Scientific Manuscript database

Accurate spatially distributed estimates of evapotranspiration (ET) derived from remotely sensed data are critical to a broad range of practical and operational applications. However, due to lengthy return intervals and cloud cover, data acquisition is not continuous over time. To fill the data gaps...

Likelihood-Based Confidence Intervals in Exploratory Factor Analysis

ERIC Educational Resources Information Center

Oort, Frans J.

2011-01-01

In exploratory or unrestricted factor analysis, all factor loadings are free to be estimated. In oblique solutions, the correlations between common factors are free to be estimated as well. The purpose of this article is to show how likelihood-based confidence intervals can be obtained for rotated factor loadings and factor correlations, by…

Implementing the measurement interval midpoint method for change estimation

Treesearch

James A. Westfall; Thomas Frieswyk; Douglas M. Griffith

2009-01-01

The adoption of nationally consistent estimation procedures for the Forest Inventory and Analysis (FIA) program mandates changes in the methods used to develop resource trend information. Particularly, it is prescribed that changes in tree status occur at the midpoint of the measurement interval to minimize potential bias. The individual-tree characteristics requiring...

A note on the kappa statistic for clustered dichotomous data.

PubMed

Zhou, Ming; Yang, Zhao

2014-06-30

The kappa statistic is widely used to assess the agreement between two raters. Motivated by a simulation-based cluster bootstrap method to calculate the variance of the kappa statistic for clustered physician-patients dichotomous data, we investigate its special correlation structure and develop a new simple and efficient data generation algorithm. For the clustered physician-patients dichotomous data, based on the delta method and its special covariance structure, we propose a semi-parametric variance estimator for the kappa statistic. An extensive Monte Carlo simulation study is performed to evaluate the performance of the new proposal and five existing methods with respect to the empirical coverage probability, root-mean-square error, and average width of the 95% confidence interval for the kappa statistic. The variance estimator ignoring the dependence within a cluster is generally inappropriate, and the variance estimators from the new proposal, bootstrap-based methods, and the sampling-based delta method perform reasonably well for at least a moderately large number of clusters (e.g., the number of clusters K ⩾50). The new proposal and sampling-based delta method provide convenient tools for efficient computations and non-simulation-based alternatives to the existing bootstrap-based methods. Moreover, the new proposal has acceptable performance even when the number of clusters is as small as K = 25. To illustrate the practical application of all the methods, one psychiatric research data and two simulated clustered physician-patients dichotomous data are analyzed. Copyright © 2014 John Wiley & Sons, Ltd.

Functional Response of Eretmocerus delhiensis on Trialeurodes vaporariorum by Parasitism and Host Feeding

PubMed Central

Ebrahimifar, Jafar; Allahyari, Hossein

2017-01-01

The parasitoid wasp, Eretmocerus delhiensis (Hymenoptera, Aphelinidae) is a thelytokous and syn-ovigenic parasitoid. To evaluate E. delhiensis as a biocontrol agent in greenhouse, the killing efficiency of this parasitoid by parasitism and host-feeding, were studied. Killing efficiency can be compared by estimation of functional response parameters. Laboratory experiments were performed in controllable conditions to evaluate the functional response of E. delhiensis at eight densities (2, 4, 8, 16, 32, 64, 100, and 120 third nymphal stage) of Trialeurodes vaporariorum (Hemiptera, Aleyrodidae) on two hosts including; tomato and prickly lettuce. The maximum likelihood estimates from regression logistic analysis revealed type II functional response for two host plants and the type of functional response was not affected by host plant. Roger’s model was used to fit the data. The attack rate (a) for E. delhiensis was 0.0286 and 0.0144 per hour on tomato and 0.0434 and 0.0170 per hour on prickly lettuce for parasitism and host feeding, respectively. Furthermore, estimated handling times (Th) were 0.4911 and 1.4453 h on tomato and 0.5713 and 1.5001 h on prickly lettuce for parasitism and host feeding, respectively. Based on 95% confidence interval, functional response parameters were significantly different between the host plants solely in parasitism. Results of this study opens new insight in the host parasitoid interactions, subsequently needs further investigation before utilizing it for management and reduction of greenhouse whitefly. PMID:28423420

Estimating the Distribution of the Incubation Periods of Human Avian Influenza A(H7N9) Virus Infections.

PubMed

Virlogeux, Victor; Li, Ming; Tsang, Tim K; Feng, Luzhao; Fang, Vicky J; Jiang, Hui; Wu, Peng; Zheng, Jiandong; Lau, Eric H Y; Cao, Yu; Qin, Ying; Liao, Qiaohong; Yu, Hongjie; Cowling, Benjamin J

2015-10-15

A novel avian influenza virus, influenza A(H7N9), emerged in China in early 2013 and caused severe disease in humans, with infections occurring most frequently after recent exposure to live poultry. The distribution of A(H7N9) incubation periods is of interest to epidemiologists and public health officials, but estimation of the distribution is complicated by interval censoring of exposures. Imputation of the midpoint of intervals was used in some early studies, resulting in estimated mean incubation times of approximately 5 days. In this study, we estimated the incubation period distribution of human influenza A(H7N9) infections using exposure data available for 229 patients with laboratory-confirmed A(H7N9) infection from mainland China. A nonparametric model (Turnbull) and several parametric models accounting for the interval censoring in some exposures were fitted to the data. For the best-fitting parametric model (Weibull), the mean incubation period was 3.4 days (95% confidence interval: 3.0, 3.7) and the variance was 2.9 days; results were very similar for the nonparametric Turnbull estimate. Under the Weibull model, the 95th percentile of the incubation period distribution was 6.5 days (95% confidence interval: 5.9, 7.1). The midpoint approximation for interval-censored exposures led to overestimation of the mean incubation period. Public health observation of potentially exposed persons for 7 days after exposure would be appropriate. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Towards a global harmonized permafrost soil organic carbon stock estimates.

NASA Astrophysics Data System (ADS)

Hugelius, G.; Mishra, U.; Yang, Y.

2017-12-01

Permafrost affected soils store disproportionately large amount of organic carbon stocks due to multiple cryopedogenic processes. Previous permafrost soil organic carbon (SOC) stock estimates used a variety of approaches and reported substantial uncertainty in SOC stocks of permafrost soils. Here, we used spatially referenced data of soil-forming factors (topographic attributes, land cover types, climate, and bedrock geology) and SOC pedon description data (n = 2552) in a regression kriging approach to predict the spatial and vertical heterogeneity of SOC stocks across the Northern Circumpolar and Tibetan permafrost regions. Our approach allowed us to take into account both environmental correlation and spatial autocorrelation to separately estimate SOC stocks and their spatial uncertainties (95% CI) for three depth intervals at 250 m spatial resolution. In Northern Circumpolar region, our results show 1278.1 (1009.33 - 1550.45) Pg C in 0-3 m depth interval, with 542.09 (451.83 - 610.15), 422.46 (306.48 - 550.82), and 313.55 (251.02 - 389.48) Pg C in 0 - 1, 1 - 2, and 2 - 3 m depth intervals, respectively. In Tibetan region, our results show 26.68 (9.82 - 79.92) Pg C in 0 - 3 m depth interval, with 13.98 (6.2 - 32.96), 6.49 (1.73 - 25.86), and 6.21 (1.889 - 20.90) Pg C in 0 - 1, 1 - 2, and 2 - 3 m depth intervals, respectively. Our estimates show large spatial variability (50 - 100% coefficient of variation, depending upon the study region and depth interval) and higher uncertainty range in comparison to existing estimates. We will present the observed controls of different environmental factors on SOC at the AGU meeting.

Estimation of Flood Discharges at Selected Recurrence Intervals for Streams in New Hampshire

USGS Publications Warehouse

Olson, Scott A.

2009-01-01

This report provides estimates of flood discharges at selected recurrence intervals for streamgages in and adjacent to New Hampshire and equations for estimating flood discharges at recurrence intervals of 2-, 5-, 10-, 25-, 50-, 100-, and 500-years for ungaged, unregulated, rural streams in New Hampshire. The equations were developed using generalized least-squares regression. Flood-frequency and drainage-basin characteristics from 117 streamgages were used in developing the equations. The drainage-basin characteristics used as explanatory variables in the regression equations include drainage area, mean April precipitation, percentage of wetland area, and main channel slope. The average standard error of prediction for estimating the 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence interval flood discharges with these equations are 30.0, 30.8, 32.0, 34.2, 36.0, 38.1, and 43.4 percent, respectively. Flood discharges at selected recurrence intervals for selected streamgages were computed following the guidelines in Bulletin 17B of the U.S. Interagency Advisory Committee on Water Data. To determine the flood-discharge exceedence probabilities at streamgages in New Hampshire, a new generalized skew coefficient map covering the State was developed. The standard error of the data on new map is 0.298. To improve estimates of flood discharges at selected recurrence intervals for 20 streamgages with short-term records (10 to 15 years), record extension using the two-station comparison technique was applied. The two-station comparison method uses data from a streamgage with long-term record to adjust the frequency characteristics at a streamgage with a short-term record. A technique for adjusting a flood-discharge frequency curve computed from a streamgage record with results from the regression equations is described in this report. Also, a technique is described for estimating flood discharge at a selected recurrence interval for an ungaged site upstream or downstream from a streamgage using a drainage-area adjustment. The final regression equations and the flood-discharge frequency data used in this study will be available in StreamStats. StreamStats is a World Wide Web application providing automated regression-equation solutions for user-selected sites on streams.

Cox model with interval-censored covariate in cohort studies.

PubMed

Ahn, Soohyun; Lim, Johan; Paik, Myunghee Cho; Sacco, Ralph L; Elkind, Mitchell S

2018-05-18

In cohort studies the outcome is often time to a particular event, and subjects are followed at regular intervals. Periodic visits may also monitor a secondary irreversible event influencing the event of primary interest, and a significant proportion of subjects develop the secondary event over the period of follow-up. The status of the secondary event serves as a time-varying covariate, but is recorded only at the times of the scheduled visits, generating incomplete time-varying covariates. While information on a typical time-varying covariate is missing for entire follow-up period except the visiting times, the status of the secondary event are unavailable only between visits where the status has changed, thus interval-censored. One may view interval-censored covariate of the secondary event status as missing time-varying covariates, yet missingness is partial since partial information is provided throughout the follow-up period. Current practice of using the latest observed status produces biased estimators, and the existing missing covariate techniques cannot accommodate the special feature of missingness due to interval censoring. To handle interval-censored covariates in the Cox proportional hazards model, we propose an available-data estimator, a doubly robust-type estimator as well as the maximum likelihood estimator via EM algorithm and present their asymptotic properties. We also present practical approaches that are valid. We demonstrate the proposed methods using our motivating example from the Northern Manhattan Study. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Intervals for posttest probabilities: a comparison of 5 methods.

PubMed

Mossman, D; Berger, J O

2001-01-01

Several medical articles discuss methods of constructing confidence intervals for single proportions and the likelihood ratio, but scant attention has been given to the systematic study of intervals for the posterior odds, or the positive predictive value, of a test. The authors describe 5 methods of constructing confidence intervals for posttest probabilities when estimates of sensitivity, specificity, and the pretest probability of a disorder are derived from empirical data. They then evaluate each method to determine how well the intervals' coverage properties correspond to their nominal value. When the estimates of pretest probabilities, sensitivity, and specificity are derived from more than 80 subjects and are not close to 0 or 1, all methods generate intervals with appropriate coverage properties. When these conditions are not met, however, the best-performing method is an objective Bayesian approach implemented by a simple simulation using a spreadsheet. Physicians and investigators can generate accurate confidence intervals for posttest probabilities in small-sample situations using the objective Bayesian approach.

Efficient estimation of abundance for patchily distributed populations via two-phase, adaptive sampling.

USGS Publications Warehouse

Conroy, M.J.; Runge, J.P.; Barker, R.J.; Schofield, M.R.; Fonnesbeck, C.J.

2008-01-01

Many organisms are patchily distributed, with some patches occupied at high density, others at lower densities, and others not occupied. Estimation of overall abundance can be difficult and is inefficient via intensive approaches such as capture-mark-recapture (CMR) or distance sampling. We propose a two-phase sampling scheme and model in a Bayesian framework to estimate abundance for patchily distributed populations. In the first phase, occupancy is estimated by binomial detection samples taken on all selected sites, where selection may be of all sites available, or a random sample of sites. Detection can be by visual surveys, detection of sign, physical captures, or other approach. At the second phase, if a detection threshold is achieved, CMR or other intensive sampling is conducted via standard procedures (grids or webs) to estimate abundance. Detection and CMR data are then used in a joint likelihood to model probability of detection in the occupancy sample via an abundance-detection model. CMR modeling is used to estimate abundance for the abundance-detection relationship, which in turn is used to predict abundance at the remaining sites, where only detection data are collected. We present a full Bayesian modeling treatment of this problem, in which posterior inference on abundance and other parameters (detection, capture probability) is obtained under a variety of assumptions about spatial and individual sources of heterogeneity. We apply the approach to abundance estimation for two species of voles (Microtus spp.) in Montana, USA. We also use a simulation study to evaluate the frequentist properties of our procedure given known patterns in abundance and detection among sites as well as design criteria. For most population characteristics and designs considered, bias and mean-square error (MSE) were low, and coverage of true parameter values by Bayesian credibility intervals was near nominal. Our two-phase, adaptive approach allows efficient estimation of abundance of rare and patchily distributed species and is particularly appropriate when sampling in all patches is impossible, but a global estimate of abundance is required.

Regression analysis of case K interval-censored failure time data in the presence of informative censoring.

PubMed

Wang, Peijie; Zhao, Hui; Sun, Jianguo

2016-12-01

Interval-censored failure time data occur in many fields such as demography, economics, medical research, and reliability and many inference procedures on them have been developed (Sun, 2006; Chen, Sun, and Peace, 2012). However, most of the existing approaches assume that the mechanism that yields interval censoring is independent of the failure time of interest and it is clear that this may not be true in practice (Zhang et al., 2007; Ma, Hu, and Sun, 2015). In this article, we consider regression analysis of case K interval-censored failure time data when the censoring mechanism may be related to the failure time of interest. For the problem, an estimated sieve maximum-likelihood approach is proposed for the data arising from the proportional hazards frailty model and for estimation, a two-step procedure is presented. In the addition, the asymptotic properties of the proposed estimators of regression parameters are established and an extensive simulation study suggests that the method works well. Finally, we apply the method to a set of real interval-censored data that motivated this study. © 2016, The International Biometric Society.

Considerations about expected a posteriori estimation in adaptive testing: adaptive a priori, adaptive correction for bias, and adaptive integration interval.

PubMed

Raiche, Gilles; Blais, Jean-Guy

2009-01-01

In a computerized adaptive test, we would like to obtain an acceptable precision of the proficiency level estimate using an optimal number of items. Unfortunately, decreasing the number of items is accompanied by a certain degree of bias when the true proficiency level differs significantly from the a priori estimate. The authors suggest that it is possible to reduced the bias, and even the standard error of the estimate, by applying to each provisional estimation one or a combination of the following strategies: adaptive correction for bias proposed by Bock and Mislevy (1982), adaptive a priori estimate, and adaptive integration interval.

Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting

PubMed Central

Dazard, Jean-Eudes; Ishwaran, Hemant; Mehlotra, Rajeev; Weinberg, Aaron; Zimmerman, Peter

2018-01-01

Unraveling interactions among variables such as genetic, clinical, demographic and environmental factors is essential to understand the development of common and complex diseases. To increase the power to detect such variables interactions associated with clinical time-to-events outcomes, we borrowed established concepts from random survival forest (RSF) models. We introduce a novel RSF-based pairwise interaction estimator and derive a randomization method with bootstrap confidence intervals for inferring interaction significance. Using various linear and nonlinear time-to-events survival models in simulation studies, we first show the efficiency of our approach: true pairwise interaction-effects between variables are uncovered, while they may not be accompanied with their corresponding main-effects, and may not be detected by standard semi-parametric regression modeling and test statistics used in survival analysis. Moreover, using a RSF-based cross-validation scheme for generating prediction estimators, we show that informative predictors may be inferred. We applied our approach to an HIV cohort study recording key host gene polymorphisms and their association with HIV change of tropism or AIDS progression. Altogether, this shows how linear or nonlinear pairwise statistical interactions of variables may be efficiently detected with a predictive value in observational studies with time-to-event outcomes. PMID:29453930

Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting.

PubMed

Dazard, Jean-Eudes; Ishwaran, Hemant; Mehlotra, Rajeev; Weinberg, Aaron; Zimmerman, Peter

2018-02-17

Unraveling interactions among variables such as genetic, clinical, demographic and environmental factors is essential to understand the development of common and complex diseases. To increase the power to detect such variables interactions associated with clinical time-to-events outcomes, we borrowed established concepts from random survival forest (RSF) models. We introduce a novel RSF-based pairwise interaction estimator and derive a randomization method with bootstrap confidence intervals for inferring interaction significance. Using various linear and nonlinear time-to-events survival models in simulation studies, we first show the efficiency of our approach: true pairwise interaction-effects between variables are uncovered, while they may not be accompanied with their corresponding main-effects, and may not be detected by standard semi-parametric regression modeling and test statistics used in survival analysis. Moreover, using a RSF-based cross-validation scheme for generating prediction estimators, we show that informative predictors may be inferred. We applied our approach to an HIV cohort study recording key host gene polymorphisms and their association with HIV change of tropism or AIDS progression. Altogether, this shows how linear or nonlinear pairwise statistical interactions of variables may be efficiently detected with a predictive value in observational studies with time-to-event outcomes.

Improving the water use efficiency of olive trees growing in water harvesting systems

NASA Astrophysics Data System (ADS)

Berliner, Pedro; Leake, Salomon; Carmi, Gennady; Agam, Nurit

2017-04-01

Water is a primary limiting factor for agricultural development in many arid and semi-arid regions in which a runoff generation is a rather frequent event. If conveyed to dyke surrounded plots and ponded, runoff water can thereafter be used for tree production. One of the most promising runoff collection configurations is that of micro-catchments in which water is collected close to the area in which runoff was generated and stored in adjacent shallow pits. The objective of this work was to assess the effect of the geometry of runoff water collection area (shallow pit or trench) on direct evaporative water losses and on the water use efficiency of olive trees grown in them. The study was conducted during the summer of 2013 and 2014. In this study regular micro-catchments with basins of 9 m2 (3 x 3 m) by 0.1 m deep were compared with trenches of one meter deep and one meter wide. Each configuration was replicated three times. One tree was planted in each shallow basin and the distance between trees in the 12 m long trench was four meters. Access tubes for neutron probes were installed in the micro-catchments and trenches (four and seven, respectively) to depths of 2.5 m. Soil water content in the soil profile was monitored periodically throughout drying periods in between simulated runoff events. Transpiration of the trees was estimated from half-hourly sap flow measurements using a Granier system. Total transpiration fluxes were computed for time intervals corresponding to consecutive soil water measurements. During the first year, a large runoff event was simulated by applying once four cubic meters to each plot; and in the second year the same volume of water was split into four applications, simulating a series of small runoff events. In both geometries, trees received the same amount of water per tree. Evaporation from trenches and micro-catchments was estimated as the difference between evapotranspiration obtained computing the differences in total soil water content between two consecutive measurements and transpiration for this interval estimated from sap flow measurements. In both years the evaporation from micro-catchments was significantly larger than that of trenches. The fractional loss due to evaporation from the total applied water for the second year for example, was 53% and 22% for micro-catchments and trenches, respectively. This indicates that a trench geometry reduces the amount of water lost to direct evaporation from the soil, and is thus more efficient in utilizing harvested runoff water.

On the Modeling of the Residual Effects of the Clock Behavior and the Atmosphere Effects in the Analysis of VLBI Data

NASA Astrophysics Data System (ADS)

Bo, Zhang; Li, Jin-Ling; Wang, Guan-Gli

2002-01-01

We checked the dependence of the estimation of parameters on the choice of piecewise interval in the continuous piecewise linear modeling of the residual clock and atmosphere effects by single analysis of 27 VLBI experiments involving Shanghai station (Seshan 25m). The following are tentatively shown: (1) Different choices of the piecewise interval lead to differences in the estimation of station coordinates and in the weighted root mean squares ( wrms ) of the delay residuals, which can be of the order of centimeters or dozens of picoseconds respectively. So the choice of piecewise interval should not be arbitrary . (2) The piecewise interval should not be too long, otherwise the short - term variations in the residual clock and atmospheric effects can not be properly modeled. While in order to maintain enough degrees of freedom in parameter estimation, the interval can not be too short, otherwise the normal equation may become near or solely singular and the noises can not be constrained as well. Therefore the choice of the interval should be within some reasonable range. (3) Since the conditions of clock and atmosphere are different from experiment to experiment and from station to station, the reasonable range of the piecewise interval should be tested and chosen separately for each experiment as well as for each station by real data analysis. This is really arduous work in routine data analysis. (4) Generally speaking, with the default interval for clock as 60min, the reasonable range of piecewise interval for residual atmospheric effect modeling is between 10min to 40min, while with the default interval for atmosphere as 20min, that for residual clock behavior is between 20min to 100min.

Microcephaly Case Fatality Rate Associated with Zika Virus Infection in Brazil: Current Estimates.

PubMed

Cunha, Antonio José Ledo Alves da; de Magalhães-Barbosa, Maria Clara; Lima-Setta, Fernanda; Medronho, Roberto de Andrade; Prata-Barbosa, Arnaldo

2017-05-01

Considering the currently confirmed cases of microcephaly and related deaths associated with Zika virus in Brazil, the estimated case fatality rate is 8.3% (95% confidence interval: 7.2-9.6). However, a third of the reported cases remain under investigation. If the confirmation rates of cases and deaths are the same in the future, the estimated case fatality rate will be as high as 10.5% (95% confidence interval: 9.5-11.7).

Aortic stiffness and the balance between cardiac oxygen supply and demand: the Rotterdam Study.

PubMed

Guelen, Ilja; Mattace-Raso, Francesco Us; van Popele, Nicole M; Westerhof, Berend E; Hofman, Albert; Witteman, Jacqueline Cm; Bos, Willem Jan W

2008-06-01

Aortic stiffness is an independent predictor of cardiovascular morbidity and mortality. We investigated whether aortic stiffness, estimated as aortic pulse wave velocity, is associated with decreased perfusion pressure estimated as the cardiac oxygen supply potential. Aortic stiffness and aortic pressure waves, reconstructed from finger blood pressure waves, were obtained in 2490 older adults within the framework of the Rotterdam Study, a large population-based study. Cardiac oxygen supply and demand were estimated using pulse wave analysis techniques, and related to aortic stiffness by linear regression analyses after adjustment for age, sex, mean arterial pressure and heart rate. Cardiac oxygen demand, estimated as the Systolic Pressure Time Index and the Rate Pressure Product, increased with increasing aortic stiffness [0.27 mmHg s (95% confidence interval: 0.21; 0.34)] and [42.2 mmHg/min (95% confidence interval: 34.1; 50.3)], respectively. Cardiac oxygen supply potential estimated as the Diastolic Pressure Time Index decreased [-0.70 mmHg s (95% confidence interval: -0.86; -0.54)] with aortic stiffening. Accordingly, the supply/demand ratio Diastolic Pressure Time Index/Systolic Pressure Time Index -1.11 (95% confidence interval: -0.14; -0.009) decreased with increasing aortic stiffness. Aortic stiffness is associated with estimates of increased cardiac oxygen demand and a decreased cardiac oxygen supply potential. These results may offer additional explanation for the relation between aortic stiffness and cardiovascular morbidity and mortality.

Optimal methods for fitting probability distributions to propagule retention time in studies of zoochorous dispersal.

PubMed

Viana, Duarte S; Santamaría, Luis; Figuerola, Jordi

2016-02-01

Propagule retention time is a key factor in determining propagule dispersal distance and the shape of "seed shadows". Propagules dispersed by animal vectors are either ingested and retained in the gut until defecation or attached externally to the body until detachment. Retention time is a continuous variable, but it is commonly measured at discrete time points, according to pre-established sampling time-intervals. Although parametric continuous distributions have been widely fitted to these interval-censored data, the performance of different fitting methods has not been evaluated. To investigate the performance of five different fitting methods, we fitted parametric probability distributions to typical discretized retention-time data with known distribution using as data-points either the lower, mid or upper bounds of sampling intervals, as well as the cumulative distribution of observed values (using either maximum likelihood or non-linear least squares for parameter estimation); then compared the estimated and original distributions to assess the accuracy of each method. We also assessed the robustness of these methods to variations in the sampling procedure (sample size and length of sampling time-intervals). Fittings to the cumulative distribution performed better for all types of parametric distributions (lognormal, gamma and Weibull distributions) and were more robust to variations in sample size and sampling time-intervals. These estimated distributions had negligible deviations of up to 0.045 in cumulative probability of retention times (according to the Kolmogorov-Smirnov statistic) in relation to original distributions from which propagule retention time was simulated, supporting the overall accuracy of this fitting method. In contrast, fitting the sampling-interval bounds resulted in greater deviations that ranged from 0.058 to 0.273 in cumulative probability of retention times, which may introduce considerable biases in parameter estimates. We recommend the use of cumulative probability to fit parametric probability distributions to propagule retention time, specifically using maximum likelihood for parameter estimation. Furthermore, the experimental design for an optimal characterization of unimodal propagule retention time should contemplate at least 500 recovered propagules and sampling time-intervals not larger than the time peak of propagule retrieval, except in the tail of the distribution where broader sampling time-intervals may also produce accurate fits.

Grouping methods for estimating the prevalences of rare traits from complex survey data that preserve confidentiality of respondents.

PubMed

Hyun, Noorie; Gastwirth, Joseph L; Graubard, Barry I

2018-03-26

Originally, 2-stage group testing was developed for efficiently screening individuals for a disease. In response to the HIV/AIDS epidemic, 1-stage group testing was adopted for estimating prevalences of a single or multiple traits from testing groups of size q, so individuals were not tested. This paper extends the methodology of 1-stage group testing to surveys with sample weighted complex multistage-cluster designs. Sample weighted-generalized estimating equations are used to estimate the prevalences of categorical traits while accounting for the error rates inherent in the tests. Two difficulties arise when using group testing in complex samples: (1) How does one weight the results of the test on each group as the sample weights will differ among observations in the same group. Furthermore, if the sample weights are related to positivity of the diagnostic test, then group-level weighting is needed to reduce bias in the prevalence estimation; (2) How does one form groups that will allow accurate estimation of the standard errors of prevalence estimates under multistage-cluster sampling allowing for intracluster correlation of the test results. We study 5 different grouping methods to address the weighting and cluster sampling aspects of complex designed samples. Finite sample properties of the estimators of prevalences, variances, and confidence interval coverage for these grouping methods are studied using simulations. National Health and Nutrition Examination Survey data are used to illustrate the methods. Copyright © 2018 John Wiley & Sons, Ltd.

Defining Incident Cases of Epilepsy in Administrative Data

PubMed Central

Bakaki, Paul M.; Koroukian, Siran M.; Jackson, Leila W.; Albert, Jeffrey M.; Kaiboriboon, Kitti

2013-01-01

Purpose To determine the minimum enrollment duration for identifying incident cases of epilepsy in administrative data. Methods We performed a retrospective dynamic cohort study using Ohio Medicaid data from 1992–2006 to identify a total of 5,037 incident epilepsy cases who had at least 1 year of follow-up prior to epilepsy diagnosis (epilepsy-free interval). The incidence for epilepsy-free intervals from 1 to 8 years, overall and stratified by pre-existing disability status, was examined. The graphical approach between the slopes of incidence estimates and the epilepsy-free intervals was used to identify the minimum epilepsy-free interval that minimized misclassification of prevalent as incident epilepsy cases. Results As the length of epilepsy-free interval increased, the incidence rates decreased. A graphical plot showed that the decline in incidence of epilepsy became nearly flat beyond the third epilepsy-free interval. Conclusion The minimum of 3-year epilepsy-free interval is needed to differentiate incident from prevalent cases in administrative data. Shorter or longer epilepsy-free intervals could result in over- or under-estimation of epilepsy incidence. PMID:23791310

Comparative statistics of Garman-Klass, Parkinson, Roger-Satchell and bridge estimators

NASA Astrophysics Data System (ADS)

Lapinova, S.; Saichev, A.

2017-01-01

Comparative statistical properties of Parkinson, Garman-Klass, Roger-Satchell and bridge oscillation estimators are discussed. Point and interval estimations, related with mentioned estimators are considered.

Current and efficiency of Brownian particles under oscillating forces in entropic barriers

NASA Astrophysics Data System (ADS)

Nutku, Ferhat; Aydιner, Ekrem

2015-04-01

In this study, considering the temporarily unbiased force and different forms of oscillating forces, we investigate the current and efficiency of Brownian particles in an entropic tube structure and present the numerically obtained results. We show that different force forms give rise to different current and efficiency profiles in different optimized parameter intervals. We find that an unbiased oscillating force and an unbiased temporal force lead to the current and efficiency, which are dependent on these parameters. We also observe that the current and efficiency caused by temporal and different oscillating forces have maximum and minimum values in different parameter intervals. We conclude that the current or efficiency can be controlled dynamically by adjusting the parameters of entropic barriers and applied force. Project supported by the Funds from Istanbul University (Grant No. 45662).

Time prediction of failure a type of lamps by using general composite hazard rate model

NASA Astrophysics Data System (ADS)

Riaman; Lesmana, E.; Subartini, B.; Supian, S.

2018-03-01

This paper discusses the basic survival model estimates to obtain the average predictive value of lamp failure time. This estimate is for the parametric model, General Composite Hazard Level Model. The random time variable model used is the exponential distribution model, as the basis, which has a constant hazard function. In this case, we discuss an example of survival model estimation for a composite hazard function, using an exponential model as its basis. To estimate this model is done by estimating model parameters, through the construction of survival function and empirical cumulative function. The model obtained, will then be used to predict the average failure time of the model, for the type of lamp. By grouping the data into several intervals and the average value of failure at each interval, then calculate the average failure time of a model based on each interval, the p value obtained from the tes result is 0.3296.

Robust Coefficients Alpha and Omega and Confidence Intervals With Outlying Observations and Missing Data: Methods and Software.

PubMed

Zhang, Zhiyong; Yuan, Ke-Hai

2016-06-01

Cronbach's coefficient alpha is a widely used reliability measure in social, behavioral, and education sciences. It is reported in nearly every study that involves measuring a construct through multiple items. With non-tau-equivalent items, McDonald's omega has been used as a popular alternative to alpha in the literature. Traditional estimation methods for alpha and omega often implicitly assume that data are complete and normally distributed. This study proposes robust procedures to estimate both alpha and omega as well as corresponding standard errors and confidence intervals from samples that may contain potential outlying observations and missing values. The influence of outlying observations and missing data on the estimates of alpha and omega is investigated through two simulation studies. Results show that the newly developed robust method yields substantially improved alpha and omega estimates as well as better coverage rates of confidence intervals than the conventional nonrobust method. An R package coefficientalpha is developed and demonstrated to obtain robust estimates of alpha and omega.

Robust Coefficients Alpha and Omega and Confidence Intervals With Outlying Observations and Missing Data

PubMed Central

Zhang, Zhiyong; Yuan, Ke-Hai

2015-01-01

Cronbach’s coefficient alpha is a widely used reliability measure in social, behavioral, and education sciences. It is reported in nearly every study that involves measuring a construct through multiple items. With non-tau-equivalent items, McDonald’s omega has been used as a popular alternative to alpha in the literature. Traditional estimation methods for alpha and omega often implicitly assume that data are complete and normally distributed. This study proposes robust procedures to estimate both alpha and omega as well as corresponding standard errors and confidence intervals from samples that may contain potential outlying observations and missing values. The influence of outlying observations and missing data on the estimates of alpha and omega is investigated through two simulation studies. Results show that the newly developed robust method yields substantially improved alpha and omega estimates as well as better coverage rates of confidence intervals than the conventional nonrobust method. An R package coefficientalpha is developed and demonstrated to obtain robust estimates of alpha and omega. PMID:29795870

Hydraulic Tomography in Fractured Sedimentary Rocks to Estimate High-Resolution 3-D Distribution of Hydraulic Conductivity

NASA Astrophysics Data System (ADS)

Tiedeman, C. R.; Barrash, W.; Thrash, C. J.; Patterson, J.; Johnson, C. D.

2016-12-01

Hydraulic tomography was performed in a 100 m2 by 20 m thick volume of contaminated fractured mudstones at the former Naval Air Warfare Center (NAWC) in the Newark Basin, New Jersey, with the objective of estimating the detailed distribution of hydraulic conductivity (K). Characterizing the fine-scale K variability is important for designing effective remediation strategies in complex geologic settings such as fractured rock. In the tomography experiment, packers isolated two to six intervals in each of seven boreholes in the volume of investigation, and fiber-optic pressure transducers enabled collection of high-resolution drawdown observations. A hydraulic tomography dataset was obtained by conducting multiple aquifer tests in which a given isolated well interval was pumped and drawdown was monitored in all other intervals. The collective data from all tests display a wide range of behavior indicative of highly heterogeneous K within the tested volume, such as: drawdown curves for different intervals crossing one another on drawdown-time plots; unique drawdown curve shapes for certain intervals; and intervals with negligible drawdown adjacent to intervals with large drawdown. Tomographic inversion of data from 15 tests conducted in the first field season focused on estimating the K distribution at a scale of 1 m3 over approximately 25% of the investigated volume, where observation density was greatest. The estimated K field is consistent with prior geologic, geophysical, and hydraulic information, including: highly variable K within bedding-plane-parting fractures that are the primary flow and transport paths at NAWC, connected high-K features perpendicular to bedding, and a spatially heterogeneous distribution of low-K rock matrix and closed fractures. Subsequent tomographic testing was conducted in the second field season, with the region of high observation density expanded to cover a greater volume of the wellfield.

Uncertainty and inferred reserve estimates; the 1995 National Assessment

USGS Publications Warehouse

Attanasi, E.D.; Coburn, Timothy C.

2003-01-01

Inferred reserves are expected additions to proved reserves of oil and gas fields discovered as of a certain date. Inferred reserves accounted for 65 percent of the total oil and 34 percent of the total gas assessed in the U.S. Geological Survey's 1995 National Assessment of oil and gas in onshore and State offshore areas. The assessment predicted that over the 80-year period from 1992 through 2071, the sizes of pre-1992 discoveries in the lower 48 onshore and State offshore areas will increase by 48 billion barrels of oil (BBO) and 313 trillion cubic feet of wet gas (TCF). At that time, only point estimates were reported. This study presents a scheme to compute confidence intervals for these estimates. The recentered 90 percent confidence interval for the estimated inferred oil of 48 BBO is 25 BBO and 82 BBO. Similarly, the endpoints of the confidence interval about inferred reserve estimate of 313 TCF are 227 TCF and 439 TCF. The range of the estimates provides a basis for development of scenarios for projecting reserve additions and ultimately oil and gas production, information important to energy policy analysis.

A Model-Based Approach to Infer Shifts in Regional Fire Regimes Over Time Using Sediment Charcoal Records

NASA Astrophysics Data System (ADS)

Itter, M.; Finley, A. O.; Hooten, M.; Higuera, P. E.; Marlon, J. R.; McLachlan, J. S.; Kelly, R.

2016-12-01

Sediment charcoal records are used in paleoecological analyses to identify individual local fire events and to estimate fire frequency and regional biomass burned at centennial to millenial time scales. Methods to identify local fire events based on sediment charcoal records have been well developed over the past 30 years, however, an integrated statistical framework for fire identification is still lacking. We build upon existing paleoecological methods to develop a hierarchical Bayesian point process model for local fire identification and estimation of fire return intervals. The model is unique in that it combines sediment charcoal records from multiple lakes across a region in a spatially-explicit fashion leading to estimation of a joint, regional fire return interval in addition to lake-specific local fire frequencies. Further, the model estimates a joint regional charcoal deposition rate free from the effects of local fires that can be used as a measure of regional biomass burned over time. Finally, the hierarchical Bayesian approach allows for tractable error propagation such that estimates of fire return intervals reflect the full range of uncertainty in sediment charcoal records. Specific sources of uncertainty addressed include sediment age models, the separation of local versus regional charcoal sources, and generation of a composite charcoal record The model is applied to sediment charcoal records from a dense network of lakes in the Yukon Flats region of Alaska. The multivariate joint modeling approach results in improved estimates of regional charcoal deposition with reduced uncertainty in the identification of individual fire events and local fire return intervals compared to individual lake approaches. Modeled individual-lake fire return intervals range from 100 to 500 years with a regional interval of roughly 200 years. Regional charcoal deposition to the network of lakes is correlated up to 50 kilometers. Finally, the joint regional charcoal deposition rate exhibits changes over time coincident with major climatic and vegetation shifts over the past 10,000 years. Ongoing work will use the regional charcoal deposition rate to estimate changes in biomass burned as a function of climate variability and regional vegetation pattern.

A comparison of confidence interval methods for the intraclass correlation coefficient in community-based cluster randomization trials with a binary outcome.

PubMed

Braschel, Melissa C; Svec, Ivana; Darlington, Gerarda A; Donner, Allan

2016-04-01

Many investigators rely on previously published point estimates of the intraclass correlation coefficient rather than on their associated confidence intervals to determine the required size of a newly planned cluster randomized trial. Although confidence interval methods for the intraclass correlation coefficient that can be applied to community-based trials have been developed for a continuous outcome variable, fewer methods exist for a binary outcome variable. The aim of this study is to evaluate confidence interval methods for the intraclass correlation coefficient applied to binary outcomes in community intervention trials enrolling a small number of large clusters. Existing methods for confidence interval construction are examined and compared to a new ad hoc approach based on dividing clusters into a large number of smaller sub-clusters and subsequently applying existing methods to the resulting data. Monte Carlo simulation is used to assess the width and coverage of confidence intervals for the intraclass correlation coefficient based on Smith's large sample approximation of the standard error of the one-way analysis of variance estimator, an inverted modified Wald test for the Fleiss-Cuzick estimator, and intervals constructed using a bootstrap-t applied to a variance-stabilizing transformation of the intraclass correlation coefficient estimate. In addition, a new approach is applied in which clusters are randomly divided into a large number of smaller sub-clusters with the same methods applied to these data (with the exception of the bootstrap-t interval, which assumes large cluster sizes). These methods are also applied to a cluster randomized trial on adolescent tobacco use for illustration. When applied to a binary outcome variable in a small number of large clusters, existing confidence interval methods for the intraclass correlation coefficient provide poor coverage. However, confidence intervals constructed using the new approach combined with Smith's method provide nominal or close to nominal coverage when the intraclass correlation coefficient is small (<0.05), as is the case in most community intervention trials. This study concludes that when a binary outcome variable is measured in a small number of large clusters, confidence intervals for the intraclass correlation coefficient may be constructed by dividing existing clusters into sub-clusters (e.g. groups of 5) and using Smith's method. The resulting confidence intervals provide nominal or close to nominal coverage across a wide range of parameters when the intraclass correlation coefficient is small (<0.05). Application of this method should provide investigators with a better understanding of the uncertainty associated with a point estimator of the intraclass correlation coefficient used for determining the sample size needed for a newly designed community-based trial. © The Author(s) 2015.

Time-resolved speckle effects on the estimation of laser-pulse arrival times

NASA Technical Reports Server (NTRS)

Tsai, B.-M.; Gardner, C. S.

1985-01-01

A maximum-likelihood (ML) estimator of the pulse arrival in laser ranging and altimetry is derived for the case of a pulse distorted by shot noise and time-resolved speckle. The performance of the estimator is evaluated for pulse reflections from flat diffuse targets and compared with the performance of a suboptimal centroid estimator and a suboptimal Bar-David ML estimator derived under the assumption of no speckle. In the large-signal limit the accuracy of the estimator was found to improve as the width of the receiver observational interval increases. The timing performance of the estimator is expected to be highly sensitive to background noise when the received pulse energy is high and the receiver observational interval is large. Finally, in the speckle-limited regime the ML estimator performs considerably better than the suboptimal estimators.

A Comparison of Momentary Time Sampling and Partial-Interval Recording for Assessment of Effects of Social Skills Training

ERIC Educational Resources Information Center

Radley, Keith C.; O'Handley, Roderick D.; Labrot, Zachary C.

2015-01-01

Assessment in social skills training often utilizes procedures such as partial-interval recording (PIR) and momentary time sampling (MTS) to estimate changes in duration in social engagements due to intervention. Although previous research suggests PIR to be more inaccurate than MTS in estimating levels of behavior, treatment analysis decisions…

An Integrated Theory of Prospective Time Interval Estimation: The Role of Cognition, Attention, and Learning

ERIC Educational Resources Information Center

Taatgen, Niels A.; van Rijn, Hedderik; Anderson, John

2007-01-01

A theory of prospective time perception is introduced and incorporated as a module in an integrated theory of cognition, thereby extending existing theories and allowing predictions about attention and learning. First, a time perception module is established by fitting existing datasets (interval estimation and bisection and impact of secondary…

Sample Size Calculation for Estimating or Testing a Nonzero Squared Multiple Correlation Coefficient

ERIC Educational Resources Information Center

Krishnamoorthy, K.; Xia, Yanping

2008-01-01

The problems of hypothesis testing and interval estimation of the squared multiple correlation coefficient of a multivariate normal distribution are considered. It is shown that available one-sided tests are uniformly most powerful, and the one-sided confidence intervals are uniformly most accurate. An exact method of calculating sample size to…

Interval Estimation of Revision Effect on Scale Reliability via Covariance Structure Modeling

ERIC Educational Resources Information Center

Raykov, Tenko

2009-01-01

A didactic discussion of a procedure for interval estimation of change in scale reliability due to revision is provided, which is developed within the framework of covariance structure modeling. The method yields ranges of plausible values for the population gain or loss in reliability of unidimensional composites, which results from deletion or…

A Direct Latent Variable Modeling Based Method for Point and Interval Estimation of Coefficient Alpha

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.

2015-01-01

A direct approach to point and interval estimation of Cronbach's coefficient alpha for multiple component measuring instruments is outlined. The procedure is based on a latent variable modeling application with widely circulated software. As a by-product, using sample data the method permits ascertaining whether the population discrepancy…

L-statistics for Repeated Measurements Data With Application to Trimmed Means, Quantiles and Tolerance Intervals.

PubMed

Assaad, Houssein I; Choudhary, Pankaj K

2013-01-01

The L -statistics form an important class of estimators in nonparametric statistics. Its members include trimmed means and sample quantiles and functions thereof. This article is devoted to theory and applications of L -statistics for repeated measurements data, wherein the measurements on the same subject are dependent and the measurements from different subjects are independent. This article has three main goals: (a) Show that the L -statistics are asymptotically normal for repeated measurements data. (b) Present three statistical applications of this result, namely, location estimation using trimmed means, quantile estimation and construction of tolerance intervals. (c) Obtain a Bahadur representation for sample quantiles. These results are generalizations of similar results for independently and identically distributed data. The practical usefulness of these results is illustrated by analyzing a real data set involving measurement of systolic blood pressure. The properties of the proposed point and interval estimators are examined via simulation.

Testing equality and interval estimation in binary responses when high dose cannot be used first under a three-period crossover design.

PubMed

Lui, Kung-Jong; Chang, Kuang-Chao

2015-01-01

When comparing two doses of a new drug with a placebo, we may consider using a crossover design subject to the condition that the high dose cannot be administered before the low dose. Under a random-effects logistic regression model, we focus our attention on dichotomous responses when the high dose cannot be used first under a three-period crossover trial. We derive asymptotic test procedures for testing equality between treatments. We further derive interval estimators to assess the magnitude of the relative treatment effects. We employ Monte Carlo simulation to evaluate the performance of these test procedures and interval estimators in a variety of situations. We use the data taken as a part of trial comparing two different doses of an analgesic with a placebo for the relief of primary dysmenorrhea to illustrate the use of the proposed test procedures and estimators.

Interval Management with Spacing to Parallel Dependent Runways (IMSPIDR) Experiment and Results

NASA Technical Reports Server (NTRS)

Baxley, Brian T.; Swieringa, Kurt A.; Capron, William R.

2012-01-01

An area in aviation operations that may offer an increase in efficiency is the use of continuous descent arrivals (CDA), especially during dependent parallel runway operations. However, variations in aircraft descent angle and speed can cause inaccuracies in estimated time of arrival calculations, requiring an increase in the size of the buffer between aircraft. This in turn reduces airport throughput and limits the use of CDAs during high-density operations, particularly to dependent parallel runways. The Interval Management with Spacing to Parallel Dependent Runways (IMSPiDR) concept uses a trajectory-based spacing tool onboard the aircraft to achieve by the runway an air traffic control assigned spacing interval behind the previous aircraft. This paper describes the first ever experiment and results of this concept at NASA Langley. Pilots flew CDAs to the Dallas Fort-Worth airport using airspeed calculations from the spacing tool to achieve either a Required Time of Arrival (RTA) or Interval Management (IM) spacing interval at the runway threshold. Results indicate flight crews were able to land aircraft on the runway with a mean of 2 seconds and less than 4 seconds standard deviation of the air traffic control assigned time, even in the presence of forecast wind error and large time delay. Statistically significant differences in delivery precision and number of speed changes as a function of stream position were observed, however, there was no trend to the difference and the error did not increase during the operation. Two areas the flight crew indicated as not acceptable included the additional number of speed changes required during the wind shear event, and issuing an IM clearance via data link while at low altitude. A number of refinements and future spacing algorithm capabilities were also identified.

Confidence intervals in Flow Forecasting by using artificial neural networks

NASA Astrophysics Data System (ADS)

Panagoulia, Dionysia; Tsekouras, George

2014-05-01

One of the major inadequacies in implementation of Artificial Neural Networks (ANNs) for flow forecasting is the development of confidence intervals, because the relevant estimation cannot be implemented directly, contrasted to the classical forecasting methods. The variation in the ANN output is a measure of uncertainty in the model predictions based on the training data set. Different methods for uncertainty analysis, such as bootstrap, Bayesian, Monte Carlo, have already proposed for hydrologic and geophysical models, while methods for confidence intervals, such as error output, re-sampling, multi-linear regression adapted to ANN have been used for power load forecasting [1-2]. The aim of this paper is to present the re-sampling method for ANN prediction models and to develop this for flow forecasting of the next day. The re-sampling method is based on the ascending sorting of the errors between real and predicted values for all input vectors. The cumulative sample distribution function of the prediction errors is calculated and the confidence intervals are estimated by keeping the intermediate value, rejecting the extreme values according to the desired confidence levels, and holding the intervals symmetrical in probability. For application of the confidence intervals issue, input vectors are used from the Mesochora catchment in western-central Greece. The ANN's training algorithm is the stochastic training back-propagation process with decreasing functions of learning rate and momentum term, for which an optimization process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, the initial values and time parameters of learning rate and momentum term etc. Input variables are historical data of previous days, such as flows, nonlinearly weather related temperatures and nonlinearly weather related rainfalls based on correlation analysis between the under prediction flow and each implicit input variable of different ANN structures [3]. The performance of each ANN structure is evaluated by the voting analysis based on eleven criteria, which are the root mean square error (RMSE), the correlation index (R), the mean absolute percentage error (MAPE), the mean percentage error (MPE), the mean percentage error (ME), the percentage volume in errors (VE), the percentage error in peak (MF), the normalized mean bias error (NMBE), the normalized root mean bias error (NRMSE), the Nash-Sutcliffe model efficiency coefficient (E) and the modified Nash-Sutcliffe model efficiency coefficient (E1). The next day flow for the test set is calculated using the best ANN structure's model. Consequently, the confidence intervals of various confidence levels for training, evaluation and test sets are compared in order to explore the generalisation dynamics of confidence intervals from training and evaluation sets. [1] H.S. Hippert, C.E. Pedreira, R.C. Souza, "Neural networks for short-term load forecasting: A review and evaluation," IEEE Trans. on Power Systems, vol. 16, no. 1, 2001, pp. 44-55. [2] G. J. Tsekouras, N.E. Mastorakis, F.D. Kanellos, V.T. Kontargyri, C.D. Tsirekis, I.S. Karanasiou, Ch.N. Elias, A.D. Salis, P.A. Kontaxis, A.A. Gialketsi: "Short term load forecasting in Greek interconnected power system using ANN: Confidence Interval using a novel re-sampling technique with corrective Factor", WSEAS International Conference on Circuits, Systems, Electronics, Control & Signal Processing, (CSECS '10), Vouliagmeni, Athens, Greece, December 29-31, 2010. [3] D. Panagoulia, I. Trichakis, G. J. Tsekouras: "Flow Forecasting via Artificial Neural Networks - A Study for Input Variables conditioned on atmospheric circulation", European Geosciences Union, General Assembly 2012 (NH1.1 / AS1.16 - Extreme meteorological and hydrological events induced by severe weather and climate change), Vienna, Austria, 22-27 April 2012.

A confidence interval analysis of sampling effort, sequencing depth, and taxonomic resolution of fungal community ecology in the era of high-throughput sequencing.

PubMed

Oono, Ryoko

2017-01-01

High-throughput sequencing technology has helped microbial community ecologists explore ecological and evolutionary patterns at unprecedented scales. The benefits of a large sample size still typically outweigh that of greater sequencing depths per sample for accurate estimations of ecological inferences. However, excluding or not sequencing rare taxa may mislead the answers to the questions 'how and why are communities different?' This study evaluates the confidence intervals of ecological inferences from high-throughput sequencing data of foliar fungal endophytes as case studies through a range of sampling efforts, sequencing depths, and taxonomic resolutions to understand how technical and analytical practices may affect our interpretations. Increasing sampling size reliably decreased confidence intervals across multiple community comparisons. However, the effects of sequencing depths on confidence intervals depended on how rare taxa influenced the dissimilarity estimates among communities and did not significantly decrease confidence intervals for all community comparisons. A comparison of simulated communities under random drift suggests that sequencing depths are important in estimating dissimilarities between microbial communities under neutral selective processes. Confidence interval analyses reveal important biases as well as biological trends in microbial community studies that otherwise may be ignored when communities are only compared for statistically significant differences.

A confidence interval analysis of sampling effort, sequencing depth, and taxonomic resolution of fungal community ecology in the era of high-throughput sequencing

PubMed Central

2017-01-01

High-throughput sequencing technology has helped microbial community ecologists explore ecological and evolutionary patterns at unprecedented scales. The benefits of a large sample size still typically outweigh that of greater sequencing depths per sample for accurate estimations of ecological inferences. However, excluding or not sequencing rare taxa may mislead the answers to the questions ‘how and why are communities different?’ This study evaluates the confidence intervals of ecological inferences from high-throughput sequencing data of foliar fungal endophytes as case studies through a range of sampling efforts, sequencing depths, and taxonomic resolutions to understand how technical and analytical practices may affect our interpretations. Increasing sampling size reliably decreased confidence intervals across multiple community comparisons. However, the effects of sequencing depths on confidence intervals depended on how rare taxa influenced the dissimilarity estimates among communities and did not significantly decrease confidence intervals for all community comparisons. A comparison of simulated communities under random drift suggests that sequencing depths are important in estimating dissimilarities between microbial communities under neutral selective processes. Confidence interval analyses reveal important biases as well as biological trends in microbial community studies that otherwise may be ignored when communities are only compared for statistically significant differences. PMID:29253889

Wastewater Treatment Costs and Outlays in Organic Petrochemicals: Standards Versus Taxes With Methodology Suggestions for Marginal Cost Pricing and Analysis

NASA Astrophysics Data System (ADS)

Thompson, Russell G.; Singleton, F. D., Jr.

1986-04-01

With the methodology recommended by Baumol and Oates, comparable estimates of wastewater treatment costs and industry outlays are developed for effluent standard and effluent tax instruments for pollution abatement in five hypothetical organic petrochemicals (olefins) plants. The computational method uses a nonlinear simulation model for wastewater treatment to estimate the system state inputs for linear programming cost estimation, following a practice developed in a National Science Foundation (Research Applied to National Needs) study at the University of Houston and used to estimate Houston Ship Channel pollution abatement costs for the National Commission on Water Quality. Focusing on best practical and best available technology standards, with effluent taxes adjusted to give nearly equal pollution discharges, shows that average daily treatment costs (and the confidence intervals for treatment cost) would always be less for the effluent tax than for the effluent standard approach. However, industry's total outlay for these treatment costs, plus effluent taxes, would always be greater for the effluent tax approach than the total treatment costs would be for the effluent standard approach. Thus the practical necessity of showing smaller outlays as a prerequisite for a policy change toward efficiency dictates the need to link the economics at the microlevel with that at the macrolevel. Aggregation of the plants into a programming modeling basis for individual sectors and for the economy would provide a sound basis for effective policy reform, because the opportunity costs of the salient regulatory policies would be captured. Then, the government's policymakers would have the informational insights necessary to legislate more efficient environmental policies in light of the wealth distribution effects.

Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11ah

PubMed Central

Tian, Le; Latré, Steven

2017-01-01

IEEE 802.11ah, marketed as Wi-Fi HaLow, extends Wi-Fi to the sub-1 GHz spectrum. Through a number of physical layer (PHY) and media access control (MAC) optimizations, it aims to bring greatly increased range, energy-efficiency, and scalability. This makes 802.11ah the perfect candidate for providing connectivity to Internet of Things (IoT) devices. One of these new features, referred to as the Restricted Access Window (RAW), focuses on improving scalability in highly dense deployments. RAW divides stations into groups and reduces contention and collisions by only allowing channel access to one group at a time. However, the standard does not dictate how to determine the optimal RAW grouping parameters. The optimal parameters depend on the current network conditions, and it has been shown that incorrect configuration severely impacts throughput, latency and energy efficiency. In this paper, we propose a traffic-adaptive RAW optimization algorithm (TAROA) to adapt the RAW parameters in real time based on the current traffic conditions, optimized for sensor networks in which each sensor transmits packets with a certain (predictable) frequency and may change the transmission frequency over time. The TAROA algorithm is executed at each target beacon transmission time (TBTT), and it first estimates the packet transmission interval of each station only based on packet transmission information obtained by access point (AP) during the last beacon interval. Then, TAROA determines the RAW parameters and assigns stations to RAW slots based on this estimated transmission frequency. The simulation results show that, compared to enhanced distributed channel access/distributed coordination function (EDCA/DCF), the TAROA algorithm can highly improve the performance of IEEE 802.11ah dense networks in terms of throughput, especially when hidden nodes exist, although it does not always achieve better latency performance. This paper contributes with a practical approach to optimizing RAW grouping under dynamic traffic in real time, which is a major leap towards applying RAW mechanism in real-life IoT networks. PMID:28677617

Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11ah.

PubMed

Tian, Le; Khorov, Evgeny; Latré, Steven; Famaey, Jeroen

2017-07-04

IEEE 802.11ah, marketed as Wi-Fi HaLow, extends Wi-Fi to the sub-1 GHz spectrum. Through a number of physical layer (PHY) and media access control (MAC) optimizations, it aims to bring greatly increased range, energy-efficiency, and scalability. This makes 802.11ah the perfect candidate for providing connectivity to Internet of Things (IoT) devices. One of these new features, referred to as the Restricted Access Window (RAW), focuses on improving scalability in highly dense deployments. RAW divides stations into groups and reduces contention and collisions by only allowing channel access to one group at a time. However, the standard does not dictate how to determine the optimal RAW grouping parameters. The optimal parameters depend on the current network conditions, and it has been shown that incorrect configuration severely impacts throughput, latency and energy efficiency. In this paper, we propose a traffic-adaptive RAW optimization algorithm (TAROA) to adapt the RAW parameters in real time based on the current traffic conditions, optimized for sensor networks in which each sensor transmits packets with a certain (predictable) frequency and may change the transmission frequency over time. The TAROA algorithm is executed at each target beacon transmission time (TBTT), and it first estimates the packet transmission interval of each station only based on packet transmission information obtained by access point (AP) during the last beacon interval. Then, TAROA determines the RAW parameters and assigns stations to RAW slots based on this estimated transmission frequency. The simulation results show that, compared to enhanced distributed channel access/distributed coordination function (EDCA/DCF), the TAROA algorithm can highly improve the performance of IEEE 802.11ah dense networks in terms of throughput, especially when hidden nodes exist, although it does not always achieve better latency performance. This paper contributes with a practical approach to optimizing RAW grouping under dynamic traffic in real time, which is a major leap towards applying RAW mechanism in real-life IoT networks.

Efficiency of Portable Antennas for Detecting Passive Integrated Transponder Tags in Stream-Dwelling Salmonids

PubMed Central

Moyer, Katherine R.

2016-01-01

Portable antennas have become an increasingly common technique for tracking fish marked with passive integrated transponder (PIT) tags. We used logistic regression to evaluate how species, fish length, and physical habitat characteristics influence portable antenna detection efficiency in stream-dwelling brown trout (Salmo trutta), bull trout (Salvelinus confluentus), and redband trout (Oncorhynchus mykiss newberrii) marked with 12-mm PIT tags. We redetected 56% (20/36) of brown trout, 34% (68/202) of bull trout, and 33% (20/61) of redband trout after a recovery period of 21 to 46 hours. Models indicate support for length and species and minor support for percent boulder, large woody debris, and percent cobble as parameters important for describing variation in detection efficiency, although 95% confidence intervals for estimates were large. The odds of detecting brown trout (1.5 ± 2.2 [mean ± SE]) are approximately four times as high as bull trout (0.4 ± 1.6) or redband trout (0.3 ± 1.8) and species-specific differences may be related to length. Our reported detection efficiency for brown trout falls within the range of other studies, but is the first reported for bull trout and redband trout. Portable antennas may be a relatively unbiased way of redetecting varying sizes of all three salmonid species. PMID:26901317

Efficiency of portable antennas for detecting passive integrated transponder tags in stream-dwelling salmonids

USGS Publications Warehouse

Banish, Nolan P.; Burdick, Summer M.; Moyer, Katherine R.

2016-01-01

Portable antennas have become an increasingly common technique for tracking fish marked with passive integrated transponder (PIT) tags. We used logistic regression to evaluate how species, fish length, and physical habitat characteristics influence portable antenna detection efficiency in stream-dwelling brown trout (Salmo trutta), bull trout (Salvelinus confluentus), and redband trout (Oncorhynchus mykiss newberrii) marked with 12-mm PIT tags. We redetected 56% (20/36) of brown trout, 34% (68/202) of bull trout, and 33% (20/61) of redband trout after a recovery period of 21 to 46 hours. Models indicate support for length and species and minor support for percent boulder, large woody debris, and percent cobble as parameters important for describing variation in detection efficiency, although 95% confidence intervals for estimates were large. The odds of detecting brown trout (1.5 ± 2.2 [mean ± SE]) are approximately four times as high as bull trout (0.4 ± 1.6) or redband trout (0.3 ± 1.8) and species-specific differences may be related to length. Our reported detection efficiency for brown trout falls within the range of other studies, but is the first reported for bull trout and redband trout. Portable antennas may be a relatively unbiased way of redetecting varying sizes of all three salmonid species.

A modified Wald interval for the area under the ROC curve (AUC) in diagnostic case-control studies

PubMed Central

2014-01-01

Background The area under the receiver operating characteristic (ROC) curve, referred to as the AUC, is an appropriate measure for describing the overall accuracy of a diagnostic test or a biomarker in early phase trials without having to choose a threshold. There are many approaches for estimating the confidence interval for the AUC. However, all are relatively complicated to implement. Furthermore, many approaches perform poorly for large AUC values or small sample sizes. Methods The AUC is actually a probability. So we propose a modified Wald interval for a single proportion, which can be calculated on a pocket calculator. We performed a simulation study to compare this modified Wald interval (without and with continuity correction) with other intervals regarding coverage probability and statistical power. Results The main result is that the proposed modified Wald intervals maintain and exploit the type I error much better than the intervals of Agresti-Coull, Wilson, and Clopper-Pearson. The interval suggested by Bamber, the Mann-Whitney interval without transformation and also the interval of the binormal AUC are very liberal. For small sample sizes the Wald interval with continuity has a comparable coverage probability as the LT interval and higher power. For large sample sizes the results of the LT interval and of the Wald interval without continuity correction are comparable. Conclusions If individual patient data is not available, but only the estimated AUC and the total sample size, the modified Wald intervals can be recommended as confidence intervals for the AUC. For small sample sizes the continuity correction should be used. PMID:24552686

A modified Wald interval for the area under the ROC curve (AUC) in diagnostic case-control studies.

PubMed

Kottas, Martina; Kuss, Oliver; Zapf, Antonia

2014-02-19

The area under the receiver operating characteristic (ROC) curve, referred to as the AUC, is an appropriate measure for describing the overall accuracy of a diagnostic test or a biomarker in early phase trials without having to choose a threshold. There are many approaches for estimating the confidence interval for the AUC. However, all are relatively complicated to implement. Furthermore, many approaches perform poorly for large AUC values or small sample sizes. The AUC is actually a probability. So we propose a modified Wald interval for a single proportion, which can be calculated on a pocket calculator. We performed a simulation study to compare this modified Wald interval (without and with continuity correction) with other intervals regarding coverage probability and statistical power. The main result is that the proposed modified Wald intervals maintain and exploit the type I error much better than the intervals of Agresti-Coull, Wilson, and Clopper-Pearson. The interval suggested by Bamber, the Mann-Whitney interval without transformation and also the interval of the binormal AUC are very liberal. For small sample sizes the Wald interval with continuity has a comparable coverage probability as the LT interval and higher power. For large sample sizes the results of the LT interval and of the Wald interval without continuity correction are comparable. If individual patient data is not available, but only the estimated AUC and the total sample size, the modified Wald intervals can be recommended as confidence intervals for the AUC. For small sample sizes the continuity correction should be used.

Hybrid Cubature Kalman filtering for identifying nonlinear models from sampled recording: Estimation of neuronal dynamics.

PubMed

Madi, Mahmoud K; Karameh, Fadi N

2017-01-01

Kalman filtering methods have long been regarded as efficient adaptive Bayesian techniques for estimating hidden states in models of linear dynamical systems under Gaussian uncertainty. Recent advents of the Cubature Kalman filter (CKF) have extended this efficient estimation property to nonlinear systems, and also to hybrid nonlinear problems where by the processes are continuous and the observations are discrete (continuous-discrete CD-CKF). Employing CKF techniques, therefore, carries high promise for modeling many biological phenomena where the underlying processes exhibit inherently nonlinear, continuous, and noisy dynamics and the associated measurements are uncertain and time-sampled. This paper investigates the performance of cubature filtering (CKF and CD-CKF) in two flagship problems arising in the field of neuroscience upon relating brain functionality to aggregate neurophysiological recordings: (i) estimation of the firing dynamics and the neural circuit model parameters from electric potentials (EP) observations, and (ii) estimation of the hemodynamic model parameters and the underlying neural drive from BOLD (fMRI) signals. First, in simulated neural circuit models, estimation accuracy was investigated under varying levels of observation noise (SNR), process noise structures, and observation sampling intervals (dt). When compared to the CKF, the CD-CKF consistently exhibited better accuracy for a given SNR, sharp accuracy increase with higher SNR, and persistent error reduction with smaller dt. Remarkably, CD-CKF accuracy shows only a mild deterioration for non-Gaussian process noise, specifically with Poisson noise, a commonly assumed form of background fluctuations in neuronal systems. Second, in simulated hemodynamic models, parametric estimates were consistently improved under CD-CKF. Critically, time-localization of the underlying neural drive, a determinant factor in fMRI-based functional connectivity studies, was significantly more accurate under CD-CKF. In conclusion, and with the CKF recently benchmarked against other advanced Bayesian techniques, the CD-CKF framework could provide significant gains in robustness and accuracy when estimating a variety of biological phenomena models where the underlying process dynamics unfold at time scales faster than those seen in collected measurements.

Hybrid Cubature Kalman filtering for identifying nonlinear models from sampled recording: Estimation of neuronal dynamics

PubMed Central

2017-01-01

Kalman filtering methods have long been regarded as efficient adaptive Bayesian techniques for estimating hidden states in models of linear dynamical systems under Gaussian uncertainty. Recent advents of the Cubature Kalman filter (CKF) have extended this efficient estimation property to nonlinear systems, and also to hybrid nonlinear problems where by the processes are continuous and the observations are discrete (continuous-discrete CD-CKF). Employing CKF techniques, therefore, carries high promise for modeling many biological phenomena where the underlying processes exhibit inherently nonlinear, continuous, and noisy dynamics and the associated measurements are uncertain and time-sampled. This paper investigates the performance of cubature filtering (CKF and CD-CKF) in two flagship problems arising in the field of neuroscience upon relating brain functionality to aggregate neurophysiological recordings: (i) estimation of the firing dynamics and the neural circuit model parameters from electric potentials (EP) observations, and (ii) estimation of the hemodynamic model parameters and the underlying neural drive from BOLD (fMRI) signals. First, in simulated neural circuit models, estimation accuracy was investigated under varying levels of observation noise (SNR), process noise structures, and observation sampling intervals (dt). When compared to the CKF, the CD-CKF consistently exhibited better accuracy for a given SNR, sharp accuracy increase with higher SNR, and persistent error reduction with smaller dt. Remarkably, CD-CKF accuracy shows only a mild deterioration for non-Gaussian process noise, specifically with Poisson noise, a commonly assumed form of background fluctuations in neuronal systems. Second, in simulated hemodynamic models, parametric estimates were consistently improved under CD-CKF. Critically, time-localization of the underlying neural drive, a determinant factor in fMRI-based functional connectivity studies, was significantly more accurate under CD-CKF. In conclusion, and with the CKF recently benchmarked against other advanced Bayesian techniques, the CD-CKF framework could provide significant gains in robustness and accuracy when estimating a variety of biological phenomena models where the underlying process dynamics unfold at time scales faster than those seen in collected measurements. PMID:28727850

[Nonparametric method of estimating survival functions containing right-censored and interval-censored data].

PubMed

Xu, Yonghong; Gao, Xiaohuan; Wang, Zhengxi

2014-04-01

Missing data represent a general problem in many scientific fields, especially in medical survival analysis. Dealing with censored data, interpolation method is one of important methods. However, most of the interpolation methods replace the censored data with the exact data, which will distort the real distribution of the censored data and reduce the probability of the real data falling into the interpolation data. In order to solve this problem, we in this paper propose a nonparametric method of estimating the survival function of right-censored and interval-censored data and compare its performance to SC (self-consistent) algorithm. Comparing to the average interpolation and the nearest neighbor interpolation method, the proposed method in this paper replaces the right-censored data with the interval-censored data, and greatly improves the probability of the real data falling into imputation interval. Then it bases on the empirical distribution theory to estimate the survival function of right-censored and interval-censored data. The results of numerical examples and a real breast cancer data set demonstrated that the proposed method had higher accuracy and better robustness for the different proportion of the censored data. This paper provides a good method to compare the clinical treatments performance with estimation of the survival data of the patients. This pro vides some help to the medical survival data analysis.

Pulse rate variability compared with Heart Rate Variability in children with and without sleep disordered breathing.

PubMed

Dehkordi, Parastoo; Garde, Ainara; Karlen, Walter; Wensley, David; Ansermino, J Mark; Dumont, Guy A

2013-01-01

Heart Rate Variability (HRV), the variation of time intervals between heartbeats, is one of the most promising and widely used quantitative markers of autonomic activity. Traditionally, HRV is measured as the series of instantaneous cycle intervals obtained from the electrocardiogram (ECG). In this study, we investigated the estimation of variation in heart rate from a photoplethysmography (PPG) signal, called pulse rate variability (PRV), and assessed its accuracy as an estimate of HRV in children with and without sleep disordered breathing (SDB). We recorded raw PPGs from 72 children using the Phone Oximeter, an oximeter connected to a mobile phone. Full polysomnography including ECG was simultaneously recorded for each subject. We used correlation and Bland-Altman analysis for comparing the parameters of HRV and PRV between two groups of children. Significant correlation (r > 0.90, p < 0.05) and close agreement were found between HRV and PRV for mean intervals, standard deviation of intervals (SDNN) and the root-mean square of the difference of successive intervals (RMSSD). However Bland-Altman analysis showed a large divergence for LF/HF ratio parameter. In addition, children with SDB had depressed SDNN and RMSSD and elevated LF/HF in comparison to children without SDB. In conclusion, PRV provides the accurate estimate of HRV in time domain analysis but does not reflect precise estimation for parameters in frequency domain.

Real-time hydraulic interval state estimation for water transport networks: a case study

NASA Astrophysics Data System (ADS)

Vrachimis, Stelios G.; Eliades, Demetrios G.; Polycarpou, Marios M.

2018-03-01

Hydraulic state estimation in water distribution networks is the task of estimating water flows and pressures in the pipes and nodes of the network based on some sensor measurements. This requires a model of the network as well as knowledge of demand outflow and tank water levels. Due to modeling and measurement uncertainty, standard state estimation may result in inaccurate hydraulic estimates without any measure of the estimation error. This paper describes a methodology for generating hydraulic state bounding estimates based on interval bounds on the parametric and measurement uncertainties. The estimation error bounds provided by this method can be applied to determine the existence of unaccounted-for water in water distribution networks. As a case study, the method is applied to a modified transport network in Cyprus, using actual data in real time.

Examining Temporal Sample Scale and Model Choice with Spatial Capture-Recapture Models in the Common Leopard Panthera pardus.

PubMed

Goldberg, Joshua F; Tempa, Tshering; Norbu, Nawang; Hebblewhite, Mark; Mills, L Scott; Wangchuk, Tshewang R; Lukacs, Paul

2015-01-01

Many large carnivores occupy a wide geographic distribution, and face threats from habitat loss and fragmentation, poaching, prey depletion, and human wildlife-conflicts. Conservation requires robust techniques for estimating population densities and trends, but the elusive nature and low densities of many large carnivores make them difficult to detect. Spatial capture-recapture (SCR) models provide a means for handling imperfect detectability, while linking population estimates to individual movement patterns to provide more accurate estimates than standard approaches. Within this framework, we investigate the effect of different sample interval lengths on density estimates, using simulations and a common leopard (Panthera pardus) model system. We apply Bayesian SCR methods to 89 simulated datasets and camera-trapping data from 22 leopards captured 82 times during winter 2010-2011 in Royal Manas National Park, Bhutan. We show that sample interval length from daily, weekly, monthly or quarterly periods did not appreciably affect median abundance or density, but did influence precision. We observed the largest gains in precision when moving from quarterly to shorter intervals. We therefore recommend daily sampling intervals for monitoring rare or elusive species where practicable, but note that monthly or quarterly sample periods can have similar informative value. We further develop a novel application of Bayes factors to select models where multiple ecological factors are integrated into density estimation. Our simulations demonstrate that these methods can help identify the "true" explanatory mechanisms underlying the data. Using this method, we found strong evidence for sex-specific movement distributions in leopards, suggesting that sexual patterns of space-use influence density. This model estimated a density of 10.0 leopards/100 km2 (95% credibility interval: 6.25-15.93), comparable to contemporary estimates in Asia. These SCR methods provide a guide to monitor and observe the effect of management interventions on leopards and other species of conservation interest.

Examining Temporal Sample Scale and Model Choice with Spatial Capture-Recapture Models in the Common Leopard Panthera pardus

PubMed Central

Goldberg, Joshua F.; Tempa, Tshering; Norbu, Nawang; Hebblewhite, Mark; Mills, L. Scott; Wangchuk, Tshewang R.; Lukacs, Paul

2015-01-01

Many large carnivores occupy a wide geographic distribution, and face threats from habitat loss and fragmentation, poaching, prey depletion, and human wildlife-conflicts. Conservation requires robust techniques for estimating population densities and trends, but the elusive nature and low densities of many large carnivores make them difficult to detect. Spatial capture-recapture (SCR) models provide a means for handling imperfect detectability, while linking population estimates to individual movement patterns to provide more accurate estimates than standard approaches. Within this framework, we investigate the effect of different sample interval lengths on density estimates, using simulations and a common leopard (Panthera pardus) model system. We apply Bayesian SCR methods to 89 simulated datasets and camera-trapping data from 22 leopards captured 82 times during winter 2010–2011 in Royal Manas National Park, Bhutan. We show that sample interval length from daily, weekly, monthly or quarterly periods did not appreciably affect median abundance or density, but did influence precision. We observed the largest gains in precision when moving from quarterly to shorter intervals. We therefore recommend daily sampling intervals for monitoring rare or elusive species where practicable, but note that monthly or quarterly sample periods can have similar informative value. We further develop a novel application of Bayes factors to select models where multiple ecological factors are integrated into density estimation. Our simulations demonstrate that these methods can help identify the “true” explanatory mechanisms underlying the data. Using this method, we found strong evidence for sex-specific movement distributions in leopards, suggesting that sexual patterns of space-use influence density. This model estimated a density of 10.0 leopards/100 km2 (95% credibility interval: 6.25–15.93), comparable to contemporary estimates in Asia. These SCR methods provide a guide to monitor and observe the effect of management interventions on leopards and other species of conservation interest. PMID:26536231

Selection of performance-tested young bulls and indirect responses in commercial beef cattle herds on pasture and in feedlots.

PubMed

Raidan, Fernanda S S; Santos, Dalinne C C; Moraes, Mariana M; Araújo, Andresa E M; Ventura, Henrique T; Bergmann, José A G; Turra, Eduardo M; Toral, Fabio L B

2016-11-09

Central testing is used to select young bulls which are likely to contribute to increased net income of the commercial beef cattle herd. We present genetic parameters for growth and reproductive traits on performance-tested young bulls and commercial animals that are raised on pasture and in feedlots. Records on young bulls and heifers in performance tests or commercial herds were used. Genetic parameters for growth and reproductive traits were estimated. Correlated responses for commercial animals when selection was applied on performance-tested young bulls were computed. The 90% highest posterior density (HPD90) intervals for heritabilities of final weight (FW), average daily gain (ADG) and scrotal circumference (SC) ranged from 0.41 to 0.49, 0.23 to 0.30 and 0.47 to 0.57, respectively, for performance-tested young bulls on pasture, from 0.45 to 0.60, 0.20 to 0.32 and 0.56 to 0.70, respectively, for performance-tested young bulls in feedlots, from 0.29 to 0.33, 0.14 to 0.18 and 0.35 to 0.45, respectively, for commercial animals on pasture, and from 0.24 to 0.44, 0.13 to 0.24 and 0.35 to 0.57 respectively, for commercial animals in feedlots. The HPD90 intervals for genetic correlations of FW, ADG and SC in performance-tested young bulls on pasture (feedlots) with FW, ADG and SC in commercial animals on pasture (feedlots) ranged from 0.86 to 0.96 (0.83 to 0.94), 0.78 to 0.90 (0.40 to 0.79) and from 0.92 to 0.97 (0.50 to 0.83), respectively. Age at first calving was genetically related to ADG (HPD90 interval = -0.48 to -0.06) and SC (HPD90 interval = -0.41 to -0.05) for performance-tested young bulls on pasture, however it was not related to ADG (HPD90 interval = -0.29 to 0.10) and SC (HPD90 interval = -0.35 to 0.13) for performance-tested young bulls in feedlots. Heritabilities for growth and SC are higher for performance-tested young bulls than for commercial animals. Evaluating and selecting for increased growth and SC on performance-tested young bulls is efficient to improve growth, SC and age at first calving in commercial animals. Evaluating and selecting performance-tested young bulls is more efficient for young bulls on pasture than in feedlots.

Towards the estimation of effect measures in studies using respondent-driven sampling.

PubMed

Rotondi, Michael A

2014-06-01

Respondent-driven sampling (RDS) is an increasingly common sampling technique to recruit hidden populations. Statistical methods for RDS are not straightforward due to the correlation between individual outcomes and subject weighting; thus, analyses are typically limited to estimation of population proportions. This manuscript applies the method of variance estimates recovery (MOVER) to construct confidence intervals for effect measures such as risk difference (difference of proportions) or relative risk in studies using RDS. To illustrate the approach, MOVER is used to construct confidence intervals for differences in the prevalence of demographic characteristics between an RDS study and convenience study of injection drug users. MOVER is then applied to obtain a confidence interval for the relative risk between education levels and HIV seropositivity and current infection with syphilis, respectively. This approach provides a simple method to construct confidence intervals for effect measures in RDS studies. Since it only relies on a proportion and appropriate confidence limits, it can also be applied to previously published manuscripts.

A time-lapse photography method for monitoring salmon (Oncorhynchus spp.) passage and abundance in streams

PubMed Central

Leacock, William B.; Eby, Lisa A.; Stanford, Jack A.

2016-01-01

Accurately estimating population sizes is often a critical component of fisheries research and management. Although there is a growing appreciation of the importance of small-scale salmon population dynamics to the stability of salmon stock-complexes, our understanding of these populations is constrained by a lack of efficient and cost-effective monitoring tools for streams. Weirs are expensive, labor intensive, and can disrupt natural fish movements. While conventional video systems avoid some of these shortcomings, they are expensive and require excessive amounts of labor to review footage for data collection. Here, we present a novel method for quantifying salmon in small streams (<15 m wide, <1 m deep) that uses both time-lapse photography and video in a model-based double sampling scheme. This method produces an escapement estimate nearly as accurate as a video-only approach, but with substantially less labor, money, and effort. It requires servicing only every 14 days, detects salmon 24 h/day, is inexpensive, and produces escapement estimates with confidence intervals. In addition to escapement estimation, we present a method for estimating in-stream salmon abundance across time, data needed by researchers interested in predator--prey interactions or nutrient subsidies. We combined daily salmon passage estimates with stream specific estimates of daily mortality developed using previously published data. To demonstrate proof of concept for these methods, we present results from two streams in southwest Kodiak Island, Alaska in which high densities of sockeye salmon spawn. PMID:27326378

Estimation of surface water quality in a Yazoo River tributary using the duration curve and recurrence interval approach

Treesearch

Ying Ouyang; Prem B. Parajuli; Daniel A. Marion

2013-01-01

Pollution of surface water with harmful chemicals and eutrophication of rivers and lakes with excess nutrients are serious environmental concerns. This study estimated surface water quality in a stream within the Yazoo River Basin (YRB), Mississippi, USA, using the duration curve and recurrence interval analysis techniques. Data from the US Geological Survey (USGS)...

The method of trend analysis of parameters time series of gas-turbine engine state

NASA Astrophysics Data System (ADS)

Hvozdeva, I.; Myrhorod, V.; Derenh, Y.

2017-10-01

This research substantiates an approach to interval estimation of time series trend component. The well-known methods of spectral and trend analysis are used for multidimensional data arrays. The interval estimation of trend component is proposed for the time series whose autocorrelation matrix possesses a prevailing eigenvalue. The properties of time series autocorrelation matrix are identified.

Estimating snow load in California for three recurrence intervals

Treesearch

David L. Azuma

1985-01-01

A key to designing facilities in snowbound areas is knowing what the expected snow load levels are for given recurrence intervals. In California, information about snow load is available only for the Lake Tahoe Basin. About 280 snow courses in the State were analyzed, and snow load estimated and related to elevation on a river basin and statewide level. The tabulated...

Challenges in risk estimation using routinely collected clinical data: The example of estimating cervical cancer risks from electronic health-records.

PubMed

Landy, Rebecca; Cheung, Li C; Schiffman, Mark; Gage, Julia C; Hyun, Noorie; Wentzensen, Nicolas; Kinney, Walter K; Castle, Philip E; Fetterman, Barbara; Poitras, Nancy E; Lorey, Thomas; Sasieni, Peter D; Katki, Hormuzd A

2018-06-01

Electronic health-records (EHR) are increasingly used by epidemiologists studying disease following surveillance testing to provide evidence for screening intervals and referral guidelines. Although cost-effective, undiagnosed prevalent disease and interval censoring (in which asymptomatic disease is only observed at the time of testing) raise substantial analytic issues when estimating risk that cannot be addressed using Kaplan-Meier methods. Based on our experience analysing EHR from cervical cancer screening, we previously proposed the logistic-Weibull model to address these issues. Here we demonstrate how the choice of statistical method can impact risk estimates. We use observed data on 41,067 women in the cervical cancer screening program at Kaiser Permanente Northern California, 2003-2013, as well as simulations to evaluate the ability of different methods (Kaplan-Meier, Turnbull, Weibull and logistic-Weibull) to accurately estimate risk within a screening program. Cumulative risk estimates from the statistical methods varied considerably, with the largest differences occurring for prevalent disease risk when baseline disease ascertainment was random but incomplete. Kaplan-Meier underestimated risk at earlier times and overestimated risk at later times in the presence of interval censoring or undiagnosed prevalent disease. Turnbull performed well, though was inefficient and not smooth. The logistic-Weibull model performed well, except when event times didn't follow a Weibull distribution. We have demonstrated that methods for right-censored data, such as Kaplan-Meier, result in biased estimates of disease risks when applied to interval-censored data, such as screening programs using EHR data. The logistic-Weibull model is attractive, but the model fit must be checked against Turnbull non-parametric risk estimates. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Estimation of two ordered mean residual lifetime functions.

PubMed

Ebrahimi, N

1993-06-01

In many statistical studies involving failure data, biometric mortality data, and actuarial data, mean residual lifetime (MRL) function is of prime importance. In this paper we introduce the problem of nonparametric estimation of a MRL function on an interval when this function is bounded from below by another such function (known or unknown) on that interval, and derive the corresponding two functional estimators. The first is to be used when there is a known bound, and the second when the bound is another MRL function to be estimated independently. Both estimators are obtained by truncating the empirical estimator discussed by Yang (1978, Annals of Statistics 6, 112-117). In the first case, it is truncated at a known bound; in the second, at a point somewhere between the two empirical estimates. Consistency of both estimators is proved, and a pointwise large-sample distribution theory of the first estimator is derived.

Spatiotemporal Modeling for Fine-Scale Maps of Regional Malaria Endemicity and Its Implications for Transitional Complexities in a Routine Surveillance Network in Western Cambodia

PubMed Central

Okami, Suguru; Kohtake, Naohiko

2017-01-01

Due to the associated and substantial efforts of many stakeholders involved in malaria containment, the disease burden of malaria has dramatically decreased in many malaria-endemic countries in recent years. Some decades after the past efforts of the global malaria eradication program, malaria elimination has again featured on the global health agenda. While risk distribution modeling and a mapping approach are effective tools to assist with the efficient allocation of limited health-care resources, these methods need some adjustment and reexamination in accordance with changes occurring in relation to malaria elimination. Limited available data, fine-scale data inaccessibility (for example, household or individual case data), and the lack of reliable data due to inefficiencies within the routine surveillance system, make it difficult to create reliable risk maps for decision-makers or health-care practitioners in the field. Furthermore, the risk of malaria may dynamically change due to various factors such as the progress of containment interventions and environmental changes. To address the complex and dynamic nature of situations in low-to-moderate malaria transmission settings, we built a spatiotemporal model of a standardized morbidity ratio (SMR) of malaria incidence, calculated through annual parasite incidence, using routinely reported surveillance data in combination with environmental indices such as remote sensing data, and the non-environmental regional containment status, to create fine-scale risk maps. A hierarchical Bayesian frame was employed to fit the transitioning malaria risk data onto the map. The model was set to estimate the SMRs of every study location at specific time intervals within its uncertainty range. Using the spatial interpolation of estimated SMRs at village level, we created fine-scale maps of two provinces in western Cambodia at specific time intervals. The maps presented different patterns of malaria risk distribution at specific time intervals. Moreover, the visualized weights estimated using the risk model, and the structure of the routine surveillance network, represent the transitional complexities emerging from ever-changing regional endemic situations. PMID:29034229

Linkage map of the honey bee, Apis mellifera, based on RAPD markers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hunt, G.J.; Page, R.E. Jr.

A linkage map was constructed for the honey bee based on the segregation of 365 random amplified polymorphic DNA (RAPD) markers in haploid male progeny of a single female bee. The X locus for sex determination and genes for black body color and malate dehydrogenase were mapped to separate linkage groups. RAPD markers were very efficient for mapping, with an average of about 2.8 loci mapped for each 10-nucleotide primer that was used in polymerase chain reactions. The mean interval size between markers on the map was 9.1 cM. The map covered 3110 cM of linked markers on 26 linkagemore » groups. We estimate the total genome size to be {approximately}3450 cM. The size of the map indicated a very high recombination rate for the honey bee. The relationship of physical to genetic distance was estimated at 52 kb/cM, suggesting that map-based cloning of genes will be feasible for this species. 71 refs., 6 figs., 1 tab.« less

Climate change decouples oceanic primary and export productivity and organic carbon burial

PubMed Central

Lopes, Cristina; Kucera, Michal; Mix, Alan C.

2015-01-01

Understanding responses of oceanic primary productivity, carbon export, and burial to climate change is essential for model-based projection of biological feedbacks in a high-CO2 world. Here we compare estimates of productivity based on the composition of fossil diatom floras with organic carbon burial off Oregon in the Northeast Pacific across a large climatic transition at the last glacial termination. Although estimated primary productivity was highest during the Last Glacial Maximum, carbon burial was lowest, reflecting reduced preservation linked to low sedimentation rates. A diatom size index further points to a glacial decrease (and deglacial increase) in the fraction of fixed carbon that was exported, inferred to reflect expansion, and contraction, of subpolar ecosystems that today favor smaller plankton. Thus, in contrast to models that link remineralization of carbon to temperature, in the Northeast Pacific, we find dominant ecosystem and sea floor control such that intervals of warming climate had more efficient carbon export and higher carbon burial despite falling primary productivity. PMID:25453073

Overcoming free energy barriers using unconstrained molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Hénin, Jérôme; Chipot, Christophe

2004-08-01

Association of unconstrained molecular dynamics (MD) and the formalisms of thermodynamic integration and average force [Darve and Pohorille, J. Chem. Phys. 115, 9169 (2001)] have been employed to determine potentials of mean force. When implemented in a general MD code, the additional computational effort, compared to other standard, unconstrained simulations, is marginal. The force acting along a chosen reaction coordinate ξ is estimated from the individual forces exerted on the chemical system and accumulated as the simulation progresses. The estimated free energy derivative computed for small intervals of ξ is canceled by an adaptive bias to overcome the barriers of the free energy landscape. Evolution of the system along the reaction coordinate is, thus, limited by its sole self-diffusion properties. The illustrative examples of the reversible unfolding of deca-L-alanine, the association of acetate and guanidinium ions in water, the dimerization of methane in water, and its transfer across the water liquid-vapor interface are examined to probe the efficiency of the method.

Overcoming free energy barriers using unconstrained molecular dynamics simulations.

PubMed

Hénin, Jérôme; Chipot, Christophe

2004-08-15

Association of unconstrained molecular dynamics (MD) and the formalisms of thermodynamic integration and average force [Darve and Pohorille, J. Chem. Phys. 115, 9169 (2001)] have been employed to determine potentials of mean force. When implemented in a general MD code, the additional computational effort, compared to other standard, unconstrained simulations, is marginal. The force acting along a chosen reaction coordinate xi is estimated from the individual forces exerted on the chemical system and accumulated as the simulation progresses. The estimated free energy derivative computed for small intervals of xi is canceled by an adaptive bias to overcome the barriers of the free energy landscape. Evolution of the system along the reaction coordinate is, thus, limited by its sole self-diffusion properties. The illustrative examples of the reversible unfolding of deca-L-alanine, the association of acetate and guanidinium ions in water, the dimerization of methane in water, and its transfer across the water liquid-vapor interface are examined to probe the efficiency of the method. (c) 2004 American Institute of Physics.

Reducing sampling error in faecal egg counts from black rhinoceros (Diceros bicornis).

PubMed

Stringer, Andrew P; Smith, Diane; Kerley, Graham I H; Linklater, Wayne L

2014-04-01

Faecal egg counts (FECs) are commonly used for the non-invasive assessment of parasite load within hosts. Sources of error, however, have been identified in laboratory techniques and sample storage. Here we focus on sampling error. We test whether a delay in sample collection can affect FECs, and estimate the number of samples needed to reliably assess mean parasite abundance within a host population. Two commonly found parasite eggs in black rhinoceros (Diceros bicornis) dung, strongyle-type nematodes and Anoplocephala gigantea, were used. We find that collection of dung from the centre of faecal boluses up to six hours after defecation does not affect FECs. More than nine samples were needed to greatly improve confidence intervals of the estimated mean parasite abundance within a host population. These results should improve the cost-effectiveness and efficiency of sampling regimes, and support the usefulness of FECs when used for the non-invasive assessment of parasite abundance in black rhinoceros populations.

Exploration of the factor structure of the Kirton Adaption-Innovation Inventory using bootstrapping estimation.

PubMed

Im, Subin; Min, Soonhong

2013-04-01

Exploratory factor analyses of the Kirton Adaption-Innovation Inventory (KAI), which serves to measure individual cognitive styles, generally indicate three factors: sufficiency of originality, efficiency, and rule/group conformity. In contrast, a 2005 study by Im and Hu using confirmatory factor analysis supported a four-factor structure, dividing the sufficiency of originality dimension into two subdimensions, idea generation and preference for change. This study extends Im and Hu's (2005) study of a derived version of the KAI by providing additional evidence of the four-factor structure. Specifically, the authors test the robustness of the parameter estimates to the violation of normality assumptions in the sample using bootstrap methods. A bias-corrected confidence interval bootstrapping procedure conducted among a sample of 356 participants--members of the Arkansas Household Research Panel, with middle SES and average age of 55.6 yr. (SD = 13.9)--showed that the four-factor model with two subdimensions of sufficiency of originality fits the data significantly better than the three-factor model in non-normality conditions.

Detection of anomalous signals in temporally correlated data (Invited)

NASA Astrophysics Data System (ADS)

Langbein, J. O.

2010-12-01

Detection of transient tectonic signals in data obtained from large geodetic networks requires the ability to detect signals that are both temporally and spatially coherent. In this report I will describe a modification to an existing method that estimates both the coefficients of temporally correlated noise model and an efficient filter based on the noise model. This filter, when applied to the original time-series, effectively whitens (or flattens) the power spectrum. The filtered data provide the means to calculate running averages which are then used to detect deviations from the background trends. For large networks, time-series of signal-to-noise ratio (SNR) can be easily constructed since, by filtering, each of the original time-series has been transformed into one that is closer to having a Gaussian distribution with a variance of 1.0. Anomalous intervals may be identified by counting the number of GPS sites for which the SNR exceeds a specified value. For example, during one time interval, if there were 5 out of 20 time-series with SNR>2, this would be considered anomalous; typically, one would expect at 95% confidence that there would be at least 1 out of 20 time-series with an SNR>2. For time intervals with an anomalously large number of high SNR, the spatial distribution of the SNR is mapped to identify the location of the anomalous signal(s) and their degree of spatial clustering. Estimating the filter that should be used to whiten the data requires modification of the existing methods that employ maximum likelihood estimation to determine the temporal covariance of the data. In these methods, it is assumed that the noise components in the data are a combination of white, flicker and random-walk processes and that they are derived from three different and independent sources. Instead, in this new method, the covariance matrix is constructed assuming that only one source is responsible for the noise and that source can be represented as a white-noise random-number generator convolved with a filter whose spectral properties are frequency (f) independent at its highest frequencies, 1/f at the middle frequencies, and 1/f2 at the lowest frequencies. For data sets with no gaps in their time-series, construction of covariance and inverse covariance matrices is extremely efficient. Application of the above algorithm to real data potentially involves several iterations as small, tectonic signals of interest are often indistinguishable from background noise. Consequently, simply plotting the time-series of each GPS site is used to identify the largest outliers and signals independent of their cause. Any analysis of the background noise levels must factor in these other signals while the gross outliers need to be removed.

Automatic Error Analysis Using Intervals

ERIC Educational Resources Information Center

Rothwell, E. J.; Cloud, M. J.

2012-01-01

A technique for automatic error analysis using interval mathematics is introduced. A comparison to standard error propagation methods shows that in cases involving complicated formulas, the interval approach gives comparable error estimates with much less effort. Several examples are considered, and numerical errors are computed using the INTLAB…

Time-variant random interval natural frequency analysis of structures

NASA Astrophysics Data System (ADS)

Wu, Binhua; Wu, Di; Gao, Wei; Song, Chongmin

2018-02-01

This paper presents a new robust method namely, unified interval Chebyshev-based random perturbation method, to tackle hybrid random interval structural natural frequency problem. In the proposed approach, random perturbation method is implemented to furnish the statistical features (i.e., mean and standard deviation) and Chebyshev surrogate model strategy is incorporated to formulate the statistical information of natural frequency with regards to the interval inputs. The comprehensive analysis framework combines the superiority of both methods in a way that computational cost is dramatically reduced. This presented method is thus capable of investigating the day-to-day based time-variant natural frequency of structures accurately and efficiently under concrete intrinsic creep effect with probabilistic and interval uncertain variables. The extreme bounds of the mean and standard deviation of natural frequency are captured through the embedded optimization strategy within the analysis procedure. Three particularly motivated numerical examples with progressive relationship in perspective of both structure type and uncertainty variables are demonstrated to justify the computational applicability, accuracy and efficiency of the proposed method.

Estimation of gas hydrate saturation using isotropic and anisotropic models at the location selected after measurement of seismic quality factor

NASA Astrophysics Data System (ADS)

Mundhra, A.; Sain, K.; Shankar, U.

2012-12-01

The Indian National Gas Hydrate Program Expedition (NGHP) 01 discovered gas hydrate in unconsolidated sediments at several drilling sites along the continental margins of Krishna-Godavari Basin, India. Presence of gas hydrate reduces the attenuation of travelling seismic waves which can be measured by estimation of seismic quality factor (Dasgupta and Clark, 1998). Here, we use log spectral ratio method (Sain et al, 2009) to compute quality factor at three locations, among which two have strong and one has no bottom simulating reflector (BSR), along seismic cross-line near one of the drilling site. Interval quality factor for three submarine sedimentary layers bounded by seafloor, BSR, one reflector above and another reflector below the BSR has been measured. To compute quality factor, unprocessed pre-stack seismic data has been used to avoid any influence of processing sequence. We have estimated that interval quality factor lies within 200-220 in the interval having BSR while it varies within 90-100 in other intervals. Thereby, high interval quality factor ascertains that observed BSR is due to presence of gas hydrates. We have performed rock physics modelling by using isotropic and anisotropic models, to quantitatively estimate gas hydrate saturation at one of the location where an interval has high quality factor. Abruptly high measured resistivity and high P-wave velocity in the interval, leads to towering hydrate saturation (Archie,1942 and Helegrud et al, 1999) in comparison to lower gas hydrate saturations estimated by pressure core and chlorinity measurements. Overestimation of saturation is attributed to presence of near vertical fractures that are identified from logging-while-drilling resistivity images. We have carried out anisotropic modeling (Kennedy and Herrick, 2004 and Lee,2009) by incorporating fracture volume and fracture porosity to estimate hydrate saturation and have observed that modeled gas hydrate saturations agree with the lower gas hydrate saturations obtained from pressure core and chlorinity measurements. Therefore, we find that 1) quality factor is significantly higher in the interval bearing gas hydrates and is a useful tool to discover hydrate deposits, 2) anisotropy due to presence of near vertical hydrate filled fractures translates into elevated saturation because of high measured resistivity and velocity and 3) anisotropic model greatly corrects the saturation estimates in fractured medium. References: Archie, G.E., 1942. Petroleum Transactions of AIME, 146, 54-62. Dasgupta, R., Clark, R.A., 1998. Geophysics 63, 2120-2128. Kennedy, W.D., Herrick, D.C., 2004. Petrophysics 45, 38-58. Lee, M.W., 2009. U.S. Geological Survey Scientific Investigations Report 2009-5141, 13. Sain, K., Singh, A.K., Thakur, N.K., Khanna, R.K., 2009.Marine Geophysical Researches 30, 137-145.

Working times of elastomeric impression materials determined by dimensional accuracy.

PubMed

Tan, E; Chai, J; Wozniak, W T

1996-01-01

The working times of five poly(vinyl siloxane) impression materials were estimated by evaluating the dimensional accuracy of stone dies of impressions of a standard model made at successive time intervals. The stainless steel standard model was represented by two abutments having known distances between landmarks in three dimensions. Three dimensions in the x-, y-, and z-axes of the stone dies were measured with a traveling microscope. A time interval was rejected as being within the working time if the percentage change of the resultant dies, in any dimension, was statistically different from those measured from stone dies from previous time intervals. The absolute dimensions of those dies from the rejected time interval also must have exceeded all those from previous time intervals. Results showed that the working times estimated with this method generally were about 30 seconds longer than those recommended by the manufacturers.

New Technique for TOC Estimation Based on Thermal Core Logging in Low-Permeable Formations (Bazhen fm.)

NASA Astrophysics Data System (ADS)

Popov, Evgeny; Popov, Yury; Spasennykh, Mikhail; Kozlova, Elena; Chekhonin, Evgeny; Zagranovskaya, Dzhuliya; Belenkaya, Irina; Alekseev, Aleksey

2016-04-01

A practical method of organic-rich intervals identifying within the low-permeable dispersive rocks based on thermal conductivity measurements along the core is presented. Non-destructive non-contact thermal core logging was performed with optical scanning technique on 4 685 full size core samples from 7 wells drilled in four low-permeable zones of the Bazhen formation (B.fm.) in the Western Siberia (Russia). The method employs continuous simultaneous measurements of rock anisotropy, volumetric heat capacity, thermal anisotropy coefficient and thermal heterogeneity factor along the cores allowing the high vertical resolution (of up to 1-2 mm). B.fm. rock matrix thermal conductivity was observed to be essentially stable within the range of 2.5-2.7 W/(m*K). However, stable matrix thermal conductivity along with the high thermal anisotropy coefficient is characteristic for B.fm. sediments due to the low rock porosity values. It is shown experimentally that thermal parameters measured relate linearly to organic richness rather than to porosity coefficient deviations. Thus, a new technique employing the transformation of the thermal conductivity profiles into continuous profiles of total organic carbon (TOC) values along the core was developed. Comparison of TOC values, estimated from the thermal conductivity values, with experimental pyrolytic TOC estimations of 665 samples from the cores using the Rock-Eval and HAWK instruments demonstrated high efficiency of the new technique for the organic rich intervals separation. The data obtained with the new technique are essential for the SR hydrocarbon generation potential, for basin and petroleum system modeling application, and estimation of hydrocarbon reserves. The method allows for the TOC richness to be accurately assessed using the thermal well logs. The research work was done with financial support of the Russian Ministry of Education and Science (unique identification number RFMEFI58114X0008).

Earth before life.

PubMed

Marzban, Caren; Viswanathan, Raju; Yurtsever, Ulvi

2014-01-09

A recent study argued, based on data on functional genome size of major phyla, that there is evidence life may have originated significantly prior to the formation of the Earth. Here a more refined regression analysis is performed in which 1) measurement error is systematically taken into account, and 2) interval estimates (e.g., confidence or prediction intervals) are produced. It is shown that such models for which the interval estimate for the time origin of the genome includes the age of the Earth are consistent with observed data. The appearance of life after the formation of the Earth is consistent with the data set under examination.

Proportion of general factor variance in a hierarchical multiple-component measuring instrument: a note on a confidence interval estimation procedure.

PubMed

Raykov, Tenko; Zinbarg, Richard E

2011-05-01

A confidence interval construction procedure for the proportion of explained variance by a hierarchical, general factor in a multi-component measuring instrument is outlined. The method provides point and interval estimates for the proportion of total scale score variance that is accounted for by the general factor, which could be viewed as common to all components. The approach may also be used for testing composite (one-tailed) or simple hypotheses about this proportion, and is illustrated with a pair of examples. ©2010 The British Psychological Society.

Assessing heat load in drylot dairy cattle: Refining on-farm sampling methodology.

PubMed

Tresoldi, Grazyne; Schütz, Karin E; Tucker, Cassandra B

2016-11-01

Identifying dairy cattle experiencing heat stress and adopting appropriate mitigation strategies can improve welfare and profitability. However, little is known about how cattle use heat abatement resources (shade, sprayed water) on drylot dairies. It is also unclear how often we need to observe animals to measure high heat load, or the relevance of specific aspects of this response, particularly in terms of panting. Our objectives were to describe and determine sampling intervals to measure cattle use of heat abatement resources, respiration rate (RR) and panting characteristics (drooling, open mouth, protruding tongue), and to evaluate the relationship between the latter 2. High-producing cows were chosen from 4 drylots (8 cows/dairy, n=32) and observed for at least 5.9h (1000 to 1800h, excluding milking) when air temperature, humidity, and the combined index averaged 33°C, 30%, and 79, respectively. Use of heat abatement resources was recorded continuously; RR and the presence and absence of each panting characteristic were recorded every 5min. From the observed values, estimates using the specified sub-sampling intervals were calculated for heat abatement resource use (1, 5, 10, 15, 20, 30, 60, 90, and 120min), and for RR and panting (10, 15, 20, 30, 60, 90, and 120min). Estimates and observed values were compared using linear regression. Sampling intervals were considered accurate if they met 3 criteria: R 2 ≥0.9, intercept=0, and slope=1. The relationship between RR and each panting characteristic was analyzed using mixed models. Cows used shade (at corral or over feed bunk) and feed bunk area (where water was sprayed) for about 90 and 50% of the observed time, respectively, and used areas with no cooling for 2min at a time, on average. Cows exhibited drooling (34±4% of observations) more often than open mouth and protruding tongue (11±3 and 8±3% of observations, respectively). Respiration rate varied depending on the presence of panting (with vs. without drool present: 97±3 vs. 74±3 breaths/min; open vs. closed mouth: 104±4 vs. 85±4 breaths/min; protruding vs. non-protruding tongue: 105±5 vs. 91±5 breaths/min). Accurate estimates were obtained when using sampling intervals ≤90min for RR, ≤60min for corral shade and sprayed water use, and ≤30min for drooling. In a hot and dry climate, cows kept in drylots had higher RR when showing panting characteristics than when these were absent, and used shade extensively, avoiding areas with no cooling. In general, 30min intervals were most efficient for measuring heat load responses. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Systematic search for wide periodic windows and bounds for the set of regular parameters for the quadratic map.

PubMed

Galias, Zbigniew

2017-05-01

An efficient method to find positions of periodic windows for the quadratic map f(x)=ax(1-x) and a heuristic algorithm to locate the majority of wide periodic windows are proposed. Accurate rigorous bounds of positions of all periodic windows with periods below 37 and the majority of wide periodic windows with longer periods are found. Based on these results, we prove that the measure of the set of regular parameters in the interval [3,4] is above 0.613960137. The properties of periodic windows are studied numerically. The results of the analysis are used to estimate that the true value of the measure of the set of regular parameters is close to 0.6139603.

Non-invasive genetic censusing and monitoring of primate populations.

PubMed

Arandjelovic, Mimi; Vigilant, Linda

2018-03-01

Knowing the density or abundance of primate populations is essential for their conservation management and contextualizing socio-demographic and behavioral observations. When direct counts of animals are not possible, genetic analysis of non-invasive samples collected from wildlife populations allows estimates of population size with higher accuracy and precision than is possible using indirect signs. Furthermore, in contrast to traditional indirect survey methods, prolonged or periodic genetic sampling across months or years enables inference of group membership, movement, dynamics, and some kin relationships. Data may also be used to estimate sex ratios, sex differences in dispersal distances, and detect gene flow among locations. Recent advances in capture-recapture models have further improved the precision of population estimates derived from non-invasive samples. Simulations using these methods have shown that the confidence interval of point estimates includes the true population size when assumptions of the models are met, and therefore this range of population size minima and maxima should be emphasized in population monitoring studies. Innovations such as the use of sniffer dogs or anti-poaching patrols for sample collection are important to ensure adequate sampling, and the expected development of efficient and cost-effective genotyping by sequencing methods for DNAs derived from non-invasive samples will automate and speed analyses. © 2018 Wiley Periodicals, Inc.

Improved adjoin-list for quality-guided phase unwrapping based on red-black trees

NASA Astrophysics Data System (ADS)

Cruz-Santos, William; López-García, Lourdes; Rueda-Paz, Juvenal; Redondo-Galvan, Arturo

2016-08-01

The quality-guide phase unwrapping is an important technique that is based on quality maps which guide the unwrapping process. The efficiency of this technique depends in the adjoin-list data structure implementation. There exists several proposals that improve the adjoin-list; Ming Zhao et. al. proposed an Indexed Interwoven Linked List (I2L2) that is based on dividing the quality values into intervals of equal size and inserting in a linked list those pixels with quality values within a certain interval. Ming Zhao and Qian Kemao proposed an improved I2L2 replacing each linked list in each interval by a heap data structure, which allows efficient procedures for insertion and deletion. In this paper, we propose an improved I2L2 which uses Red-Black trees (RBT) data structures for each interval. Our proposal has as main goal to avoid the unbalanced properties of the head and thus, reducing the time complexity of insertion. In order to maintain the same efficiency of the heap when deleting an element, we provide an efficient way to remove the pixel with the highest quality value in the RBT using a pointer to the rightmost element in the tree. We also provide a new partition strategy of the phase values that is based on a density criterion. Experimental results applied to phase shifting profilometry are shown for large images.

The Incidence, Prevalence and Burden of OM in Unselected Children Aged 1 to 8 Years Followed by Weekly Otoscopy through the “Common Cold” Season

PubMed Central

Mandel, Ellen M.; Doyle, William J.; Winther, Birgit; Alper, Cuneyt M.

2008-01-01

Background There is a continuing interest in defining the incidence, prevalence and burden of otitis media (OM) in the individual and population for purposes of assigning “risk factors”. Often overlooked in past studies are the contributions of cold-like illnesses (CLIs) and sampling interval to those estimates. Objective Describe the incidence of symptomatic (AOM) and asymptomatic (OME) OM, the prevalence of OM, the contribution of CLI incidence, burden and other OM “risk factors” to the incidence and burden of OM, and the effect of sampling interval on those measures in children. Methods 148 children (74 male; 131 white, aged 1.0–8.6 years) were followed from November 1 to April 30 by weekly pneumatic otoscopy to diagnose OM presence/absence and by daily parental diary to assign CLI episodes. Data for previously identified OM “risk factors” were collected on 127. Results were summarized using standard measures of incidence, prevalence and burden, and multiple-regression techniques were used to identify OM “risk factors”. Results The basal OM prevalence was 20% with peaks in December and March and the temporal pattern was correlated with CLI prevalence. The incidence of OME (per 27232 child-days) was 317, AOM was 74 and CLI was 456. The seasonal pattern of AOM and OME incidences tracked and was correlated with that for CLIs. New OM episodes were usually of short duration (≤7 days in 40%, ≤4 weeks in 75–90%) and the usual OM burden was low (median=12%). OM and breastfeeding histories and CLI incidence/prevalence were significant predictors of OME and AOM incidence and OM burden. Longer sampling intervals were less efficient in capturing AOM and OME durations and incidences, but not OM burden. Conclusions These results demonstrate a high incidence and prevalence of OM, most OM episodes were of short duration and longer sampling intervals introduced biases into some parameter estimates. There was a significant relationship between OM and CLI incidence, prevalence and burden suggesting that CLI experience should be controlled for in assessing independent “risk factors” for AOM and OME. PMID:18272237

Estimating the intensity of a cyclic Poisson process in the presence of additive and multiplicative linear trend

NASA Astrophysics Data System (ADS)

Wayan Mangku, I.

2017-10-01

In this paper we survey some results on estimation of the intensity function of a cyclic Poisson process in the presence of additive and multiplicative linear trend. We do not assume any parametric form for the cyclic component of the intensity function, except that it is periodic. Moreover, we consider the case when there is only a single realization of the Poisson process is observed in a bounded interval. The considered estimators are weakly and strongly consistent when the size of the observation interval indefinitely expands. Asymptotic approximations to the bias and variance of those estimators are presented.

Behavior, passage, and downstream migration of juvenile Chinook salmon from Detroit Reservoir to Portland, Oregon, 2014–15

USGS Publications Warehouse

Kock, Tobias J.; Beeman, John W.; Hansen, Amy C.; Hansel, Hal C.; Hansen, Gabriel S.; Hatton, Tyson W.; Kofoot, Eric E.; Sholtis, Matthew D.; Sprando, Jamie M.

2015-11-16

A Cormack-Jolly-Seber mark-recapture model was developed to provide reach-specific survival estimates for juvenile Chinook salmon. A portion of the tagged population overwintered in the Willamette River Basin and outmigrated several months after release. As a result, survival estimates from the model would have been negatively biased by factors such as acoustic tag failure and tag loss. Data from laboratory studies were incorporated into the model to provide survival estimates that accounted for these factors. In the North Santiam River between Minto Dam and the Bennett Dam complex, a distance of 37.2 kilometers, survival was estimated to be 0.844 (95-percent confidence interval 0.795–0.893). The survival estimate for the 203.7 kilometer reach between the Bennett Dam complex and Portland, Oregon, was 0.279 (95-percent confidence interval 0.234–0.324), and included portions of the North Santiam, Santiam, and Willamette Rivers. The cumulative survival estimate in the 240.9 kilometer reach from the Minto Dam tailrace to Portland was 0.236 (95-percent confidence interval 0.197–0.275).

Parametric Model Based On Imputations Techniques for Partly Interval Censored Data

NASA Astrophysics Data System (ADS)

Zyoud, Abdallah; Elfaki, F. A. M.; Hrairi, Meftah

2017-12-01

The term ‘survival analysis’ has been used in a broad sense to describe collection of statistical procedures for data analysis. In this case, outcome variable of interest is time until an event occurs where the time to failure of a specific experimental unit might be censored which can be right, left, interval, and Partly Interval Censored data (PIC). In this paper, analysis of this model was conducted based on parametric Cox model via PIC data. Moreover, several imputation techniques were used, which are: midpoint, left & right point, random, mean, and median. Maximum likelihood estimate was considered to obtain the estimated survival function. These estimations were then compared with the existing model, such as: Turnbull and Cox model based on clinical trial data (breast cancer data), for which it showed the validity of the proposed model. Result of data set indicated that the parametric of Cox model proved to be more superior in terms of estimation of survival functions, likelihood ratio tests, and their P-values. Moreover, based on imputation techniques; the midpoint, random, mean, and median showed better results with respect to the estimation of survival function.

Immediate Vascular Imaging Needed for Efficient Triage of Patients With Acute Ischemic Stroke Initially Admitted to Nonthrombectomy Centers.

PubMed

Boulouis, Gregoire; Siddiqui, Khawja-Ahmeruddin; Lauer, Arne; Charidimou, Andreas; Regenhardt, Robert W; Viswanathan, Anand; Leslie-Mazwi, Thabele M; Rost, Natalia; Schwamm, Lee H

2017-08-01

Current guidelines for endovascular thrombectomy (EVT) used to select patients for transfer to thrombectomy-capable stroke centers (TSC) may result in unnecessary transfers. We sought to determine the impact of simulated baseline vascular imaging on reducing unnecessary transfers and clinical-imaging factors associated with receiving EVT after transfer. We identified patients with stroke transferred for EVT from 30 referring hospitals between 2010 and 2016 who had a referring hospitals brain computed tomography and repeat imaging on TSC arrival available for review. Initial Alberta Stroke Program Early CT scores and TSC vascular occlusion level were assessed. The main outcome variable was receiving EVT at TSC. Models were simulated to derive optimal triaging parameters for EVT. A total of 508 patients were included in the analysis (mean age, 69±14 years; 42% women). Application at referring hospitals of current guidelines for EVT yielded sensitivity of 92% (95% confidence interval, 0.84-0.96) and specificity of 53% (95% confidence interval, 0.48-0.57) for receiving EVT at TSC. Repeated simulations identified optimal selection criteria for transfer as National Institute of Health Stroke Scale >8 plus baseline vascular imaging (sensitivity=91%; 95% confidence interval, 0.83-0.95; and specificity=80%; 95% confidence interval, 0.75-0.83). Our findings provide quantitative estimates of the claim that implementing vascular imaging at the referring hospitals would result in significantly fewer futile transfers for EVT and a data-driven framework to inform transfer policies. © 2017 American Heart Association, Inc.

Evaluating the Impact of Guessing and Its Interactions with Other Test Characteristics on Confidence Interval Procedures for Coefficient Alpha

ERIC Educational Resources Information Center

Paek, Insu

2016-01-01

The effect of guessing on the point estimate of coefficient alpha has been studied in the literature, but the impact of guessing and its interactions with other test characteristics on the interval estimators for coefficient alpha has not been fully investigated. This study examined the impact of guessing and its interactions with other test…

Computational methods for a three-dimensional model of the petroleum-discovery process

USGS Publications Warehouse

Schuenemeyer, J.H.; Bawiec, W.J.; Drew, L.J.

1980-01-01

A discovery-process model devised by Drew, Schuenemeyer, and Root can be used to predict the amount of petroleum to be discovered in a basin from some future level of exploratory effort: the predictions are based on historical drilling and discovery data. Because marginal costs of discovery and production are a function of field size, the model can be used to make estimates of future discoveries within deposit size classes. The modeling approach is a geometric one in which the area searched is a function of the size and shape of the targets being sought. A high correlation is assumed between the surface-projection area of the fields and the volume of petroleum. To predict how much oil remains to be found, the area searched must be computed, and the basin size and discovery efficiency must be estimated. The basin is assumed to be explored randomly rather than by pattern drilling. The model may be used to compute independent estimates of future oil at different depth intervals for a play involving multiple producing horizons. We have written FORTRAN computer programs that are used with Drew, Schuenemeyer, and Root's model to merge the discovery and drilling information and perform the necessary computations to estimate undiscovered petroleum. These program may be modified easily for the estimation of remaining quantities of commodities other than petroleum. ?? 1980.

A Solution Space for a System of Null-State Partial Differential Equations: Part 2

NASA Astrophysics Data System (ADS)

Flores, Steven M.; Kleban, Peter

2015-01-01

This article is the second of four that completely and rigorously characterize a solution space for a homogeneous system of 2 N + 3 linear partial differential equations in 2 N variables that arises in conformal field theory (CFT) and multiple Schramm-Löwner evolution (SLE). The system comprises 2 N null-state equations and three conformal Ward identities which govern CFT correlation functions of 2 N one-leg boundary operators. In the first article (Flores and Kleban, Commun Math Phys, arXiv:1212.2301, 2012), we use methods of analysis and linear algebra to prove that dim , with C N the Nth Catalan number. The analysis of that article is complete except for the proof of a lemma that it invokes. The purpose of this article is to provide that proof. The lemma states that if every interval among ( x 2, x 3), ( x 3, x 4),…,( x 2 N-1, x 2 N ) is a two-leg interval of (defined in Flores and Kleban, Commun Math Phys, arXiv:1212.2301, 2012), then F vanishes. Proving this lemma by contradiction, we show that the existence of such a nonzero function implies the existence of a non-vanishing CFT two-point function involving primary operators with different conformal weights, an impossibility. This proof (which is rigorous in spite of our occasional reference to CFT) involves two different types of estimates, those that give the asymptotic behavior of F as the length of one interval vanishes, and those that give this behavior as the lengths of two intervals vanish simultaneously. We derive these estimates by using Green functions to rewrite certain null-state PDEs as integral equations, combining other null-state PDEs to obtain Schauder interior estimates, and then repeatedly integrating the integral equations with these estimates until we obtain optimal bounds. Estimates in which two interval lengths vanish simultaneously divide into two cases: two adjacent intervals and two non-adjacent intervals. The analysis of the latter case is similar to that for one vanishing interval length. In contrast, the analysis of the former case is more complicated, involving a Green function that contains the Jacobi heat kernel as its essential ingredient.

Establishing Lower Developmental Thresholds for a Common BlowFly: For Use in Estimating Elapsed Time since Death Using Entomologyical Methods

DTIC Science & Technology

2011-10-01

Abstract …….. Forensic entomology is a science used to estimate a post-mortem interval (PMI). Larvae develop at predictable rates and the time interval...Warren,Jodie; DRDC CSS CR 2011-23; Defence R&D Canada – CSS; October 2011. Introduction or background: Forensic entomology is the study of insects...in Europe since the 1850’s. Forensic entomology is now an integral part of a death investigation when estimating a time since death beyond 72 hours

Information transmission in hippocampal CA1 neuron models in the presence of poisson shot noise: the case of periodic sub-threshold spike trains.

PubMed

Kawaguchi, Minato; Mino, Hiroyuki; Durand, Dominique M

2006-01-01

This article presents an analysis of the information transmission of periodic sub-threshold spike trains in a hippocampal CA1 neuron model in the presence of a homogeneous Poisson shot noise. In the computer simulation, periodic sub-threshold spike trains were presented repeatedly to the midpoint of the main apical branch, while the homogeneous Poisson shot noise was applied to the mid-point of a basal dendrite in the CA1 neuron model consisting of the soma with one sodium, one calcium, and five potassium channels. From spike firing times recorded at the soma, the inter spike intervals were generated and then the probability, p(T), of the inter-spike interval histogram corresponding to the spike interval, r, of the periodic input spike trains was estimated to obtain an index of information transmission. In the present article, it is shown that at a specific amplitude of the homogeneous Poisson shot noise, p(T) was found to be maximized, as well as the possibility to encode the periodic sub-threshold spike trains became greater. It was implied that setting the amplitude of the homogeneous Poisson shot noise to the specific values which maximize the information transmission might contribute to efficiently encoding the periodic sub-threshold spike trains by utilizing the stochastic resonance.

Stability of INFIT and OUTFIT Compared to Simulated Estimates in Applied Setting.

PubMed

Hodge, Kari J; Morgan, Grant B

Residual-based fit statistics are commonly used as an indication of the extent to which the item response data fit the Rash model. Fit statistic estimates are influenced by sample size and rules-of thumb estimates may result in incorrect conclusions about the extent to which the model fits the data. Estimates obtained in this analysis were compared to 250 simulated data sets to examine the stability of the estimates. All INFIT estimates were within the rule-of-thumb range of 0.7 to 1.3. However, only 82% of the INFIT estimates fell within the 2.5th and 97.5th percentile of the simulated item's INFIT distributions using this 95% confidence-like interval. This is a 18 percentage point difference in items that were classified as acceptable. Fourty-eight percent of OUTFIT estimates fell within the 0.7 to 1.3 rule- of-thumb range. Whereas 34% of OUTFIT estimates fell within the 2.5th and 97.5th percentile of the simulated item's OUTFIT distributions. This is a 13 percentage point difference in items that were classified as acceptable. When using the rule-of- thumb ranges for fit estimates the magnitude of misfit was smaller than with the 95% confidence interval of the simulated distribution. The findings indicate that the use of confidence intervals as critical values for fit statistics leads to different model data fit conclusions than traditional rule of thumb critical values.

Finite-sample corrected generalized estimating equation of population average treatment effects in stepped wedge cluster randomized trials.

PubMed

Scott, JoAnna M; deCamp, Allan; Juraska, Michal; Fay, Michael P; Gilbert, Peter B

2017-04-01

Stepped wedge designs are increasingly commonplace and advantageous for cluster randomized trials when it is both unethical to assign placebo, and it is logistically difficult to allocate an intervention simultaneously to many clusters. We study marginal mean models fit with generalized estimating equations for assessing treatment effectiveness in stepped wedge cluster randomized trials. This approach has advantages over the more commonly used mixed models that (1) the population-average parameters have an important interpretation for public health applications and (2) they avoid untestable assumptions on latent variable distributions and avoid parametric assumptions about error distributions, therefore, providing more robust evidence on treatment effects. However, cluster randomized trials typically have a small number of clusters, rendering the standard generalized estimating equation sandwich variance estimator biased and highly variable and hence yielding incorrect inferences. We study the usual asymptotic generalized estimating equation inferences (i.e., using sandwich variance estimators and asymptotic normality) and four small-sample corrections to generalized estimating equation for stepped wedge cluster randomized trials and for parallel cluster randomized trials as a comparison. We show by simulation that the small-sample corrections provide improvement, with one correction appearing to provide at least nominal coverage even with only 10 clusters per group. These results demonstrate the viability of the marginal mean approach for both stepped wedge and parallel cluster randomized trials. We also study the comparative performance of the corrected methods for stepped wedge and parallel designs, and describe how the methods can accommodate interval censoring of individual failure times and incorporate semiparametric efficient estimators.

Confidence intervals for population allele frequencies: the general case of sampling from a finite diploid population of any size.

PubMed

Fung, Tak; Keenan, Kevin

2014-01-01

The estimation of population allele frequencies using sample data forms a central component of studies in population genetics. These estimates can be used to test hypotheses on the evolutionary processes governing changes in genetic variation among populations. However, existing studies frequently do not account for sampling uncertainty in these estimates, thus compromising their utility. Incorporation of this uncertainty has been hindered by the lack of a method for constructing confidence intervals containing the population allele frequencies, for the general case of sampling from a finite diploid population of any size. In this study, we address this important knowledge gap by presenting a rigorous mathematical method to construct such confidence intervals. For a range of scenarios, the method is used to demonstrate that for a particular allele, in order to obtain accurate estimates within 0.05 of the population allele frequency with high probability (> or = 95%), a sample size of > 30 is often required. This analysis is augmented by an application of the method to empirical sample allele frequency data for two populations of the checkerspot butterfly (Melitaea cinxia L.), occupying meadows in Finland. For each population, the method is used to derive > or = 98.3% confidence intervals for the population frequencies of three alleles. These intervals are then used to construct two joint > or = 95% confidence regions, one for the set of three frequencies for each population. These regions are then used to derive a > or = 95%% confidence interval for Jost's D, a measure of genetic differentiation between the two populations. Overall, the results demonstrate the practical utility of the method with respect to informing sampling design and accounting for sampling uncertainty in studies of population genetics, important for scientific hypothesis-testing and also for risk-based natural resource management.

Quantifying uncertainty on sediment loads using bootstrap confidence intervals

NASA Astrophysics Data System (ADS)

Slaets, Johanna I. F.; Piepho, Hans-Peter; Schmitter, Petra; Hilger, Thomas; Cadisch, Georg

2017-01-01

Load estimates are more informative than constituent concentrations alone, as they allow quantification of on- and off-site impacts of environmental processes concerning pollutants, nutrients and sediment, such as soil fertility loss, reservoir sedimentation and irrigation channel siltation. While statistical models used to predict constituent concentrations have been developed considerably over the last few years, measures of uncertainty on constituent loads are rarely reported. Loads are the product of two predictions, constituent concentration and discharge, integrated over a time period, which does not make it straightforward to produce a standard error or a confidence interval. In this paper, a linear mixed model is used to estimate sediment concentrations. A bootstrap method is then developed that accounts for the uncertainty in the concentration and discharge predictions, allowing temporal correlation in the constituent data, and can be used when data transformations are required. The method was tested for a small watershed in Northwest Vietnam for the period 2010-2011. The results showed that confidence intervals were asymmetric, with the highest uncertainty in the upper limit, and that a load of 6262 Mg year-1 had a 95 % confidence interval of (4331, 12 267) in 2010 and a load of 5543 Mg an interval of (3593, 8975) in 2011. Additionally, the approach demonstrated that direct estimates from the data were biased downwards compared to bootstrap median estimates. These results imply that constituent loads predicted from regression-type water quality models could frequently be underestimating sediment yields and their environmental impact.

Interval Timing Deficits Assessed by Time Reproduction Dual Tasks as Cognitive Endophenotypes for Attention-Deficit/Hyperactivity Disorder

PubMed Central

Hwang-Gu, Shoou-Lian; Gau, Susan Shur-Fen

2015-01-01

The literature has suggested timing processing as a potential endophenotype for attention deficit/hyperactivity disorder (ADHD); however, whether the subjective internal clock speed presented by verbal estimation and limited attention capacity presented by time reproduction could be endophenotypes for ADHD is still unknown. We assessed 223 youths with DSM-IV ADHD (age range: 10-17 years), 105 unaffected siblings, and 84 typically developing (TD) youths using psychiatric interviews, intelligence tests, verbal estimation and time reproduction tasks (single task and simple and difficult dual tasks) at 5-second, 12-second, and 17-second intervals. We found that youths with ADHD tended to overestimate time in verbal estimation more than their unaffected siblings and TD youths, implying that fast subjective internal clock speed might be a characteristic of ADHD, rather than an endophenotype for ADHD. Youths with ADHD and their unaffected siblings were less precise in time reproduction dual tasks than TD youths. The magnitude of estimated errors in time reproduction was greater in youths with ADHD and their unaffected siblings than in TD youths, with an increased time interval at the 17-second interval and with increased task demands on both simple and difficult dual tasks versus the single task. Increased impaired time reproduction in dual tasks with increased intervals and task demands were shown in youths with ADHD and their unaffected siblings, suggesting that time reproduction deficits explained by limited attention capacity might be a useful endophenotype of ADHD. PMID:25992899

Using known populations of pronghorn to evaluate sampling plans and estimators

USGS Publications Warehouse

Kraft, K.M.; Johnson, D.H.; Samuelson, J.M.; Allen, S.H.

1995-01-01

Although sampling plans and estimators of abundance have good theoretical properties, their performance in real situations is rarely assessed because true population sizes are unknown. We evaluated widely used sampling plans and estimators of population size on 3 known clustered distributions of pronghorn (Antilocapra americana). Our criteria were accuracy of the estimate, coverage of 95% confidence intervals, and cost. Sampling plans were combinations of sampling intensities (16, 33, and 50%), sample selection (simple random sampling without replacement, systematic sampling, and probability proportional to size sampling with replacement), and stratification. We paired sampling plans with suitable estimators (simple, ratio, and probability proportional to size). We used area of the sampling unit as the auxiliary variable for the ratio and probability proportional to size estimators. All estimators were nearly unbiased, but precision was generally low (overall mean coefficient of variation [CV] = 29). Coverage of 95% confidence intervals was only 89% because of the highly skewed distribution of the pronghorn counts and small sample sizes, especially with stratification. Stratification combined with accurate estimates of optimal stratum sample sizes increased precision, reducing the mean CV from 33 without stratification to 25 with stratification; costs increased 23%. Precise results (mean CV = 13) but poor confidence interval coverage (83%) were obtained with simple and ratio estimators when the allocation scheme included all sampling units in the stratum containing most pronghorn. Although areas of the sampling units varied, ratio estimators and probability proportional to size sampling did not increase precision, possibly because of the clumped distribution of pronghorn. Managers should be cautious in using sampling plans and estimators to estimate abundance of aggregated populations.

Estimated Magnitudes and Recurrence Intervals of Peak Flows on the Mousam and Little Ossipee Rivers for the Flood of April 2007 in Southern Maine

USGS Publications Warehouse

Hodgkins, Glenn A.; Stewart, Gregory J.; Cohn, Timothy A.; Dudley, Robert W.

2007-01-01

Large amounts of rain fell on southern Maine from the afternoon of April 15, 2007, to the afternoon of April 16, 2007, causing substantial damage to houses, roads, and culverts. This report provides an estimate of the peak flows on two rivers in southern Maine--the Mousam River and the Little Ossipee River--because of their severe flooding. The April 2007 estimated peak flow of 9,230 ft3/s at the Mousam River near West Kennebunk had a recurrence interval between 100 and 500 years; 95-percent confidence limits for this flow ranged from 25 years to greater than 500 years. The April 2007 estimated peak flow of 8,220 ft3/s at the Little Ossipee River near South Limington had a recurrence interval between 100 and 500 years; 95-percent confidence limits for this flow ranged from 50 years to greater than 500 years.

Resampling methods in Microsoft Excel® for estimating reference intervals

PubMed Central

Theodorsson, Elvar

2015-01-01

Computer- intensive resampling/bootstrap methods are feasible when calculating reference intervals from non-Gaussian or small reference samples. Microsoft Excel® in version 2010 or later includes natural functions, which lend themselves well to this purpose including recommended interpolation procedures for estimating 2.5 and 97.5 percentiles.
The purpose of this paper is to introduce the reader to resampling estimation techniques in general and in using Microsoft Excel® 2010 for the purpose of estimating reference intervals in particular.
Parametric methods are preferable to resampling methods when the distributions of observations in the reference samples is Gaussian or can transformed to that distribution even when the number of reference samples is less than 120. Resampling methods are appropriate when the distribution of data from the reference samples is non-Gaussian and in case the number of reference individuals and corresponding samples are in the order of 40. At least 500-1000 random samples with replacement should be taken from the results of measurement of the reference samples. PMID:26527366

Resampling methods in Microsoft Excel® for estimating reference intervals.

PubMed

Theodorsson, Elvar

2015-01-01

Computer-intensive resampling/bootstrap methods are feasible when calculating reference intervals from non-Gaussian or small reference samples. Microsoft Excel® in version 2010 or later includes natural functions, which lend themselves well to this purpose including recommended interpolation procedures for estimating 2.5 and 97.5 percentiles. 
The purpose of this paper is to introduce the reader to resampling estimation techniques in general and in using Microsoft Excel® 2010 for the purpose of estimating reference intervals in particular.
 Parametric methods are preferable to resampling methods when the distributions of observations in the reference samples is Gaussian or can transformed to that distribution even when the number of reference samples is less than 120. Resampling methods are appropriate when the distribution of data from the reference samples is non-Gaussian and in case the number of reference individuals and corresponding samples are in the order of 40. At least 500-1000 random samples with replacement should be taken from the results of measurement of the reference samples.

Classical and Bayesian Seismic Yield Estimation: The 1998 Indian and Pakistani Tests

NASA Astrophysics Data System (ADS)

Shumway, R. H.

2001-10-01

- The nuclear tests in May, 1998, in India and Pakistan have stimulated a renewed interest in yield estimation, based on limited data from uncalibrated test sites. We study here the problem of estimating yields using classical and Bayesian methods developed by Shumway (1992), utilizing calibration data from the Semipalatinsk test site and measured magnitudes for the 1998 Indian and Pakistani tests given by Murphy (1998). Calibration is done using multivariate classical or Bayesian linear regression, depending on the availability of measured magnitude-yield data and prior information. Confidence intervals for the classical approach are derived applying an extension of Fieller's method suggested by Brown (1982). In the case where prior information is available, the posterior predictive magnitude densities are inverted to give posterior intervals for yield. Intervals obtained using the joint distribution of magnitudes are comparable to the single-magnitude estimates produced by Murphy (1998) and reinforce the conclusion that the announced yields of the Indian and Pakistani tests were too high.

Classical and Bayesian Seismic Yield Estimation: The 1998 Indian and Pakistani Tests

NASA Astrophysics Data System (ADS)

Shumway, R. H.

The nuclear tests in May, 1998, in India and Pakistan have stimulated a renewed interest in yield estimation, based on limited data from uncalibrated test sites. We study here the problem of estimating yields using classical and Bayesian methods developed by Shumway (1992), utilizing calibration data from the Semipalatinsk test site and measured magnitudes for the 1998 Indian and Pakistani tests given by Murphy (1998). Calibration is done using multivariate classical or Bayesian linear regression, depending on the availability of measured magnitude-yield data and prior information. Confidence intervals for the classical approach are derived applying an extension of Fieller's method suggested by Brown (1982). In the case where prior information is available, the posterior predictive magnitude densities are inverted to give posterior intervals for yield. Intervals obtained using the joint distribution of magnitudes are comparable to the single-magnitude estimates produced by Murphy (1998) and reinforce the conclusion that the announced yields of the Indian and Pakistani tests were too high.

A comparison of analysis methods to estimate contingency strength.

PubMed

Lloyd, Blair P; Staubitz, Johanna L; Tapp, Jon T

2018-05-09

To date, several data analysis methods have been used to estimate contingency strength, yet few studies have compared these methods directly. To compare the relative precision and sensitivity of four analysis methods (i.e., exhaustive event-based, nonexhaustive event-based, concurrent interval, concurrent+lag interval), we applied all methods to a simulated data set in which several response-dependent and response-independent schedules of reinforcement were programmed. We evaluated the degree to which contingency strength estimates produced from each method (a) corresponded with expected values for response-dependent schedules and (b) showed sensitivity to parametric manipulations of response-independent reinforcement. Results indicated both event-based methods produced contingency strength estimates that aligned with expected values for response-dependent schedules, but differed in sensitivity to response-independent reinforcement. The precision of interval-based methods varied by analysis method (concurrent vs. concurrent+lag) and schedule type (continuous vs. partial), and showed similar sensitivities to response-independent reinforcement. Recommendations and considerations for measuring contingencies are identified. © 2018 Society for the Experimental Analysis of Behavior.

Evaluation of Fish Movements, Migration Patterns, and Population Abundance with Streamwidth PIT Tag Interrogation Systems, Final Report 2002.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zydlewski, Gayle; Winter, Christiane; McClanahan, Dee

2003-02-01

Two remote Streamwidth PIT tag Interrogation systems (SPIs) were operated continuously for over one year to test the feasibility of these systems for generating movement, migration, survival and smolt production estimates for salmonids. A total of 1,588 juvenile (< 100 mm FL) naturally produced salmonids (7 coho salmon, 482 cutthroat trout, and 1,099 steelhead) were PIT tagged above the upstream-most SPI (9 sites approximately 1 linear km each) in Fall 2001. Age at tagging for wild caught cutthroat and steelhead was 1 year. SPIs were operating before any PIT tagged fish were released in the creek. Over 390,000 detections weremore » recorded from October 2001 to 31 July 2002. Efficiencies were site dependent, but overall detection efficiency for the creek was 97% with 95% confidence intervals of 91-100%. PIT tag detection efficiency ranged from 55-100% depending on the SPI and varied throughout the year with average efficiencies of 73% and 89%. SPI efficiency of PIT tag detection was not completely dependent on electronics noise levels or environmental conditions. Fish from all tagging locations were detected at the SPIs. Steelhead and cutthroat trout were primarily detected moving in the Spring (April-June) coincident with the anticipated smolt migration. Steelhead were also detected moving past SPIs at lower numbers in the Fall and Winter. Travel time between SPIs (downstream movement) was highly dependent on time of year. Travel time in the Spring was significantly faster (34.4 {+-} 7.0 hours) for all species than during any other time of year (763.1 {+-} 267.0 hours). Steelhead and cutthroat migrating in the Spring were the same age as those that did not migrate in the Spring. Peak of steelhead migration recorded at the two SPIs was 5/11 and 5/12 and the peak in the screw trap was recorded on 5/17. Steelhead smolt production estimates using SPIs (3,802 with 95% confidence intervals of 3,440 - 4,245) was similar to those using more standard screw trap methods (approximately 5,400). All species used the faster moving/deeper section of the creek at both SPIs. A backpack PIT tag detector was also developed and used as another remote 'recapture' for additional accuracy in estimating population survival and recapture probability. This unit was used at an approximate efficiency of 24% to survey the creek after the Spring migration. Twenty-five individual fish were re-located. All PIT tag data were used to calculate survival and recapture probabilities using the Cormack-Jolly-Seber population model. Survival for steelhead was high and recapture probability depended greatly on season. Probability of recapture was highest in Spring (29.5%) and relatively low in all other seasons (< 7% in Fall, Winter, and Summer). Wild steelhead PIT tagged in the field and returned to the laboratory had a tag retention rate of 97.6%. A laboratory study was designed to determine the effects of 3-sized PIT tags (12 mm, 20 mm, and 23 mm) on survival and growth of individuals. Survival from surgical implantation of 23 mm PIT tags was > 98% for fish (coho salmon and steelhead). Retention of 23 mm PIT tags was 100% for coho salmon and 89% for steelhead. For both coho and steelhead, growth rates during the first month were affected by tagging, but by the end of 2 months growth effects equalized for all tag sizes. Life history characteristics quantified with SPI techniques are comparable to standard techniques. For example, peaks of Spring migration for steelhead and cutthroat were amazingly similar to those reported from the screw trap. These techniques will enable application of less laborious methods which are more accurate at estimating life history parameters.« less

Evaluation of Fish Movements, Migration Patterns and Populations Abundance with Streamwidth PIT Tag Interrogation Systems, Final Report 2002.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zydlewski, Gayle B.; Casey, Sean

2003-02-01

Two remote Streamwidth PIT tag Interrogation systems (SPIs) were operated continuously for over one year to test the feasibility of these systems for generating movement, migration, survival and smolt production estimates for salmonids. A total of 1,588 juvenile (< 100 mm FL) naturally produced salmonids (7 coho salmon, 482 cutthroat trout, and 1,099 steelhead) were PIT tagged above the upstream-most SPI (9 sites approximately 1 linear km each) in Fall 2001. Age at tagging for wild caught cutthroat and steelhead was 1 year. SPIs were operating before any PIT tagged fish were released in the creek. Over 390,000 detections weremore » recorded from October 2001 to 31 July 2002. Efficiencies were site dependent, but overall detection efficiency for the creek was 97% with 95% confidence intervals of 91-100%. PIT tag detection efficiency ranged from 55-100% depending on the SPI and varied throughout the year with average efficiencies of 73% and 89%. SPI efficiency of PIT tag detection was not completely dependent on electronics noise levels or environmental conditions. Fish from all tagging locations were detected at the SPIs. Steelhead and cutthroat trout were primarily detected moving in the Spring (April-June) coincident with the anticipated smolt migration. Steelhead were also detected moving past SPIs at lower numbers in the Fall and Winter. Travel time between SPIs (downstream movement) was highly dependent on time of year. Travel time in the Spring was significantly faster (34.4 {+-} 7.0 hours) for all species than during any other time of year (763.1 {+-} 267.0 hours). Steelhead and cutthroat migrating in the Spring were the same age as those that did not migrate in the Spring. Peak of steelhead migration recorded at the two SPIs was 5/11 and 5/12 and the peak in the screw trap was recorded on 5/17. Steelhead smolt production estimates using SPIs (3,802 with 95% confidence intervals of 3,440-4,245) was similar to those using more standard screw trap methods (approximately 5,400). All species used the faster moving/deeper section of the creek at both SPIs. A backpack PIT tag detector was also developed and used as another remote ''recapture'' for additional accuracy in estimating population survival and recapture probability. This unit was used at an approximate efficiency of 24% to survey the creek after the Spring migration. Twenty-five individual fish were re-located. All PIT tag data were used to calculate survival and recapture probabilities using the Cormack-Jolly-Seber population model. Survival for steelhead was high and recapture probability depended greatly on season. Probability of recapture was highest in Spring (29.5%) and relatively low in all other seasons (< 7% in Fall, Winter, and Summer). Wild steelhead PIT tagged in the field and returned to the laboratory had a tag retention rate of 97.6%. A laboratory study was designed to determine the effects of 3-sized PIT tags (12 mm, 20 mm, and 23 mm) on survival and growth of individuals. Survival from surgical implantation of 23 mm PIT tags was > 98% for fish (coho salmon and steelhead). Retention of 23 mm PIT tags was 100% for coho salmon and 89% for steelhead. For both coho and steelhead, growth rates during the first month were affected by tagging, but by the end of 2 months growth effects equalized for all tag sizes. Life history characteristics quantified with SPI techniques are comparable to standard techniques. For example, peaks of Spring migration for steelhead and cutthroat were amazingly similar to those reported from the screw trap. These techniques will enable application of less laborious methods which are more accurate at estimating life history parameters.« less

Gulkana Glacier, Alaska-Mass balance, meteorology, and water measurements, 1997-2001

USGS Publications Warehouse

March, Rod S.; O'Neel, Shad

2011-01-01

The measured winter snow, maximum winter snow, net, and annual balances for 1997-2001 in the Gulkana Glacier basin are determined at specific points and over the entire glacier area using the meteorological, hydrological, and glaciological data. We provide descriptions of glacier geometry to aid in estimation of conventional and reference surface mass balances and descriptions of ice motion to aid in the understanding of the glacier's response to its changing geometry. These data provide annual estimates for area altitude distribution, equilibrium line altitude, and accumulation area ratio during the study interval. New determinations of historical area altitude distributions are given for 1900 and annually from 1966 to 2001. As original weather instrumentation is nearing the end of its deployment lifespan, we provide new estimates of overlap comparisons and precipitation catch efficiency. During 1997-2001, Gulkana Glacier showed a continued and accelerated negative mass balance trend, especially below the equilibrium line altitude where thinning was pronounced. Ice motion also slowed, which combined with the negative mass balance, resulted in glacier retreat under a warming climate. Average annual runoff augmentation by glacier shrinkage for 1997-2001 was 25 percent compared to the previous average of 13 percent, in accordance with the measured glacier volume reductions.

Assessment of type II diabetes mellitus using irregularly sampled measurements with missing data.

PubMed

Barazandegan, Melissa; Ekram, Fatemeh; Kwok, Ezra; Gopaluni, Bhushan; Tulsyan, Aditya

2015-04-01

Diabetes mellitus is one of the leading diseases in the developed world. In order to better regulate blood glucose in a diabetic patient, improved modelling of insulin-glucose dynamics is a key factor in the treatment of diabetes mellitus. In the current work, the insulin-glucose dynamics in type II diabetes mellitus can be modelled by using a stochastic nonlinear state-space model. Estimating the parameters of such a model is difficult as only a few blood glucose and insulin measurements per day are available in a non-clinical setting. Therefore, developing a predictive model of the blood glucose of a person with type II diabetes mellitus is important when the glucose and insulin concentrations are only available at irregular intervals. To overcome these difficulties, we resort to online sequential Monte Carlo (SMC) estimation of states and parameters of the state-space model for type II diabetic patients under various levels of randomly missing clinical data. Our results show that this method is efficient in monitoring and estimating the dynamics of the peripheral glucose, insulin and incretins concentration when 10, 25 and 50% of the simulated clinical data were randomly removed.

Doppler-based motion compensation algorithm for focusing the signature of a rotorcraft.

PubMed

Goldman, Geoffrey H

2013-02-01

A computationally efficient algorithm was developed and tested to compensate for the effects of motion on the acoustic signature of a rotorcraft. For target signatures with large spectral peaks that vary slowly in amplitude and have near constant frequency, the time-varying Doppler shift can be tracked and then removed from the data. The algorithm can be used to preprocess data for classification, tracking, and nulling algorithms. The algorithm was tested on rotorcraft data. The average instantaneous frequency of the first harmonic of a rotorcraft was tracked with a fixed-lag smoother. Then, state space estimates of the frequency were used to calculate a time warping that removed the effect of a time-varying Doppler shift from the data. The algorithm was evaluated by analyzing the increase in the amplitude of the harmonics in the spectrum of a rotorcraft. The results depended upon the frequency of the harmonics and the processing interval duration. Under good conditions, the results for the fundamental frequency of the target (~11 Hz) almost achieved an estimated upper bound. The results for higher frequency harmonics had larger increases in the amplitude of the peaks, but significantly lower than the estimated upper bounds.

A neural-network based estimator to search for primordial non-Gaussianity in Planck CMB maps

DOE Office of Scientific and Technical Information (OSTI.GOV)

Novaes, C.P.; Bernui, A.; Ferreira, I.S.

2015-09-01

We present an upgraded combined estimator, based on Minkowski Functionals and Neural Networks, with excellent performance in detecting primordial non-Gaussianity in simulated maps that also contain a weighted mixture of Galactic contaminations, besides real pixel's noise from Planck cosmic microwave background radiation data. We rigorously test the efficiency of our estimator considering several plausible scenarios for residual non-Gaussianities in the foreground-cleaned Planck maps, with the intuition to optimize the training procedure of the Neural Network to discriminate between contaminations with primordial and secondary non-Gaussian signatures. We look for constraints of primordial local non-Gaussianity at large angular scales in the foreground-cleanedmore » Planck maps. For the SMICA map we found f{sub NL} = 33 ± 23, at 1σ confidence level, in excellent agreement with the WMAP-9yr and Planck results. In addition, for the other three Planck maps we obtain similar constraints with values in the interval f{sub NL}  element of  [33, 41], concomitant with the fact that these maps manifest distinct features in reported analyses, like having different pixel's noise intensities.« less

Interval-based reconstruction for uncertainty quantification in PET

NASA Astrophysics Data System (ADS)

Kucharczak, Florentin; Loquin, Kevin; Buvat, Irène; Strauss, Olivier; Mariano-Goulart, Denis

2018-02-01

A new directed interval-based tomographic reconstruction algorithm, called non-additive interval based expectation maximization (NIBEM) is presented. It uses non-additive modeling of the forward operator that provides intervals instead of single-valued projections. The detailed approach is an extension of the maximum likelihood—expectation maximization algorithm based on intervals. The main motivation for this extension is that the resulting intervals have appealing properties for estimating the statistical uncertainty associated with the reconstructed activity values. After reviewing previously published theoretical concepts related to interval-based projectors, this paper describes the NIBEM algorithm and gives examples that highlight the properties and advantages of this interval valued reconstruction.

Age estimation in the living: Transition analysis on developing third molars.

PubMed

Tangmose, Sara; Thevissen, Patrick; Lynnerup, Niels; Willems, Guy; Boldsen, Jesper

2015-12-01

A radiographic assessment of third molar development is essential for differentiating between juveniles and adolescents in forensic age estimations. As the developmental stages of third molars are highly correlated, age estimates based on a combination of a full set of third molar scores are statistically complicated. Transition analysis (TA) is a statistical method developed for estimating age at death in skeletons, which combines several correlated developmental traits into one age estimate including a 95% prediction interval. The aim of this study was to evaluate the performance of TA in the living on a full set of third molar scores. A cross sectional sample of 854 panoramic radiographs, homogenously distributed by sex and age (15.0-24.0 years), were randomly split in two; a reference sample for obtaining age estimates including a 95% prediction interval according to TA; and a validation sample to test the age estimates against actual age. The mean inaccuracy of the age estimates was 1.82 years (±1.35) in males and 1.81 years (±1.44) in females. The mean bias was 0.55 years (±2.20) in males and 0.31 years (±2.30) in females. Of the actual ages, 93.7% of the males and 95.9% of the females (validation sample) fell within the 95% prediction interval. Moreover, at a sensitivity and specificity of 0.824 and 0.937 in males and 0.814 and 0.827 in females, TA performs well in differentiating between being a minor as opposed to an adult. Although accuracy does not outperform other methods, TA provides unbiased age estimates which minimize the risk of wrongly estimating minors as adults. Furthermore, when corrected ad hoc, TA produces appropriate prediction intervals. As TA allows expansion with additional traits, i.e. stages of development of the left hand-wrist and the clavicle, it has a great potential for future more accurate and reproducible age estimates, including an estimated probability of having attained the legal age limit of 18 years. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Satellite altimetry based rating curves throughout the entire Amazon basin

NASA Astrophysics Data System (ADS)

Paris, A.; Calmant, S.; Paiva, R. C.; Collischonn, W.; Silva, J. S.; Bonnet, M.; Seyler, F.

2013-05-01

The Amazonian basin is the largest hydrological basin all over the world. In the recent past years, the basin has experienced an unusual succession of extreme draughts and floods, which origin is still a matter of debate. Yet, the amount of data available is poor, both over time and space scales, due to factor like basin's size, access difficulty and so on. One of the major locks is to get discharge series distributed over the entire basin. Satellite altimetry can be used to improve our knowledge of the hydrological stream flow conditions in the basin, through rating curves. Rating curves are mathematical relationships between stage and discharge at a given place. The common way to determine the parameters of the relationship is to compute the non-linear regression between the discharge and stage series. In this study, the discharge data was obtained by simulation through the entire basin using the MGB-IPH model with TRMM Merge input rainfall data and assimilation of gage data, run from 1998 to 2010. The stage dataset is made of ~800 altimetry series at ENVISAT and JASON-2 virtual stations. Altimetry series span between 2002 and 2010. In the present work we present the benefits of using stochastic methods instead of probabilistic ones to determine a dataset of rating curve parameters which are consistent throughout the entire Amazon basin. The rating curve parameters have been computed using a parameter optimization technique based on Markov Chain Monte Carlo sampler and Bayesian inference scheme. This technique provides an estimate of the best parameters for the rating curve, but also their posterior probability distribution, allowing the determination of a credibility interval for the rating curve. Also is included in the rating curve determination the error over discharges estimates from the MGB-IPH model. These MGB-IPH errors come from either errors in the discharge derived from the gage readings or errors in the satellite rainfall estimates. The present experiment shows that the stochastic approach is more efficient than the determinist one. By using for the parameters prior credible intervals defined by the user, this method provides an estimate of best rating curve estimate without any unlikely parameter, and all sites achieved convergence before reaching the maximum number of model evaluations. Results were assessed trough the Nash Sutcliffe efficiency coefficient, applied both to discharge and logarithm of discharges. Most of the virtual stations had good or very good results, showing values of Ens going from 0.7 to 0.98. However, worse results were found at a few virtual stations, unveiling the necessity of investigating possibilities of segmentation of the rating curve, depending on the stage or the rising or recession limb, but also possible errors in the altimetry series.

Forecasting overhaul or replacement intervals based on estimated system failure intensity

NASA Astrophysics Data System (ADS)

Gannon, James M.

1994-12-01

System reliability can be expressed in terms of the pattern of failure events over time. Assuming a nonhomogeneous Poisson process and Weibull intensity function for complex repairable system failures, the degree of system deterioration can be approximated. Maximum likelihood estimators (MLE's) for the system Rate of Occurrence of Failure (ROCOF) function are presented. Evaluating the integral of the ROCOF over annual usage intervals yields the expected number of annual system failures. By associating a cost of failure with the expected number of failures, budget and program policy decisions can be made based on expected future maintenance costs. Monte Carlo simulation is used to estimate the range and the distribution of the net present value and internal rate of return of alternative cash flows based on the distributions of the cost inputs and confidence intervals of the MLE's.

Paleomagnetic and rock magnetic study of IODP Site U1408 in the Northwest Atlantic - toward the high-resolution relative paleointensity estimate during the middle Eocene

NASA Astrophysics Data System (ADS)

Yamamoto, Y.; Yamazaki, T.; Oda, H.

2015-12-01

We have conducted paleomagnetic and rock magnetic measurements on the sedimentary sections recovered from Integrated Ocean Drilling Program (IODP) Site U1408 in the Northwest Atlantic, off Newfoundland. The measurements were done on u-channel samples using a pass-through superconducting rock magnetometer in a manner that remanent magnetizations (natural, anhysteretic and isothermal remanent magnetizations: NRM, ARM and IRM) were subjected to stepwise alternating field (AF) demagnetizations up to 80 mT and are measured with 1 cm spacing at each step.The characteristic remanent magnetization (ChRM) was resolved after AF demagnetization of 20-30 mT for most of the studied interval. As a result, we could identify several polarity reversals which were able to be correlated with the geomagnetic polarity time scale by Gradstein et al. (2012) (Geologic Time Scale 2012), with referring the shipboard biostratigraphy (Norris et al., 2014). The interval at ~ 33-157 mcd (meter composite depth) was interpreted to cover the Chrons C18n.1n to C20n with missing Chron C19n because of the somewhat ambiguous magnetic signals at the interval at ~ 70-110 mcd. The correlation provided an age model inferring sedimentation rate of about 2-4 cm/kyr during these chrons.There is the interval that shows relatively constant ARM and IRM intensities as well as ratios of ARM to IRM (ARM/IRM): the interval at ~ 37-90 mcd resulted in ARM intensity of 0.2-0.4 A/m, IRM intensity of 1-2 A/m and ARM/IRM of 0.17-0.20. This interval corresponds to the Chron C18 and the estimated sedimentation rate of the interval is ~ 2 cm/kyr. It is expected that high-resolution relative paleointensity estimate during the middle Eocene is potentially possible. We will report a preliminary estimate.

speed-ne: Software to simulate and estimate genetic effective population size (Ne ) from linkage disequilibrium observed in single samples.

PubMed

Hamilton, Matthew B; Tartakovsky, Maria; Battocletti, Amy

2018-05-01

The genetic effective population size, N e , can be estimated from the average gametic disequilibrium (r2^) between pairs of loci, but such estimates require evaluation of assumptions and currently have few methods to estimate confidence intervals. speed-ne is a suite of matlab computer code functions to estimate Ne^ from r2^ with a graphical user interface and a rich set of outputs that aid in understanding data patterns and comparing multiple estimators. speed-ne includes functions to either generate or input simulated genotype data to facilitate comparative studies of Ne^ estimators under various population genetic scenarios. speed-ne was validated with data simulated under both time-forward and time-backward coalescent models of genetic drift. Three classes of estimators were compared with simulated data to examine several general questions: what are the impacts of microsatellite null alleles on Ne^, how should missing data be treated, and does disequilibrium contributed by reduced recombination among some loci in a sample impact Ne^. Estimators differed greatly in precision in the scenarios examined, and a widely employed Ne^ estimator exhibited the largest variances among replicate data sets. speed-ne implements several jackknife approaches to estimate confidence intervals, and simulated data showed that jackknifing over loci and jackknifing over individuals provided ~95% confidence interval coverage for some estimators and should be useful for empirical studies. speed-ne provides an open-source extensible tool for estimation of Ne^ from empirical genotype data and to conduct simulations of both microsatellite and single nucleotide polymorphism (SNP) data types to develop expectations and to compare Ne^ estimators. © 2018 John Wiley & Sons Ltd.

The efficacy of a novel mobile phone application for goldmann ptosis visual field interpretation.

PubMed

Maamari, Robi N; D'Ambrosio, Michael V; Joseph, Jeffrey M; Tao, Jeremiah P

2014-01-01

To evaluate the efficacy of a novel mobile phone application that calculates superior visual field defects on Goldmann visual field charts. Experimental study in which the mobile phone application and 14 oculoplastic surgeons interpreted the superior visual field defect in 10 Goldmann charts. Percent error of the mobile phone application and the oculoplastic surgeons' estimates were calculated compared with computer software computation of the actual defects. Precision and time efficiency of the application were evaluated by processing the same Goldmann visual field chart 10 repeated times. The mobile phone application was associated with a mean percent error of 1.98% (95% confidence interval[CI], 0.87%-3.10%) in superior visual field defect calculation. The average mean percent error of the oculoplastic surgeons' visual estimates was 19.75% (95% CI, 14.39%-25.11%). Oculoplastic surgeons, on average, underestimated the defect in all 10 Goldmann charts. There was high interobserver variance among oculoplastic surgeons. The percent error of the 10 repeated measurements on a single chart was 0.93% (95% CI, 0.40%-1.46%). The average time to process 1 chart was 12.9 seconds (95% CI, 10.9-15.0 seconds). The mobile phone application was highly accurate, precise, and time-efficient in calculating the percent superior visual field defect using Goldmann charts. Oculoplastic surgeon visual interpretations were highly inaccurate, highly variable, and usually underestimated the field vision loss.

Seasonal variation in size-dependent survival of juvenile Atlantic salmon (Salmo salar): Performance of multistate capture-mark-recapture models

USGS Publications Warehouse

Letcher, B.H.; Horton, G.E.

2008-01-01

We estimated the magnitude and shape of size-dependent survival (SDS) across multiple sampling intervals for two cohorts of stream-dwelling Atlantic salmon (Salmo salar) juveniles using multistate capture-mark-recapture (CMR) models. Simulations designed to test the effectiveness of multistate models for detecting SDS in our system indicated that error in SDS estimates was low and that both time-invariant and time-varying SDS could be detected with sample sizes of >250, average survival of >0.6, and average probability of capture of >0.6, except for cases of very strong SDS. In the field (N ??? 750, survival 0.6-0.8 among sampling intervals, probability of capture 0.6-0.8 among sampling occasions), about one-third of the sampling intervals showed evidence of SDS, with poorer survival of larger fish during the age-2+ autumn and quadratic survival (opposite direction between cohorts) during age-1+ spring. The varying magnitude and shape of SDS among sampling intervals suggest a potential mechanism for the maintenance of the very wide observed size distributions. Estimating SDS using multistate CMR models appears complementary to established approaches, can provide estimates with low error, and can be used to detect intermittent SDS. ?? 2008 NRC Canada.

Ongoing behavior predicts perceptual report of interval duration

PubMed Central

Gouvêa, Thiago S.; Monteiro, Tiago; Soares, Sofia; Atallah, Bassam V.; Paton, Joseph J.

2014-01-01

The ability to estimate the passage of time is essential for adaptive behavior in complex environments. Yet, it is not known how the brain encodes time over the durations necessary to explain animal behavior. Under temporally structured reinforcement schedules, animals tend to develop temporally structured behavior, and interval timing has been suggested to be accomplished by learning sequences of behavioral states. If this is true, trial to trial fluctuations in behavioral sequences should be predictive of fluctuations in time estimation. We trained rodents in an duration categorization task while continuously monitoring their behavior with a high speed camera. Animals developed highly reproducible behavioral sequences during the interval being timed. Moreover, those sequences were often predictive of perceptual report from early in the trial, providing support to the idea that animals may use learned behavioral patterns to estimate the duration of time intervals. To better resolve the issue, we propose that continuous and simultaneous behavioral and neural monitoring will enable identification of neural activity related to time perception that is not explained by ongoing behavior. PMID:24672473

Nonparametric estimation of median survival times with applications to multi-site or multi-center studies.

PubMed

Rahbar, Mohammad H; Choi, Sangbum; Hong, Chuan; Zhu, Liang; Jeon, Sangchoon; Gardiner, Joseph C

2018-01-01

We propose a nonparametric shrinkage estimator for the median survival times from several independent samples of right-censored data, which combines the samples and hypothesis information to improve the efficiency. We compare efficiency of the proposed shrinkage estimation procedure to unrestricted estimator and combined estimator through extensive simulation studies. Our results indicate that performance of these estimators depends on the strength of homogeneity of the medians. When homogeneity holds, the combined estimator is the most efficient estimator. However, it becomes inconsistent when homogeneity fails. On the other hand, the proposed shrinkage estimator remains efficient. Its efficiency decreases as the equality of the survival medians is deviated, but is expected to be as good as or equal to the unrestricted estimator. Our simulation studies also indicate that the proposed shrinkage estimator is robust to moderate levels of censoring. We demonstrate application of these methods to estimating median time for trauma patients to receive red blood cells in the Prospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study.

Nonparametric estimation of median survival times with applications to multi-site or multi-center studies

PubMed Central

Choi, Sangbum; Hong, Chuan; Zhu, Liang; Jeon, Sangchoon; Gardiner, Joseph C.

2018-01-01

We propose a nonparametric shrinkage estimator for the median survival times from several independent samples of right-censored data, which combines the samples and hypothesis information to improve the efficiency. We compare efficiency of the proposed shrinkage estimation procedure to unrestricted estimator and combined estimator through extensive simulation studies. Our results indicate that performance of these estimators depends on the strength of homogeneity of the medians. When homogeneity holds, the combined estimator is the most efficient estimator. However, it becomes inconsistent when homogeneity fails. On the other hand, the proposed shrinkage estimator remains efficient. Its efficiency decreases as the equality of the survival medians is deviated, but is expected to be as good as or equal to the unrestricted estimator. Our simulation studies also indicate that the proposed shrinkage estimator is robust to moderate levels of censoring. We demonstrate application of these methods to estimating median time for trauma patients to receive red blood cells in the Prospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study. PMID:29772007

TOPSIS-based consensus model for group decision-making with incomplete interval fuzzy preference relations.

PubMed

Liu, Fang; Zhang, Wei-Guo

2014-08-01

Due to the vagueness of real-world environments and the subjective nature of human judgments, it is natural for experts to estimate their judgements by using incomplete interval fuzzy preference relations. In this paper, based on the technique for order preference by similarity to ideal solution method, we present a consensus model for group decision-making (GDM) with incomplete interval fuzzy preference relations. To do this, we first define a new consistency measure for incomplete interval fuzzy preference relations. Second, a goal programming model is proposed to estimate the missing interval preference values and it is guided by the consistency property. Third, an ideal interval fuzzy preference relation is constructed by using the induced ordered weighted averaging operator, where the associated weights of characterizing the operator are based on the defined consistency measure. Fourth, a similarity degree between complete interval fuzzy preference relations and the ideal one is defined. The similarity degree is related to the associated weights, and used to aggregate the experts' preference relations in such a way that more importance is given to ones with the higher similarity degree. Finally, a new algorithm is given to solve the GDM problem with incomplete interval fuzzy preference relations, which is further applied to partnership selection in formation of virtual enterprises.

Confidence intervals and sample size calculations for the standardized mean difference effect size between two normal populations under heteroscedasticity.

PubMed

Shieh, G

2013-12-01

The use of effect sizes and associated confidence intervals in all empirical research has been strongly emphasized by journal publication guidelines. To help advance theory and practice in the social sciences, this article describes an improved procedure for constructing confidence intervals of the standardized mean difference effect size between two independent normal populations with unknown and possibly unequal variances. The presented approach has advantages over the existing formula in both theoretical justification and computational simplicity. In addition, simulation results show that the suggested one- and two-sided confidence intervals are more accurate in achieving the nominal coverage probability. The proposed estimation method provides a feasible alternative to the most commonly used measure of Cohen's d and the corresponding interval procedure when the assumption of homogeneous variances is not tenable. To further improve the potential applicability of the suggested methodology, the sample size procedures for precise interval estimation of the standardized mean difference are also delineated. The desired precision of a confidence interval is assessed with respect to the control of expected width and to the assurance probability of interval width within a designated value. Supplementary computer programs are developed to aid in the usefulness and implementation of the introduced techniques.

Confidence Intervals for True Scores Using the Skew-Normal Distribution

ERIC Educational Resources Information Center

Garcia-Perez, Miguel A.

2010-01-01

A recent comparative analysis of alternative interval estimation approaches and procedures has shown that confidence intervals (CIs) for true raw scores determined with the Score method--which uses the normal approximation to the binomial distribution--have actual coverage probabilities that are closest to their nominal level. It has also recently…

The Role of Short-Term Memory Capacity and Task Experience for Overconfidence in Judgment under Uncertainty

ERIC Educational Resources Information Center

Hansson, Patrik; Juslin, Peter; Winman, Anders

2008-01-01

Research with general knowledge items demonstrates extreme overconfidence when people estimate confidence intervals for unknown quantities, but close to zero overconfidence when the same intervals are assessed by probability judgment. In 3 experiments, the authors investigated if the overconfidence specific to confidence intervals derives from…

Applying Bootstrap Resampling to Compute Confidence Intervals for Various Statistics with R

ERIC Educational Resources Information Center

Dogan, C. Deha

2017-01-01

Background: Most of the studies in academic journals use p values to represent statistical significance. However, this is not a good indicator of practical significance. Although confidence intervals provide information about the precision of point estimation, they are, unfortunately, rarely used. The infrequent use of confidence intervals might…

Using an R Shiny to Enhance the Learning Experience of Confidence Intervals

ERIC Educational Resources Information Center

Williams, Immanuel James; Williams, Kelley Kim

2018-01-01

Many students find understanding confidence intervals difficult, especially because of the amalgamation of concepts such as confidence levels, standard error, point estimates and sample sizes. An R Shiny application was created to assist the learning process of confidence intervals using graphics and data from the US National Basketball…

Reporting Confidence Intervals and Effect Sizes: Collecting the Evidence

ERIC Educational Resources Information Center

Zientek, Linda Reichwein; Ozel, Z. Ebrar Yetkiner; Ozel, Serkan; Allen, Jeff

2012-01-01

Confidence intervals (CIs) and effect sizes are essential to encourage meta-analytic thinking and to accumulate research findings. CIs provide a range of plausible values for population parameters with a degree of confidence that the parameter is in that particular interval. CIs also give information about how precise the estimates are. Comparison…

Confidence Intervals for Weighted Composite Scores under the Compound Binomial Error Model

ERIC Educational Resources Information Center

Kim, Kyung Yong; Lee, Won-Chan

2018-01-01

Reporting confidence intervals with test scores helps test users make important decisions about examinees by providing information about the precision of test scores. Although a variety of estimation procedures based on the binomial error model are available for computing intervals for test scores, these procedures assume that items are randomly…

Accuracy in parameter estimation for targeted effects in structural equation modeling: sample size planning for narrow confidence intervals.

PubMed

Lai, Keke; Kelley, Ken

2011-06-01

In addition to evaluating a structural equation model (SEM) as a whole, often the model parameters are of interest and confidence intervals for those parameters are formed. Given a model with a good overall fit, it is entirely possible for the targeted effects of interest to have very wide confidence intervals, thus giving little information about the magnitude of the population targeted effects. With the goal of obtaining sufficiently narrow confidence intervals for the model parameters of interest, sample size planning methods for SEM are developed from the accuracy in parameter estimation approach. One method plans for the sample size so that the expected confidence interval width is sufficiently narrow. An extended procedure ensures that the obtained confidence interval will be no wider than desired, with some specified degree of assurance. A Monte Carlo simulation study was conducted that verified the effectiveness of the procedures in realistic situations. The methods developed have been implemented in the MBESS package in R so that they can be easily applied by researchers. © 2011 American Psychological Association

Estimation of the uncertainty of analyte concentration from the measurement uncertainty.

PubMed

Brown, Simon; Cooke, Delwyn G; Blackwell, Leonard F

2015-09-01

Ligand-binding assays, such as immunoassays, are usually analysed using standard curves based on the four-parameter and five-parameter logistic models. An estimate of the uncertainty of an analyte concentration obtained from such curves is needed for confidence intervals or precision profiles. Using a numerical simulation approach, it is shown that the uncertainty of the analyte concentration estimate becomes significant at the extremes of the concentration range and that this is affected significantly by the steepness of the standard curve. We also provide expressions for the coefficient of variation of the analyte concentration estimate from which confidence intervals and the precision profile can be obtained. Using three examples, we show that the expressions perform well.

16 CFR 305.5 - Determinations of estimated annual energy consumption, estimated annual operating cost, and...

Code of Federal Regulations, 2010 CFR

2010-01-01

... consumption, estimated annual operating cost, and energy efficiency rating, and of water use rate. 305.5... energy efficiency rating, and of water use rate. (a) Procedures for determining the estimated annual energy consumption, the estimated annual operating costs, the energy efficiency ratings, and the efficacy...

Global determination of rating curves in the Amazon basin from satellite altimetry

NASA Astrophysics Data System (ADS)

Paris, Adrien; Paiva, Rodrigo C. D.; Santos da Silva, Joecila; Medeiros Moreira, Daniel; Calmant, Stéphane; Collischonn, Walter; Bonnet, Marie-Paule; Seyler, Frédérique

2014-05-01

The Amazonian basin is the largest hydrological basin all over the world. Over the past few years, it has experienced an unusual succession of extreme droughts and floods, which origin is still a matter of debate. One of the major issues in understanding such events is to get discharge series distributed over the entire basin. Satellite altimetry can be used to improve our knowledge of the hydrological stream flow conditions in the basin, through rating curves. Rating curves are mathematical relationships between stage and discharge at a given place. The common way to determine the parameters of the relationship is to compute the non-linear regression between the discharge and stage series. In this study, the discharge data was obtained by simulation through the entire basin using the MGB-IPH model with TRMM Merge input rainfall data and assimilation of gage data, run from 1998 to 2009. The stage dataset is made of ~900 altimetry series at ENVISAT and Jason-2 virtual stations, sampling the stages over more than a hundred of rivers in the basin. Altimetry series span between 2002 and 2011. In the present work we present the benefits of using stochastic methods instead of probabilistic ones to determine a dataset of rating curve parameters which are hydrologicaly meaningful throughout the entire Amazon basin. The rating curve parameters have been computed using an optimization technique based on Markov Chain Monte Carlo sampler and Bayesian inference scheme. This technique provides an estimate of the best value for the parameters together with their posterior probability distribution, allowing the determination of a credibility interval for calculated discharge. Also the error over discharges estimates from the MGB-IPH model is included in the rating curve determination. These MGB-IPH errors come from either errors in the discharge derived from the gage readings or errors in the satellite rainfall estimates. The present experiment shows that the stochastic approach is more efficient than the determinist one. By using for the parameters prior credible intervals defined by the user, this method provides an estimate of best rating curve estimate without any unlikely parameter. Results were assessed trough the Nash Sutcliffe efficiency coefficient. Ens superior to 0.7 is found for most of the 920 virtual stations . From these results we were able to determinate a fully coherent map of river bed height, mean depth and Manning's roughness coefficient, information that can be reused in hydrological modeling. Bad results found at a few virtual stations are also of interest. For some sub-basins in the Andean piemont, the bad result confirms that the model failed to estimate discharges overthere. Other are found at tributary mouths experiencing backwater effects from the Amazon. Considering mean monthly slope at the virtual station in the rating curve equation, we obtain rated discharges much more consistent with modeled and measured ones, showing that it is now possible to obtain a meaningful rating curve in such critical areas.

Application of an Individual-Based Transmission Hazard Model for Estimation of Influenza Vaccine Effectiveness in a Household Cohort.

PubMed

Petrie, Joshua G; Eisenberg, Marisa C; Ng, Sophia; Malosh, Ryan E; Lee, Kyu Han; Ohmit, Suzanne E; Monto, Arnold S

2017-12-15

Household cohort studies are an important design for the study of respiratory virus transmission. Inferences from these studies can be improved through the use of mechanistic models to account for household structure and risk as an alternative to traditional regression models. We adapted a previously described individual-based transmission hazard (TH) model and assessed its utility for analyzing data from a household cohort maintained in part for study of influenza vaccine effectiveness (VE). Households with ≥4 individuals, including ≥2 children <18 years of age, were enrolled and followed during the 2010-2011 influenza season. VE was estimated in both TH and Cox proportional hazards (PH) models. For each individual, TH models estimated hazards of infection from the community and each infected household contact. Influenza A(H3N2) infection was laboratory-confirmed in 58 (4%) subjects. VE estimates from both models were similarly low overall (Cox PH: 20%, 95% confidence interval: -57, 59; TH: 27%, 95% credible interval: -23, 58) and highest for children <9 years of age (Cox PH: 40%, 95% confidence interval: -49, 76; TH: 52%, 95% credible interval: 7, 75). VE estimates were robust to model choice, although the ability of the TH model to accurately describe transmission of influenza presents continued opportunity for analyses. © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Allometric and temporal scaling of movement characteristics in Galapagos tortoises

USGS Publications Warehouse

Bastille-Rousseau, Guillaume; Yackulic, Charles B.; Frair, Jacqueline L.; Cabrera, Freddy; Blake, Stephen

2016-01-01

Understanding how individual movement scales with body size is of fundamental importance in predicting ecological relationships for diverse species. One-dimensional movement metrics scale consistently with body size yet vary over different temporal scales. Knowing how temporal scale influences the relationship between animal body size and movement would better inform hypotheses about the efficiency of foraging behaviour, the ontogeny of energy budgets, and numerous life-history trade-offs.We investigated how the temporal scaling of allometric patterns in movement varies over the course of a year, specifically during periods of motivated (directional and fast movement) and unmotivated (stationary and tortuous movement) behaviour. We focused on a recently diverged group of species that displays wide variation in movement behaviour – giant Galapagos tortoises (Chelonoidis spp.) – to test how movement metrics estimated on a monthly basis scaled with body size.We used state-space modelling to estimate seven different movement metrics of Galapagos tortoises. We used log-log regression of the power law to evaluate allometric scaling for these movement metrics and contrasted relationships by species and sex.Allometric scaling of movement was more apparent during motivated periods of movement. During this period, allometry was revealed at multiple temporal intervals (hourly, daily and monthly), with values observed at daily and monthly intervals corresponding most closely to the expected one-fourth scaling coefficient, albeit with wide credible intervals. We further detected differences in the magnitude of scaling among taxa uncoupled from observed differences in the temporal structuring of their movement rates.Our results indicate that the definition of temporal scales is fundamental to the detection of allometry of movement and should be given more attention in movement studies. Our approach not only provides new conceptual insights into temporal attributes in one-dimensional scaling of movement, but also generates valuable insights into the movement ecology of iconic yet poorly understood Galapagos giant tortoises.

Allometric and temporal scaling of movement characteristics in Galapagos tortoises.

PubMed

Bastille-Rousseau, Guillaume; Yackulic, Charles B; Frair, Jacqueline L; Cabrera, Freddy; Blake, Stephen

2016-09-01

Understanding how individual movement scales with body size is of fundamental importance in predicting ecological relationships for diverse species. One-dimensional movement metrics scale consistently with body size yet vary over different temporal scales. Knowing how temporal scale influences the relationship between animal body size and movement would better inform hypotheses about the efficiency of foraging behaviour, the ontogeny of energy budgets, and numerous life-history trade-offs. We investigated how the temporal scaling of allometric patterns in movement varies over the course of a year, specifically during periods of motivated (directional and fast movement) and unmotivated (stationary and tortuous movement) behaviour. We focused on a recently diverged group of species that displays wide variation in movement behaviour - giant Galapagos tortoises (Chelonoidis spp.) - to test how movement metrics estimated on a monthly basis scaled with body size. We used state-space modelling to estimate seven different movement metrics of Galapagos tortoises. We used log-log regression of the power law to evaluate allometric scaling for these movement metrics and contrasted relationships by species and sex. Allometric scaling of movement was more apparent during motivated periods of movement. During this period, allometry was revealed at multiple temporal intervals (hourly, daily and monthly), with values observed at daily and monthly intervals corresponding most closely to the expected one-fourth scaling coefficient, albeit with wide credible intervals. We further detected differences in the magnitude of scaling among taxa uncoupled from observed differences in the temporal structuring of their movement rates. Our results indicate that the definition of temporal scales is fundamental to the detection of allometry of movement and should be given more attention in movement studies. Our approach not only provides new conceptual insights into temporal attributes in one-dimensional scaling of movement, but also generates valuable insights into the movement ecology of iconic yet poorly understood Galapagos giant tortoises. © 2016 The Authors. Journal of Animal Ecology © 2016 British Ecological Society.

Real-Time PCR Quantification Using A Variable Reaction Efficiency Model

PubMed Central

Platts, Adrian E.; Johnson, Graham D.; Linnemann, Amelia K.; Krawetz, Stephen A.

2008-01-01

Quantitative real-time PCR remains a cornerstone technique in gene expression analysis and sequence characterization. Despite the importance of the approach to experimental biology the confident assignment of reaction efficiency to the early cycles of real-time PCR reactions remains problematic. Considerable noise may be generated where few cycles in the amplification are available to estimate peak efficiency. An alternate approach that uses data from beyond the log-linear amplification phase is explored with the aim of reducing noise and adding confidence to efficiency estimates. PCR reaction efficiency is regressed to estimate the per-cycle profile of an asymptotically departed peak efficiency, even when this is not closely approximated in the measurable cycles. The process can be repeated over replicates to develop a robust estimate of peak reaction efficiency. This leads to an estimate of the maximum reaction efficiency that may be considered primer-design specific. Using a series of biological scenarios we demonstrate that this approach can provide an accurate estimate of initial template concentration. PMID:18570886

The Time Is Up: Compression of Visual Time Interval Estimations of Bimodal Aperiodic Patterns

PubMed Central

Duarte, Fabiola; Lemus, Luis

2017-01-01

The ability to estimate time intervals subserves many of our behaviors and perceptual experiences. However, it is not clear how aperiodic (AP) stimuli affect our perception of time intervals across sensory modalities. To address this question, we evaluated the human capacity to discriminate between two acoustic (A), visual (V) or audiovisual (AV) time intervals of trains of scattered pulses. We first measured the periodicity of those stimuli and then sought for correlations with the accuracy and reaction times (RTs) of the subjects. We found that, for all time intervals tested in our experiment, the visual system consistently perceived AP stimuli as being shorter than the periodic (P) ones. In contrast, such a compression phenomenon was not apparent during auditory trials. Our conclusions are: first, the subjects exposed to P stimuli are more likely to measure their durations accurately. Second, perceptual time compression occurs for AP visual stimuli. Lastly, AV discriminations are determined by A dominance rather than by AV enhancement. PMID:28848406

Action and perception in social contexts: intentional binding for social action effects

PubMed Central

Pfister, Roland; Obhi, Sukhvinder S.; Rieger, Martina; Wenke, Dorit

2014-01-01

The subjective experience of controlling events in the environment alters the perception of these events. For instance, the interval between one's own actions and their consequences is subjectively compressed—a phenomenon known as intentional binding. In two experiments, we studied intentional binding in a social setting in which actions of one agent prompted a second agent to perform another action. Participants worked in pairs and were assigned to a “leader” and a “follower” role, respectively. The leader's key presses triggered (after a variable interval) a tone and this tone served as go signal for the follower to perform a keypress as well. Leaders and followers estimated the interval between the leader's keypress and the following tone, or the interval between the tone and the follower's keypress. The leader showed reliable intentional binding for both intervals relative to the follower's estimates. These results indicate that human agents experience a pre-reflective sense of agency for genuinely social consequences of their actions. PMID:25228869

Refining estimates of availability bias to improve assessments of the conservation status of an endangered dolphin.

PubMed

Sucunza, Federico; Danilewicz, Daniel; Cremer, Marta; Andriolo, Artur; Zerbini, Alexandre N

2018-01-01

Estimation of visibility bias is critical to accurately compute abundance of wild populations. The franciscana, Pontoporia blainvillei, is considered the most threatened small cetacean in the southwestern Atlantic Ocean. Aerial surveys are considered the most effective method to estimate abundance of this species, but many existing estimates have been considered unreliable because they lack proper estimation of correction factors for visibility bias. In this study, helicopter surveys were conducted to determine surfacing-diving intervals of franciscanas and to estimate availability for aerial platforms. Fifteen hours were flown and 101 groups of 1 to 7 franciscanas were monitored, resulting in a sample of 248 surface-dive cycles. The mean surfacing interval and diving interval times were 16.10 seconds (SE = 9.74) and 39.77 seconds (SE = 29.06), respectively. Availability was estimated at 0.39 (SE = 0.01), a value 16-46% greater than estimates computed from diving parameters obtained from boats or from land. Generalized mixed-effects models were used to investigate the influence of biological and environmental predictors on the proportion of time franciscana groups are visually available to be seen from an aerial platform. These models revealed that group size was the main factor influencing the proportion at surface. The use of negatively biased estimates of availability results in overestimation of abundance, leads to overly optimistic assessments of extinction probabilities and to potentially ineffective management actions. This study demonstrates that estimates of availability must be computed from suitable platforms to ensure proper conservation decisions are implemented to protect threatened species such as the franciscana.

Refining estimates of availability bias to improve assessments of the conservation status of an endangered dolphin

PubMed Central

Danilewicz, Daniel; Cremer, Marta; Andriolo, Artur; Zerbini, Alexandre N.

2018-01-01

Estimation of visibility bias is critical to accurately compute abundance of wild populations. The franciscana, Pontoporia blainvillei, is considered the most threatened small cetacean in the southwestern Atlantic Ocean. Aerial surveys are considered the most effective method to estimate abundance of this species, but many existing estimates have been considered unreliable because they lack proper estimation of correction factors for visibility bias. In this study, helicopter surveys were conducted to determine surfacing-diving intervals of franciscanas and to estimate availability for aerial platforms. Fifteen hours were flown and 101 groups of 1 to 7 franciscanas were monitored, resulting in a sample of 248 surface-dive cycles. The mean surfacing interval and diving interval times were 16.10 seconds (SE = 9.74) and 39.77 seconds (SE = 29.06), respectively. Availability was estimated at 0.39 (SE = 0.01), a value 16–46% greater than estimates computed from diving parameters obtained from boats or from land. Generalized mixed-effects models were used to investigate the influence of biological and environmental predictors on the proportion of time franciscana groups are visually available to be seen from an aerial platform. These models revealed that group size was the main factor influencing the proportion at surface. The use of negatively biased estimates of availability results in overestimation of abundance, leads to overly optimistic assessments of extinction probabilities and to potentially ineffective management actions. This study demonstrates that estimates of availability must be computed from suitable platforms to ensure proper conservation decisions are implemented to protect threatened species such as the franciscana. PMID:29534086

PMICALC: an R code-based software for estimating post-mortem interval (PMI) compatible with Windows, Mac and Linux operating systems.

PubMed

Muñoz-Barús, José I; Rodríguez-Calvo, María Sol; Suárez-Peñaranda, José M; Vieira, Duarte N; Cadarso-Suárez, Carmen; Febrero-Bande, Manuel

2010-01-30

In legal medicine the correct determination of the time of death is of utmost importance. Recent advances in estimating post-mortem interval (PMI) have made use of vitreous humour chemistry in conjunction with Linear Regression, but the results are questionable. In this paper we present PMICALC, an R code-based freeware package which estimates PMI in cadavers of recent death by measuring the concentrations of potassium ([K+]), hypoxanthine ([Hx]) and urea ([U]) in the vitreous humor using two different regression models: Additive Models (AM) and Support Vector Machine (SVM), which offer more flexibility than the previously used Linear Regression. The results from both models are better than those published to date and can give numerical expression of PMI with confidence intervals and graphic support within 20 min. The program also takes into account the cause of death. 2009 Elsevier Ireland Ltd. All rights reserved.

Generalized additive models and Lucilia sericata growth: assessing confidence intervals and error rates in forensic entomology.

PubMed

Tarone, Aaron M; Foran, David R

2008-07-01

Forensic entomologists use blow fly development to estimate a postmortem interval. Although accurate, fly age estimates can be imprecise for older developmental stages and no standard means of assigning confidence intervals exists. Presented here is a method for modeling growth of the forensically important blow fly Lucilia sericata, using generalized additive models (GAMs). Eighteen GAMs were created to predict the extent of juvenile fly development, encompassing developmental stage, length, weight, strain, and temperature data, collected from 2559 individuals. All measures were informative, explaining up to 92.6% of the deviance in the data, though strain and temperature exerted negligible influences. Predictions made with an independent data set allowed for a subsequent examination of error. Estimates using length and developmental stage were within 5% of true development percent during the feeding portion of the larval life cycle, while predictions for postfeeding third instars were less precise, but within expected error.

Empirical estimation of a distribution function with truncated and doubly interval-censored data and its application to AIDS studies.

PubMed

Sun, J

1995-09-01

In this paper we discuss the non-parametric estimation of a distribution function based on incomplete data for which the measurement origin of a survival time or the date of enrollment in a study is known only to belong to an interval. Also the survival time of interest itself is observed from a truncated distribution and is known only to lie in an interval. To estimate the distribution function, a simple self-consistency algorithm, a generalization of Turnbull's (1976, Journal of the Royal Statistical Association, Series B 38, 290-295) self-consistency algorithm, is proposed. This method is then used to analyze two AIDS cohort studies, for which direct use of the EM algorithm (Dempster, Laird and Rubin, 1976, Journal of the Royal Statistical Association, Series B 39, 1-38), which is computationally complicated, has previously been the usual method of the analysis.

Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting

NASA Astrophysics Data System (ADS)

Wani, Omar; Beckers, Joost V. L.; Weerts, Albrecht H.; Solomatine, Dimitri P.

2017-08-01

A non-parametric method is applied to quantify residual uncertainty in hydrologic streamflow forecasting. This method acts as a post-processor on deterministic model forecasts and generates a residual uncertainty distribution. Based on instance-based learning, it uses a k nearest-neighbour search for similar historical hydrometeorological conditions to determine uncertainty intervals from a set of historical errors, i.e. discrepancies between past forecast and observation. The performance of this method is assessed using test cases of hydrologic forecasting in two UK rivers: the Severn and Brue. Forecasts in retrospect were made and their uncertainties were estimated using kNN resampling and two alternative uncertainty estimators: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Results show that kNN uncertainty estimation produces accurate and narrow uncertainty intervals with good probability coverage. Analysis also shows that the performance of this technique depends on the choice of search space. Nevertheless, the accuracy and reliability of uncertainty intervals generated using kNN resampling are at least comparable to those produced by QR and UNEEC. It is concluded that kNN uncertainty estimation is an interesting alternative to other post-processors, like QR and UNEEC, for estimating forecast uncertainty. Apart from its concept being simple and well understood, an advantage of this method is that it is relatively easy to implement.

Uncertainty of exploitation estimates made from tag returns

USGS Publications Warehouse

Miranda, L.E.; Brock, R.E.; Dorr, B.S.

2002-01-01

Over 6,000 crappies Pomoxis spp. were tagged in five water bodies to estimate exploitation rates by anglers. Exploitation rates were computed as the percentage of tags returned after adjustment for three sources of uncertainty: postrelease mortality due to the tagging process, tag loss, and the reporting rate of tagged fish. Confidence intervals around exploitation rates were estimated by resampling from the probability distributions of tagging mortality, tag loss, and reporting rate. Estimates of exploitation rates ranged from 17% to 54% among the five study systems. Uncertainty around estimates of tagging mortality, tag loss, and reporting resulted in 90% confidence intervals around the median exploitation rate as narrow as 15 percentage points and as broad as 46 percentage points. The greatest source of estimation error was uncertainty about tag reporting. Because the large investments required by tagging and reward operations produce imprecise estimates of the exploitation rate, it may be worth considering other approaches to estimating it or simply circumventing the exploitation question altogether.

Confidence intervals for the population mean tailored to small sample sizes, with applications to survey sampling.

PubMed

Rosenblum, Michael A; Laan, Mark J van der

2009-01-07

The validity of standard confidence intervals constructed in survey sampling is based on the central limit theorem. For small sample sizes, the central limit theorem may give a poor approximation, resulting in confidence intervals that are misleading. We discuss this issue and propose methods for constructing confidence intervals for the population mean tailored to small sample sizes. We present a simple approach for constructing confidence intervals for the population mean based on tail bounds for the sample mean that are correct for all sample sizes. Bernstein's inequality provides one such tail bound. The resulting confidence intervals have guaranteed coverage probability under much weaker assumptions than are required for standard methods. A drawback of this approach, as we show, is that these confidence intervals are often quite wide. In response to this, we present a method for constructing much narrower confidence intervals, which are better suited for practical applications, and that are still more robust than confidence intervals based on standard methods, when dealing with small sample sizes. We show how to extend our approaches to much more general estimation problems than estimating the sample mean. We describe how these methods can be used to obtain more reliable confidence intervals in survey sampling. As a concrete example, we construct confidence intervals using our methods for the number of violent deaths between March 2003 and July 2006 in Iraq, based on data from the study "Mortality after the 2003 invasion of Iraq: A cross sectional cluster sample survey," by Burnham et al. (2006).

Confidence intervals from single observations in forest research

Treesearch

Harry T. Valentine; George M. Furnival; Timothy G. Gregoire

1991-01-01

A procedure for constructing confidence intervals and testing hypothese from a single trial or observation is reviewed. The procedure requires a prior, fixed estimate or guess of the outcome of an experiment or sampling. Two examples of applications are described: a confidence interval is constructed for the expected outcome of a systematic sampling of a forested tract...

Statistical tools for transgene copy number estimation based on real-time PCR.

PubMed

Yuan, Joshua S; Burris, Jason; Stewart, Nathan R; Mentewab, Ayalew; Stewart, C Neal

2007-11-01

As compared with traditional transgene copy number detection technologies such as Southern blot analysis, real-time PCR provides a fast, inexpensive and high-throughput alternative. However, the real-time PCR based transgene copy number estimation tends to be ambiguous and subjective stemming from the lack of proper statistical analysis and data quality control to render a reliable estimation of copy number with a prediction value. Despite the recent progresses in statistical analysis of real-time PCR, few publications have integrated these advancements in real-time PCR based transgene copy number determination. Three experimental designs and four data quality control integrated statistical models are presented. For the first method, external calibration curves are established for the transgene based on serially-diluted templates. The Ct number from a control transgenic event and putative transgenic event are compared to derive the transgene copy number or zygosity estimation. Simple linear regression and two group T-test procedures were combined to model the data from this design. For the second experimental design, standard curves were generated for both an internal reference gene and the transgene, and the copy number of transgene was compared with that of internal reference gene. Multiple regression models and ANOVA models can be employed to analyze the data and perform quality control for this approach. In the third experimental design, transgene copy number is compared with reference gene without a standard curve, but rather, is based directly on fluorescence data. Two different multiple regression models were proposed to analyze the data based on two different approaches of amplification efficiency integration. Our results highlight the importance of proper statistical treatment and quality control integration in real-time PCR-based transgene copy number determination. These statistical methods allow the real-time PCR-based transgene copy number estimation to be more reliable and precise with a proper statistical estimation. Proper confidence intervals are necessary for unambiguous prediction of trangene copy number. The four different statistical methods are compared for their advantages and disadvantages. Moreover, the statistical methods can also be applied for other real-time PCR-based quantification assays including transfection efficiency analysis and pathogen quantification.

Repeated significance tests of linear combinations of sensitivity and specificity of a diagnostic biomarker

PubMed Central

Wu, Mixia; Shu, Yu; Li, Zhaohai; Liu, Aiyi

2016-01-01

A sequential design is proposed to test whether the accuracy of a binary diagnostic biomarker meets the minimal level of acceptance. The accuracy of a binary diagnostic biomarker is a linear combination of the marker’s sensitivity and specificity. The objective of the sequential method is to minimize the maximum expected sample size under the null hypothesis that the marker’s accuracy is below the minimal level of acceptance. The exact results of two-stage designs based on Youden’s index and efficiency indicate that the maximum expected sample sizes are smaller than the sample sizes of the fixed designs. Exact methods are also developed for estimation, confidence interval and p-value concerning the proposed accuracy index upon termination of the sequential testing. PMID:26947768

Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting

PubMed Central

Chan, Kwun Chuen Gary; Yam, Sheung Chi Phillip; Zhang, Zheng

2015-01-01

Summary The estimation of average treatment effects based on observational data is extremely important in practice and has been studied by generations of statisticians under different frameworks. Existing globally efficient estimators require non-parametric estimation of a propensity score function, an outcome regression function or both, but their performance can be poor in practical sample sizes. Without explicitly estimating either functions, we consider a wide class calibration weights constructed to attain an exact three-way balance of the moments of observed covariates among the treated, the control, and the combined group. The wide class includes exponential tilting, empirical likelihood and generalized regression as important special cases, and extends survey calibration estimators to different statistical problems and with important distinctions. Global semiparametric efficiency for the estimation of average treatment effects is established for this general class of calibration estimators. The results show that efficiency can be achieved by solely balancing the covariate distributions without resorting to direct estimation of propensity score or outcome regression function. We also propose a consistent estimator for the efficient asymptotic variance, which does not involve additional functional estimation of either the propensity score or the outcome regression functions. The proposed variance estimator outperforms existing estimators that require a direct approximation of the efficient influence function. PMID:27346982

Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting.

PubMed

Chan, Kwun Chuen Gary; Yam, Sheung Chi Phillip; Zhang, Zheng

2016-06-01

The estimation of average treatment effects based on observational data is extremely important in practice and has been studied by generations of statisticians under different frameworks. Existing globally efficient estimators require non-parametric estimation of a propensity score function, an outcome regression function or both, but their performance can be poor in practical sample sizes. Without explicitly estimating either functions, we consider a wide class calibration weights constructed to attain an exact three-way balance of the moments of observed covariates among the treated, the control, and the combined group. The wide class includes exponential tilting, empirical likelihood and generalized regression as important special cases, and extends survey calibration estimators to different statistical problems and with important distinctions. Global semiparametric efficiency for the estimation of average treatment effects is established for this general class of calibration estimators. The results show that efficiency can be achieved by solely balancing the covariate distributions without resorting to direct estimation of propensity score or outcome regression function. We also propose a consistent estimator for the efficient asymptotic variance, which does not involve additional functional estimation of either the propensity score or the outcome regression functions. The proposed variance estimator outperforms existing estimators that require a direct approximation of the efficient influence function.

Military Applicability of Interval Training for Health and Performance.

PubMed

Gibala, Martin J; Gagnon, Patrick J; Nindl, Bradley C

2015-11-01

Militaries from around the globe have predominantly used endurance training as their primary mode of aerobic physical conditioning, with historical emphasis placed on the long distance run. In contrast to this traditional exercise approach to training, interval training is characterized by brief, intermittent bouts of intense exercise, separated by periods of lower intensity exercise or rest for recovery. Although hardly a novel concept, research over the past decade has shed new light on the potency of interval training to elicit physiological adaptations in a time-efficient manner. This work has largely focused on the benefits of low-volume interval training, which involves a relatively small total amount of exercise, as compared with the traditional high-volume approach to training historically favored by militaries. Studies that have directly compared interval and moderate-intensity continuous training have shown similar improvements in cardiorespiratory fitness and the capacity for aerobic energy metabolism, despite large differences in total exercise and training time commitment. Interval training can also be applied in a calisthenics manner to improve cardiorespiratory fitness and strength, and this approach could easily be incorporated into a military conditioning environment. Although interval training can elicit physiological changes in men and women, the potential for sex-specific adaptations in the adaptive response to interval training warrants further investigation. Additional work is needed to clarify adaptations occurring over the longer term; however, interval training deserves consideration from a military applicability standpoint as a time-efficient training strategy to enhance soldier health and performance. There is value for military leaders in identifying strategies that reduce the time required for exercise, but nonetheless provide an effective training stimulus.

Architects and Design-Phase Cost Estimates: Design Professionals Should Reconsider the Value of Third-Party Estimates

ERIC Educational Resources Information Center

Coakley, John

2010-01-01

Professional cost estimators are widely used by architects during the design phases of a project to provide preliminary cost estimates. These estimates may begin at the conceptual design phase and are prepared at regular intervals through the construction document phase. Estimating professionals are frequently tasked with "selling" the importance…

Refusal bias in HIV prevalence estimates from nationally representative seroprevalence surveys.

PubMed

Reniers, Georges; Eaton, Jeffrey

2009-03-13

To assess the relationship between prior knowledge of one's HIV status and the likelihood to refuse HIV testing in populations-based surveys and explore its potential for producing bias in HIV prevalence estimates. Using longitudinal survey data from Malawi, we estimate the relationship between prior knowledge of HIV-positive status and subsequent refusal of an HIV test. We use that parameter to develop a heuristic model of refusal bias that is applied to six Demographic and Health Surveys, in which refusal by HIV status is not observed. The model only adjusts for refusal bias conditional on a completed interview. Ecologically, HIV prevalence, prior testing rates and refusal for HIV testing are highly correlated. Malawian data further suggest that amongst individuals who know their status, HIV-positive individuals are 4.62 (95% confidence interval, 2.60-8.21) times more likely to refuse testing than HIV-negative ones. On the basis of that parameter and other inputs from the Demographic and Health Surveys, our model predicts downward bias in national HIV prevalence estimates ranging from 1.5% (95% confidence interval, 0.7-2.9) for Senegal to 13.3% (95% confidence interval, 7.2-19.6) for Malawi. In absolute terms, bias in HIV prevalence estimates is negligible for Senegal but 1.6 (95% confidence interval, 0.8-2.3) percentage points for Malawi. Downward bias is more severe in urban populations. Because refusal rates are higher in men, seroprevalence surveys also tend to overestimate the female-to-male ratio of infections. Prior knowledge of HIV status informs decisions to participate in seroprevalence surveys. Informed refusals may produce bias in estimates of HIV prevalence and the sex ratio of infections.

Estimating the burden of disease attributable to indoor air pollution from household use of solid fuels in South Africa in 2000.

PubMed

Norman, Rosana; Barnes, Brendon; Mathee, Angela; Bradshaw, Debbie

2007-08-01

To estimate the burden of respiratory ill health in South African children and adults in 2000 from exposure to indoor air pollution associated with household use of solid fuels. World Health Organization comparative risk assessment (CRA) methodology was followed. The South African Census 2001 was used to derive the proportion of households using solid fuels for cooking and heating by population group. Exposure estimates were adjusted by a ventilation factor taking into account the general level of ventilation in the households. Population-attributable fractions were calculated and applied to revised burden of disease estimates for each population group. Monte Carlo simulation-modelling techniques were used for uncertainty analysis. South Africa. Black African, coloured, white and Indian children under 5 years of age and adults aged 30 years and older. Mortality and disability-adjusted life years (DALYs) from acute lower respiratory infections in children under 5 years, and chronic obstructive pulmonary disease and lung cancer in adults 30 years and older. An estimated 20% of South African households were exposed to indoor smoke from solid fuels, with marked variation by population group. This exposure was estimated to have caused 2,489 deaths (95% uncertainty interval 1,672 - 3,324) or 0.5% (95% uncertainty interval 0.3 - 0.6%) of all deaths in South Africa in 2000. The loss of healthy life years comprised a slightly smaller proportion of the total: 60,934 DALYs (95% uncertainty interval 41,170 - 81,246) or 0.4% of all DALYs (95% uncertainty interval 0.3 - 0.5%) in South Africa in 2000. Almost 99% of this burden occurred in the black African population. The most important interventions to reduce this impact include access to cleaner household fuels, improved stoves, and better ventilation.

Global, Regional, and National Burden of Rheumatic Heart Disease, 1990-2015.

PubMed

Watkins, David A; Johnson, Catherine O; Colquhoun, Samantha M; Karthikeyan, Ganesan; Beaton, Andrea; Bukhman, Gene; Forouzanfar, Mohammed H; Longenecker, Christopher T; Mayosi, Bongani M; Mensah, George A; Nascimento, Bruno R; Ribeiro, Antonio L P; Sable, Craig A; Steer, Andrew C; Naghavi, Mohsen; Mokdad, Ali H; Murray, Christopher J L; Vos, Theo; Carapetis, Jonathan R; Roth, Gregory A

2017-08-24

Rheumatic heart disease remains an important preventable cause of cardiovascular death and disability, particularly in low-income and middle-income countries. We estimated global, regional, and national trends in the prevalence of and mortality due to rheumatic heart disease as part of the 2015 Global Burden of Disease study. We systematically reviewed data on fatal and nonfatal rheumatic heart disease for the period from 1990 through 2015. Two Global Burden of Disease analytic tools, the Cause of Death Ensemble model and DisMod-MR 2.1, were used to produce estimates of mortality and prevalence, including estimates of uncertainty. We estimated that there were 319,400 (95% uncertainty interval, 297,300 to 337,300) deaths due to rheumatic heart disease in 2015. Global age-standardized mortality due to rheumatic heart disease decreased by 47.8% (95% uncertainty interval, 44.7 to 50.9) from 1990 to 2015, but large differences were observed across regions. In 2015, the highest age-standardized mortality due to and prevalence of rheumatic heart disease were observed in Oceania, South Asia, and central sub-Saharan Africa. We estimated that in 2015 there were 33.4 million (95% uncertainty interval, 29.7 million to 43.1 million) cases of rheumatic heart disease and 10.5 million (95% uncertainty interval, 9.6 million to 11.5 million) disability-adjusted life-years due to rheumatic heart disease globally. We estimated the global disease prevalence of and mortality due to rheumatic heart disease over a 25-year period. The health-related burden of rheumatic heart disease has declined worldwide, but high rates of disease persist in some of the poorest regions in the world. (Funded by the Bill and Melinda Gates Foundation and the Medtronic Foundation.).

Performance analysis of complex repairable industrial systems using PSO and fuzzy confidence interval based methodology.

PubMed

Garg, Harish

2013-03-01

The main objective of the present paper is to propose a methodology for analyzing the behavior of the complex repairable industrial systems. In real-life situations, it is difficult to find the most optimal design policies for MTBF (mean time between failures), MTTR (mean time to repair) and related costs by utilizing available resources and uncertain data. For this, the availability-cost optimization model has been constructed for determining the optimal design parameters for improving the system design efficiency. The uncertainties in the data related to each component of the system are estimated with the help of fuzzy and statistical methodology in the form of the triangular fuzzy numbers. Using these data, the various reliability parameters, which affects the system performance, are obtained in the form of the fuzzy membership function by the proposed confidence interval based fuzzy Lambda-Tau (CIBFLT) methodology. The computed results by CIBFLT are compared with the existing fuzzy Lambda-Tau methodology. Sensitivity analysis on the system MTBF has also been addressed. The methodology has been illustrated through a case study of washing unit, the main part of the paper industry. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Simultaneous determination of radionuclides separable into natural decay series by use of time-interval analysis.

PubMed

Hashimoto, Tetsuo; Sanada, Yukihisa; Uezu, Yasuhiro

2004-05-01

A delayed coincidence method, time-interval analysis (TIA), has been applied to successive alpha- alpha decay events on the millisecond time-scale. Such decay events are part of the (220)Rn-->(216)Po ( T(1/2) 145 ms) (Th-series) and (219)Rn-->(215)Po ( T(1/2) 1.78 ms) (Ac-series). By using TIA in addition to measurement of (226)Ra (U-series) from alpha-spectrometry by liquid scintillation counting (LSC), two natural decay series could be identified and separated. The TIA detection efficiency was improved by using the pulse-shape discrimination technique (PSD) to reject beta-pulses, by solvent extraction of Ra combined with simple chemical separation, and by purging the scintillation solution with dry N(2) gas. The U- and Th-series together with the Ac-series were determined, respectively, from alpha spectra and TIA carried out immediately after Ra-extraction. Using the (221)Fr-->(217)At ( T(1/2) 32.3 ms) decay process as a tracer, overall yields were estimated from application of TIA to the (225)Ra (Np-decay series) at the time of maximum growth. The present method has proven useful for simultaneous determination of three radioactive decay series in environmental samples.

Estimating numbers of greater prairie-chickens using mark-resight techniques

USGS Publications Warehouse

Clifton, A.M.; Krementz, D.G.

2006-01-01

Current monitoring efforts for greater prairie-chicken (Tympanuchus cupido pinnatus) populations indicate that populations are declining across their range. Monitoring the population status of greater prairie-chickens is based on traditional lek surveys (TLS) that provide an index without considering detectability. Estimators, such as immigration-emigration joint maximum-likelihood estimator from a hypergeometric distribution (IEJHE), can account for detectability and provide reliable population estimates based on resightings. We evaluated the use of mark-resight methods using radiotelemetry to estimate population size and density of greater prairie-chickens on 2 sites at a tallgrass prairie in the Flint Hills of Kansas, USA. We used average distances traveled from lek of capture to estimate density. Population estimates and confidence intervals at the 2 sites were 54 (CI 50-59) on 52.9 km 2 and 87 (CI 82-94) on 73.6 km2. The TLS performed at the same sites resulted in population ranges of 7-34 and 36-63 and always produced a lower population index than the mark-resight population estimate with a larger range. Mark-resight simulations with varying male:female ratios of marks indicated that this ratio was important in designing a population study on prairie-chickens. Confidence intervals for estimates when no marks were placed on females at the 2 sites (CI 46-50, 76-84) did not overlap confidence intervals when 40% of marks were placed on females (CI 54-64, 91-109). Population estimates derived using this mark-resight technique were apparently more accurate than traditional methods and would be more effective in detecting changes in prairie-chicken populations. Our technique could improve prairie-chicken management by providing wildlife biologists and land managers with a tool to estimate the population size and trends of lekking bird species, such as greater prairie-chickens.

Simulation data for an estimation of the maximum theoretical value and confidence interval for the correlation coefficient.

PubMed

Rocco, Paolo; Cilurzo, Francesco; Minghetti, Paola; Vistoli, Giulio; Pedretti, Alessandro

2017-10-01

The data presented in this article are related to the article titled "Molecular Dynamics as a tool for in silico screening of skin permeability" (Rocco et al., 2017) [1]. Knowledge of the confidence interval and maximum theoretical value of the correlation coefficient r can prove useful to estimate the reliability of developed predictive models, in particular when there is great variability in compiled experimental datasets. In this Data in Brief article, data from purposely designed numerical simulations are presented to show how much the maximum r value is worsened by increasing the data uncertainty. The corresponding confidence interval of r is determined by using the Fisher r → Z transform.

Arsenic metabolism efficiency has a causal role in arsenic toxicity: Mendelian randomization and gene-environment interaction.

PubMed

Pierce, Brandon L; Tong, Lin; Argos, Maria; Gao, Jianjun; Farzana, Jasmine; Roy, Shantanu; Paul-Brutus, Rachelle; Rahaman, Ronald; Rakibuz-Zaman, Muhammad; Parvez, Faruque; Ahmed, Alauddin; Quasem, Iftekhar; Hore, Samar K; Alam, Shafiul; Islam, Tariqul; Harjes, Judith; Sarwar, Golam; Slavkovich, Vesna; Gamble, Mary V; Chen, Yu; Yunus, Mohammad; Rahman, Mahfuzar; Baron, John A; Graziano, Joseph H; Ahsan, Habibul

2013-12-01

Arsenic exposure through drinking water is a serious global health issue. Observational studies suggest that individuals who metabolize arsenic efficiently are at lower risk for toxicities such as arsenical skin lesions. Using two single nucleotide polymorphisms(SNPs) in the 10q24.32 region (near AS3MT) that show independent associations with metabolism efficiency, Mendelian randomization can be used to assess whether the association between metabolism efficiency and skin lesions is likely to be causal. Using data on 2060 arsenic-exposed Bangladeshi individuals, we estimated associations for two 10q24.32 SNPs with relative concentrations of three urinary arsenic species (representing metabolism efficiency): inorganic arsenic (iAs), monomethylarsonic acid(MMA) and dimethylarsinic acid (DMA). SNP-based predictions of iAs%, MMA% and DMA% were tested for association with skin lesion status among 2483 cases and 2857 controls. Causal odds ratios for skin lesions were 0.90 (95% confidence interval[CI]: 0.87, 0.95), 1.19 (CI: 1.10, 1.28) and 1.23 (CI: 1.12, 1.36)for a one standard deviation increase in DMA%, MMA% and iAs%,respectively. We demonstrated genotype-arsenic interaction, with metabolism-related variants showing stronger associations with skin lesion risk among individuals with high arsenic exposure (synergy index: 1.37; CI: 1.11, 1.62). We provide strong evidence for a causal relationship between arsenic metabolism efficiency and skin lesion risk. Mendelian randomization can be used to assess the causal role of arsenic exposure and metabolism in a wide array of health conditions.exposure and metabolism in a wide array of health conditions.Developing interventions that increase arsenic metabolism efficiency are likely to reduce the impact of arsenic exposure on health.

Genetic merit for fertility traits in Holstein cows: I. Production characteristics and reproductive efficiency in a pasture-based system.

PubMed

Cummins, S B; Lonergan, P; Evans, A C O; Berry, D P; Evans, R D; Butler, S T

2012-03-01

The objective of the present study was to characterize the phenotypic performance of cows with similar proportions of Holstein genetics, similar genetic merit for milk production traits, but with good (Fert+) or poor (Fert-) genetic merit for fertility traits. Specifically, we tested the hypothesis that cows with a negative estimated breeding value for calving interval would have superior fertility performance and would have detectable differences in body reserve mobilization and circulating concentrations of metabolic hormones and metabolites compared with cows that had a positive estimated breeding value for calving interval. For the duration of the study, cows were managed identically as a single herd in a typical grass-based, spring-calving production system. A total of 80 lactation records were available from 26 Fert+ and 26 Fert- cows over 2 consecutive years (2008 and 2009). During yr 1, cows were monitored during a 20-wk breeding season to evaluate reproductive performance. Milk production, body condition score (scale 1 to 5), body weight, grass dry matter intake, energy balance, and metabolic hormone and metabolite data were collected during both years. The Fert+ cows had greater daily milk yield (19.5 vs. 18.7 kg/d), shorter interval from calving to conception (85.6 vs. 113.8 d), and fewer services per cow (1.78 vs. 2.83). No difference between groups in grass dry matter intake, energy balance, or body weight was observed. The Fert+ cows maintained greater BCS during mid (2.84 vs. 2.74 units) and late lactation (2.82 vs. 2.73 units). Circulating concentrations of insulin-like growth factor-I were greater throughout the gestation-lactation cycle in Fert+ cows (148.3 vs. 128.2 ng/mL). The Fert+ cows also had greater circulating concentrations of insulin during the first 4 wk of lactation (1.71 vs. 1.24 μIU/mL). Analysis of records from national herd data verified the association between genetic merit for fertility traits and phenotypic reproductive performance; Fert+ cows (n = 2,436) required 11.1 d less to recalve than did Fert- cows (n = 1,388), and the percentage of cows that successfully calved for the second time within 365 and 400 d of the first calving was 8 and 13% greater for Fert+ compared with Fert- cows, respectively. These results demonstrate that genetic merit for fertility traits had a pronounced effect on reproductive efficiency, BCS profiles, and circulating concentrations of insulin-like growth factor-I. Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Average static stress drops for heterogeneous slip distributions: Comparison of several measures and implications for energy partition in earthquakes

NASA Astrophysics Data System (ADS)

Noda, H.; Lapusta, N.; Kanamori, H.

2010-12-01

Static stress drop is often estimated using the seismic moment and rupture area based on a model for uniform stress drop distribution; we denote this estimate by Δσ_M. Δσ_M is sometimes interpreted as the spatial average of stress change over the ruptured area, denoted here as Δσ_A, and used accordingly, for example, to discuss the relation between recurrence interval and the healing of the frictional surface in a system with one degree of freedom [e.g., Marone, 1998]. Δσ_M is also used to estimate available energy (defined as the strain energy change computed using the final stress state as the reference one) and radiation efficiency [e.g., Venkataraman and Kanamori, 2004]. In this work, we define a stress drop measure, Δσ_E, that would enter the exact computation of available energy and radiation efficiency. The three stress drop measures - Δσ_M that can be estimated from observations, Δσ_A, and Δσ_E - are equal if the static stress change is spatially uniform, and that motivates substituting Δσ_M for the other two quantities in applications. However, finite source inversions suggest that the stress change is heterogeneous in natural earthquakes [e.g., Bouchon, 1997]. Since Δσ_M is the average of stress change weighted by slip distribution due to a uniform stress drop [Madariaga, 1979], Δσ_E is the average of stress change weighted by actual slip distribution in the event (this work), and Δσ_A is the simple spatial average of stress change, the three measures should, in general, be different. Here, we investigate the effect of heterogeneity aiming to understand how to use the seismological estimates of stress drop appropriately. We create heterogeneous slip distributions for both circular and rectangular planar ruptures using the approach motivated by Liu-Zeng et al. [2005] and Lavalleé et al [2005]. We find that, indeed, the three stress drop measures differ in our scenarios. In particular, heterogeneity increases Δσ_E and thus the available energy when the seismic moment (and hence Δσ_M) is preserved. So using Δσ_M instead of Δσ_E would underestimate available energy and hence overestimate radiation efficiency. For a range of parameters, Δσ_E is well-approximated by the seismic estimate Δσ_M if the latter is computed using a modified (decreased) rupture area that excludes low-slipped regions; a qualitatively similar procedure is already being used in practice [Somerville et al, 1999].

Effects of temperature on early-phase transmission of Yersina pestis by the flea, Xenopsylla cheopis.

PubMed

Schotthoefer, Anna M; Bearden, Scott W; Vetter, Sara M; Holmes, Jennifer; Montenieri, John A; Graham, Christine B; Woods, Michael E; Eisen, Rebecca J; Gage, Kenneth L

2011-03-01

Sharp declines in human and animal cases of plague, caused by the bacterium Yersinia pestis (Yersin), have been observed when outbreaks coincide with hot weather. Failure of biofilm production, or blockage, to occur in the flea, as temperatures reach 30 degrees C has been suggested as an explanation for these declines. Recent work demonstrating efficient flea transmission during the first few days after fleas have taken an infectious blood meal, in the absence of blockage (e.g., early-phase transmission), however, has called this hypothesis into question. To explore the potential effects of temperature on early-phase transmission, we infected colony-reared Xenopsylla cheopis (Rothchild) fleas with a wild-type strain of plague bacteria using an artificial feeding system, and held groups of fleas at 10, 23, 27, and 30 degrees C. Naive Swiss Webster mice were exposed to fleas from each of these temperatures on days 1-4 postinfection, and monitored for signs of infection for 21 d. Temperature did not significantly influence the rates of transmission observed for fleas held at 23, 27, and 30 degrees C. Estimated per flea transmission efficiencies for these higher temperatures ranged from 2.32 to 4.96% (95% confidence interval [CI]: 0.96-8.74). In contrast, no transmission was observed in mice challenged by fleas held at 10 degrees C (per flea transmission efficiency estimates, 0-1.68%). These results suggest that declines in human and animal cases during hot weather are not related to changes in the abilities of X. cheopis fleas to transmit Y. pestis infections during the early-phase period. By contrast, transmission may be delayed or inhibited at low temperatures, indicating that epizootic spread of Y. pestis by X. cheopis via early-phase transmission is unlikely during colder periods of the year.

An optimization based sampling approach for multiple metrics uncertainty analysis using generalized likelihood uncertainty estimation

NASA Astrophysics Data System (ADS)

Zhou, Rurui; Li, Yu; Lu, Di; Liu, Haixing; Zhou, Huicheng

2016-09-01

This paper investigates the use of an epsilon-dominance non-dominated sorted genetic algorithm II (ɛ-NSGAII) as a sampling approach with an aim to improving sampling efficiency for multiple metrics uncertainty analysis using Generalized Likelihood Uncertainty Estimation (GLUE). The effectiveness of ɛ-NSGAII based sampling is demonstrated compared with Latin hypercube sampling (LHS) through analyzing sampling efficiency, multiple metrics performance, parameter uncertainty and flood forecasting uncertainty with a case study of flood forecasting uncertainty evaluation based on Xinanjiang model (XAJ) for Qing River reservoir, China. Results obtained demonstrate the following advantages of the ɛ-NSGAII based sampling approach in comparison to LHS: (1) The former performs more effective and efficient than LHS, for example the simulation time required to generate 1000 behavioral parameter sets is shorter by 9 times; (2) The Pareto tradeoffs between metrics are demonstrated clearly with the solutions from ɛ-NSGAII based sampling, also their Pareto optimal values are better than those of LHS, which means better forecasting accuracy of ɛ-NSGAII parameter sets; (3) The parameter posterior distributions from ɛ-NSGAII based sampling are concentrated in the appropriate ranges rather than uniform, which accords with their physical significance, also parameter uncertainties are reduced significantly; (4) The forecasted floods are close to the observations as evaluated by three measures: the normalized total flow outside the uncertainty intervals (FOUI), average relative band-width (RB) and average deviation amplitude (D). The flood forecasting uncertainty is also reduced a lot with ɛ-NSGAII based sampling. This study provides a new sampling approach to improve multiple metrics uncertainty analysis under the framework of GLUE, and could be used to reveal the underlying mechanisms of parameter sets under multiple conflicting metrics in the uncertainty analysis process.

Evaluation of a mark-recapture method for estimating mortality and migration rates of stratified populations

USGS Publications Warehouse

Dorazio, R.M.; Rago, P.J.

1991-01-01

We simulated mark–recapture experiments to evaluate a method for estimating fishing mortality and migration rates of populations stratified at release and recovery. When fish released in two or more strata were recovered from different recapture strata in nearly the same proportions, conditional recapture probabilities were estimated outside the [0, 1] interval. The maximum likelihood estimates tended to be biased and imprecise when the patterns of recaptures produced extremely "flat" likelihood surfaces. Absence of bias was not guaranteed, however, in experiments where recapture rates could be estimated within the [0, 1] interval. Inadequate numbers of tag releases and recoveries also produced biased estimates, although the bias was easily detected by the high sampling variability of the estimates. A stratified tag–recapture experiment with sockeye salmon (Oncorhynchus nerka) was used to demonstrate procedures for analyzing data that produce biased estimates of recapture probabilities. An estimator was derived to examine the sensitivity of recapture rate estimates to assumed differences in natural and tagging mortality, tag loss, and incomplete reporting of tag recoveries.

Continuous-time interval model identification of blood glucose dynamics for type 1 diabetes

NASA Astrophysics Data System (ADS)

Kirchsteiger, Harald; Johansson, Rolf; Renard, Eric; del Re, Luigi

2014-07-01

While good physiological models of the glucose metabolism in type 1 diabetic patients are well known, their parameterisation is difficult. The high intra-patient variability observed is a further major obstacle. This holds for data-based models too, so that no good patient-specific models are available. Against this background, this paper proposes the use of interval models to cover the different metabolic conditions. The control-oriented models contain a carbohydrate and insulin sensitivity factor to be used for insulin bolus calculators directly. Available clinical measurements were sampled on an irregular schedule which prompts the use of continuous-time identification, also for the direct estimation of the clinically interpretable factors mentioned above. An identification method is derived and applied to real data from 28 diabetic patients. Model estimation was done on a clinical data-set, whereas validation results shown were done on an out-of-clinic, everyday life data-set. The results show that the interval model approach allows a much more regular estimation of the parameters and avoids physiologically incompatible parameter estimates.

A method for analyzing clustered interval-censored data based on Cox's model.

PubMed

Kor, Chew-Teng; Cheng, Kuang-Fu; Chen, Yi-Hau

2013-02-28

Methods for analyzing interval-censored data are well established. Unfortunately, these methods are inappropriate for the studies with correlated data. In this paper, we focus on developing a method for analyzing clustered interval-censored data. Our method is based on Cox's proportional hazard model with piecewise-constant baseline hazard function. The correlation structure of the data can be modeled by using Clayton's copula or independence model with proper adjustment in the covariance estimation. We establish estimating equations for the regression parameters and baseline hazards (and a parameter in copula) simultaneously. Simulation results confirm that the point estimators follow a multivariate normal distribution, and our proposed variance estimations are reliable. In particular, we found that the approach with independence model worked well even when the true correlation model was derived from Clayton's copula. We applied our method to a family-based cohort study of pandemic H1N1 influenza in Taiwan during 2009-2010. Using the proposed method, we investigate the impact of vaccination and family contacts on the incidence of pH1N1 influenza. Copyright © 2012 John Wiley & Sons, Ltd.

Sensitivity of Crop Gross Primary Production Simulations to In-situ and Reanalysis Meteorological Data

NASA Astrophysics Data System (ADS)

Jin, C.; Xiao, X.; Wagle, P.

2014-12-01

Accurate estimation of crop Gross Primary Production (GPP) is important for food securityand terrestrial carbon cycle. Numerous publications have reported the potential of the satellite-based Production Efficiency Models (PEMs) to estimate GPP driven by in-situ climate data. Simulations of the PEMs often require surface reanalysis climate data as inputs, for example, the North America Regional Reanalysis datasets (NARR). These reanalysis datasets showed certain biases from the in-situ climate datasets. Thus, sensitivity analysis of the PEMs to the climate inputs is needed before their application at the regional scale. This study used the satellite-based Vegetation Photosynthesis Model (VPM), which is driven by solar radiation (R), air temperature (T), and the satellite-based vegetation indices, to quantify the causes and degree of uncertainties in crop GPP estimates due to different meteorological inputs at the 8-day interval (in-situ AmeriFlux data and NARR surface reanalysis data). The NARR radiation (RNARR) explained over 95% of the variability in in-situ RAF and TAF measured from AmeriFlux. The bais of TNARR was relatively small. However, RNARR had a systematical positive bias of ~3.5 MJ m-2day-1 from RAF. A simple adjustment based on the spatial statistic between RNARR and RAF produced relatively accurate radiation data for all crop site-years by reducing RMSE from 4 to 1.7 MJ m-2day-1. The VPM-based GPP estimates with three climate datasets (i.e., in-situ, and NARR before and after adjustment, GPPVPM,AF, GPPVPM,NARR, and GPPVPM,adjNARR) showed good agreements with the seasonal dynamics of crop GPP derived from the flux towers (GPPAF). The GPPVPM,AF differed from GPPAF by 2% for maize, and -8% to -12% for soybean on the 8-day interval. The positive bias of RNARR resulted in an overestimation of GPPVPM,NARR at both maize and soybean systems. However, GPPVPM,adjNARR significantly reduced the uncertainties of the maize GPP from 25% to 2%. The results from this study revealed that the errors of the NARR surface reanalysis data introduced significant uncertainties of the PEMs-based GPP estimates. Therefore, it is important to develop more accurate radiation datasets at the regional and global scales to estimate gross and net primary production of terrestrial ecosystems at the regional and global scales.

Using filtered forecasting techniques to determine personalized monitoring schedules for patients with open-angle glaucoma.

PubMed

Schell, Greggory J; Lavieri, Mariel S; Helm, Jonathan E; Liu, Xiang; Musch, David C; Van Oyen, Mark P; Stein, Joshua D

2014-08-01

To determine whether dynamic and personalized schedules of visual field (VF) testing and intraocular pressure (IOP) measurements result in an improvement in disease progression detection compared with fixed interval schedules for performing these tests when evaluating patients with open-angle glaucoma (OAG). Secondary analyses using longitudinal data from 2 randomized controlled trials. A total of 571 participants from the Advanced Glaucoma Intervention Study (AGIS) and the Collaborative Initial Glaucoma Treatment Study (CIGTS). Perimetric and tonometric data were obtained for AGIS and CIGTS trial participants and used to parameterize and validate a Kalman filter model. The Kalman filter updates knowledge about each participant's disease dynamics as additional VF tests and IOP measurements are obtained. After incorporating the most recent VF and IOP measurements, the model forecasts each participant's disease dynamics into the future and characterizes the forecasting error. To determine personalized schedules for future VF tests and IOP measurements, we developed an algorithm by combining the Kalman filter for state estimation with the predictive power of logistic regression to identify OAG progression. The algorithm was compared with 1-, 1.5-, and 2-year fixed interval schedules of obtaining VF and IOP measurements. Length of diagnostic delay in detecting OAG progression, efficiency of detecting progression, and number of VF and IOP measurements needed to assess for progression. Participants were followed in the AGIS and CIGTS trials for a mean (standard deviation) of 6.5 (2.8) years. Our forecasting model achieved a 29% increased efficiency in identifying OAG progression (P<0.0001) and detected OAG progression 57% sooner (reduced diagnostic delay) (P = 0.02) than following a fixed yearly monitoring schedule, without increasing the number of VF tests and IOP measurements required. The model performed well for patients with mild and advanced disease. The model performed significantly more testing of patients who exhibited OAG progression than nonprogressing patients (1.3 vs. 1.0 tests per year; P<0.0001). Use of dynamic and personalized testing schedules can enhance the efficiency of OAG progression detection and reduce diagnostic delay compared with yearly fixed monitoring intervals. If further validation studies confirm these findings, such algorithms may be able to greatly enhance OAG management. Copyright © 2014 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

A one-model approach based on relaxed combinations of inputs for evaluating input congestion in DEA

NASA Astrophysics Data System (ADS)

Khodabakhshi, Mohammad

2009-08-01

This paper provides a one-model approach of input congestion based on input relaxation model developed in data envelopment analysis (e.g. [G.R. Jahanshahloo, M. Khodabakhshi, Suitable combination of inputs for improving outputs in DEA with determining input congestion -- Considering textile industry of China, Applied Mathematics and Computation (1) (2004) 263-273; G.R. Jahanshahloo, M. Khodabakhshi, Determining assurance interval for non-Archimedean ele improving outputs model in DEA, Applied Mathematics and Computation 151 (2) (2004) 501-506; M. Khodabakhshi, A super-efficiency model based on improved outputs in data envelopment analysis, Applied Mathematics and Computation 184 (2) (2007) 695-703; M. Khodabakhshi, M. Asgharian, An input relaxation measure of efficiency in stochastic data analysis, Applied Mathematical Modelling 33 (2009) 2010-2023]. This approach reduces solving three problems with the two-model approach introduced in the first of the above-mentioned reference to two problems which is certainly important from computational point of view. The model is applied to a set of data extracted from ISI database to estimate input congestion of 12 Canadian business schools.

New Approaches to Robust Confidence Intervals for Location: A Simulation Study.

DTIC Science & Technology

1984-06-01

obtain a denominator for the test statistic. Those statistics based on location estimates derived from Hampel’s redescending influence function or v...defined an influence function for a test in terms of the behavior of its P-values when the data are sampled from a model distribution modified by point...proposal could be used for interval estimation as well as hypothesis testing, the extension is immediate. Once an influence function has been defined

Demography and population status of polar bears in western Hudson Bay

USGS Publications Warehouse

Lunn, Nicholas J.; Regher, Eric V; Servanty, Sabrina; Converse, Sarah J.; Richardson, Evan S.; Stirling, Ian

2013-01-01

The 2011 abundance estimate from this analysis was 806 bears with a 95% Bayesian credible interval of 653-984. This is lower than, but broadly consistent with, the abundance estimate of 1,030 (95% confidence interval = 745-1406) from a 2011 aerial survey (Stapleton et al. 2014). The capture-recapture and aerial survey approaches have different spatial and temporal coverage of the WH subpopulation and, consequently, the effective study population considered by each approach is different.

Regression Equations for Estimating Flood Flows at Selected Recurrence Intervals for Ungaged Streams in Pennsylvania

USGS Publications Warehouse

Roland, Mark A.; Stuckey, Marla H.

2008-01-01

Regression equations were developed for estimating flood flows at selected recurrence intervals for ungaged streams in Pennsylvania with drainage areas less than 2,000 square miles. These equations were developed utilizing peak-flow data from 322 streamflow-gaging stations within Pennsylvania and surrounding states. All stations used in the development of the equations had 10 or more years of record and included active and discontinued continuous-record as well as crest-stage partial-record stations. The state was divided into four regions, and regional regression equations were developed to estimate the 2-, 5-, 10-, 50-, 100-, and 500-year recurrence-interval flood flows. The equations were developed by means of a regression analysis that utilized basin characteristics and flow data associated with the stations. Significant explanatory variables at the 95-percent confidence level for one or more regression equations included the following basin characteristics: drainage area; mean basin elevation; and the percentages of carbonate bedrock, urban area, and storage within a basin. The regression equations can be used to predict the magnitude of flood flows for specified recurrence intervals for most streams in the state; however, they are not valid for streams with drainage areas generally greater than 2,000 square miles or with substantial regulation, diversion, or mining activity within the basin. Estimates of flood-flow magnitude and frequency for streamflow-gaging stations substantially affected by upstream regulation are also presented.

Analysis of the phase transition in the two-dimensional Ising ferromagnet using a Lempel-Ziv string-parsing scheme and black-box data-compression utilities

NASA Astrophysics Data System (ADS)

Melchert, O.; Hartmann, A. K.

2015-02-01

In this work we consider information-theoretic observables to analyze short symbolic sequences, comprising time series that represent the orientation of a single spin in a two-dimensional (2D) Ising ferromagnet on a square lattice of size L2=1282 for different system temperatures T . The latter were chosen from an interval enclosing the critical point Tc of the model. At small temperatures the sequences are thus very regular; at high temperatures they are maximally random. In the vicinity of the critical point, nontrivial, long-range correlations appear. Here we implement estimators for the entropy rate, excess entropy (i.e., "complexity"), and multi-information. First, we implement a Lempel-Ziv string-parsing scheme, providing seemingly elaborate entropy rate and multi-information estimates and an approximate estimator for the excess entropy. Furthermore, we apply easy-to-use black-box data-compression utilities, providing approximate estimators only. For comparison and to yield results for benchmarking purposes, we implement the information-theoretic observables also based on the well-established M -block Shannon entropy, which is more tedious to apply compared to the first two "algorithmic" entropy estimation procedures. To test how well one can exploit the potential of such data-compression techniques, we aim at detecting the critical point of the 2D Ising ferromagnet. Among the above observables, the multi-information, which is known to exhibit an isolated peak at the critical point, is very easy to replicate by means of both efficient algorithmic entropy estimation procedures. Finally, we assess how good the various algorithmic entropy estimates compare to the more conventional block entropy estimates and illustrate a simple modification that yields enhanced results.

Modeling trends from North American Breeding Bird Survey data: a spatially explicit approach

USGS Publications Warehouse

Bled, Florent; Sauer, John R.; Pardieck, Keith L.; Doherty, Paul; Royle, J. Andy

2013-01-01

Population trends, defined as interval-specific proportional changes in population size, are often used to help identify species of conservation interest. Efficient modeling of such trends depends on the consideration of the correlation of population changes with key spatial and environmental covariates. This can provide insights into causal mechanisms and allow spatially explicit summaries at scales that are of interest to management agencies. We expand the hierarchical modeling framework used in the North American Breeding Bird Survey (BBS) by developing a spatially explicit model of temporal trend using a conditional autoregressive (CAR) model. By adopting a formal spatial model for abundance, we produce spatially explicit abundance and trend estimates. Analyses based on large-scale geographic strata such as Bird Conservation Regions (BCR) can suffer from basic imbalances in spatial sampling. Our approach addresses this issue by providing an explicit weighting based on the fundamental sample allocation unit of the BBS. We applied the spatial model to three species from the BBS. Species have been chosen based upon their well-known population change patterns, which allows us to evaluate the quality of our model and the biological meaning of our estimates. We also compare our results with the ones obtained for BCRs using a nonspatial hierarchical model (Sauer and Link 2011). Globally, estimates for mean trends are consistent between the two approaches but spatial estimates provide much more precise trend estimates in regions on the edges of species ranges that were poorly estimated in non-spatial analyses. Incorporating a spatial component in the analysis not only allows us to obtain relevant and biologically meaningful estimates for population trends, but also enables us to provide a flexible framework in order to obtain trend estimates for any area.

Funnel plot control limits to identify poorly performing healthcare providers when there is uncertainty in the value of the benchmark.

PubMed

Manktelow, Bradley N; Seaton, Sarah E; Evans, T Alun

2016-12-01

There is an increasing use of statistical methods, such as funnel plots, to identify poorly performing healthcare providers. Funnel plots comprise the construction of control limits around a benchmark and providers with outcomes falling outside the limits are investigated as potential outliers. The benchmark is usually estimated from observed data but uncertainty in this estimate is usually ignored when constructing control limits. In this paper, the use of funnel plots in the presence of uncertainty in the value of the benchmark is reviewed for outcomes from a Binomial distribution. Two methods to derive the control limits are shown: (i) prediction intervals; (ii) tolerance intervals Tolerance intervals formally include the uncertainty in the value of the benchmark while prediction intervals do not. The probability properties of 95% control limits derived using each method were investigated through hypothesised scenarios. Neither prediction intervals nor tolerance intervals produce funnel plot control limits that satisfy the nominal probability characteristics when there is uncertainty in the value of the benchmark. This is not necessarily to say that funnel plots have no role to play in healthcare, but that without the development of intervals satisfying the nominal probability characteristics they must be interpreted with care. © The Author(s) 2014.

Mono Lake excursion recorded in sediment of the Santa Clara Valley, California

USGS Publications Warehouse

Mankinen, Edward A.; Wentworth, Carl M.

2004-01-01

Two intervals recording anomalous paleomagnetic inclinations were encountered in the top 40 meters of research drill hole CCOC in the Santa Clara Valley, California. The younger of these two intervals has an age of 28,090 ± 330 radiocarbon years B.P. (calibrated age ∼32.8 ka). This age is in excellent agreement with the latest estimate for the Mono Lake excursion at the type locality and confirms that the excursion has been recorded by sediment in the San Francisco Bay region. The age of an anomalous inclination change below the Mono Lake excursion was not directly determined, but estimates of sedimentation rates indicate that the geomagnetic behavior it represents most likely occurred during the Mono Lake/Laschamp time interval (∼45–28 ka). If true, it may represent one of several recurring fluctuations of magnetic inclination during an interval of a weak geomagnetic dipole, behavior noted in other studies in the region.

Compression based entropy estimation of heart rate variability on multiple time scales.

PubMed

Baumert, Mathias; Voss, Andreas; Javorka, Michal

2013-01-01

Heart rate fluctuates beat by beat in a complex manner. The aim of this study was to develop a framework for entropy assessment of heart rate fluctuations on multiple time scales. We employed the Lempel-Ziv algorithm for lossless data compression to investigate the compressibility of RR interval time series on different time scales, using a coarse-graining procedure. We estimated the entropy of RR interval time series of 20 young and 20 old subjects and also investigated the compressibility of randomly shuffled surrogate RR time series. The original RR time series displayed significantly smaller compression entropy values than randomized RR interval data. The RR interval time series of older subjects showed significantly different entropy characteristics over multiple time scales than those of younger subjects. In conclusion, data compression may be useful approach for multiscale entropy assessment of heart rate variability.

Assessing Hospital Performance After Percutaneous Coronary Intervention Using Big Data.

PubMed

Spertus, Jacob V; T Normand, Sharon-Lise; Wolf, Robert; Cioffi, Matt; Lovett, Ann; Rose, Sherri

2016-11-01

Although risk adjustment remains a cornerstone for comparing outcomes across hospitals, optimal strategies continue to evolve in the presence of many confounders. We compared conventional regression-based model to approaches particularly suited to leveraging big data. We assessed hospital all-cause 30-day excess mortality risk among 8952 adults undergoing percutaneous coronary intervention between October 1, 2011, and September 30, 2012, in 24 Massachusetts hospitals using clinical registry data linked with billing data. We compared conventional logistic regression models with augmented inverse probability weighted estimators and targeted maximum likelihood estimators to generate more efficient and unbiased estimates of hospital effects. We also compared a clinically informed and a machine-learning approach to confounder selection, using elastic net penalized regression in the latter case. Hospital excess risk estimates range from -1.4% to 2.0% across methods and confounder sets. Some hospitals were consistently classified as low or as high excess mortality outliers; others changed classification depending on the method and confounder set used. Switching from the clinically selected list of 11 confounders to a full set of 225 confounders increased the estimation uncertainty by an average of 62% across methods as measured by confidence interval length. Agreement among methods ranged from fair, with a κ statistic of 0.39 (SE: 0.16), to perfect, with a κ of 1 (SE: 0.0). Modern causal inference techniques should be more frequently adopted to leverage big data while minimizing bias in hospital performance assessments. © 2016 American Heart Association, Inc.

Efficient Estimation of the Standardized Value

ERIC Educational Resources Information Center

Longford, Nicholas T.

2009-01-01

We derive an estimator of the standardized value which, under the standard assumptions of normality and homoscedasticity, is more efficient than the established (asymptotically efficient) estimator and discuss its gains for small samples. (Contains 1 table and 3 figures.)

High-Efficiency Postdilution Online Hemodiafiltration Reduces All-Cause Mortality in Hemodialysis Patients

PubMed Central

Moreso, Francesc; Pons, Mercedes; Ramos, Rosa; Mora-Macià, Josep; Carreras, Jordi; Soler, Jordi; Torres, Ferran; Campistol, Josep M.; Martinez-Castelao, Alberto

2013-01-01

Retrospective studies suggest that online hemodiafiltration (OL-HDF) may reduce the risk of mortality compared with standard hemodialysis in patients with ESRD. We conducted a multicenter, open-label, randomized controlled trial in which we assigned 906 chronic hemodialysis patients either to continue hemodialysis (n=450) or to switch to high-efficiency postdilution OL-HDF (n=456). The primary outcome was all-cause mortality, and secondary outcomes included cardiovascular mortality, all-cause hospitalization, treatment tolerability, and laboratory data. Compared with patients who continued on hemodialysis, those assigned to OL-HDF had a 30% lower risk of all-cause mortality (hazard ratio [HR], 0.70; 95% confidence interval [95% CI], 0.53–0.92; P=0.01), a 33% lower risk of cardiovascular mortality (HR, 0.67; 95% CI, 0.44–1.02; P=0.06), and a 55% lower risk of infection-related mortality (HR, 0.45; 95% CI, 0.21–0.96; P=0.03). The estimated number needed to treat suggested that switching eight patients from hemodialysis to OL-HDF may prevent one annual death. The incidence rates of dialysis sessions complicated by hypotension and of all-cause hospitalization were lower in patients assigned to OL-HDF. In conclusion, high-efficiency postdilution OL-HDF reduces all-cause mortality compared with conventional hemodialysis. PMID:23411788

High-efficiency postdilution online hemodiafiltration reduces all-cause mortality in hemodialysis patients.

PubMed

Maduell, Francisco; Moreso, Francesc; Pons, Mercedes; Ramos, Rosa; Mora-Macià, Josep; Carreras, Jordi; Soler, Jordi; Torres, Ferran; Campistol, Josep M; Martinez-Castelao, Alberto

2013-02-01

Retrospective studies suggest that online hemodiafiltration (OL-HDF) may reduce the risk of mortality compared with standard hemodialysis in patients with ESRD. We conducted a multicenter, open-label, randomized controlled trial in which we assigned 906 chronic hemodialysis patients either to continue hemodialysis (n=450) or to switch to high-efficiency postdilution OL-HDF (n=456). The primary outcome was all-cause mortality, and secondary outcomes included cardiovascular mortality, all-cause hospitalization, treatment tolerability, and laboratory data. Compared with patients who continued on hemodialysis, those assigned to OL-HDF had a 30% lower risk of all-cause mortality (hazard ratio [HR], 0.70; 95% confidence interval [95% CI], 0.53-0.92; P=0.01), a 33% lower risk of cardiovascular mortality (HR, 0.67; 95% CI, 0.44-1.02; P=0.06), and a 55% lower risk of infection-related mortality (HR, 0.45; 95% CI, 0.21-0.96; P=0.03). The estimated number needed to treat suggested that switching eight patients from hemodialysis to OL-HDF may prevent one annual death. The incidence rates of dialysis sessions complicated by hypotension and of all-cause hospitalization were lower in patients assigned to OL-HDF. In conclusion, high-efficiency postdilution OL-HDF reduces all-cause mortality compared with conventional hemodialysis.

Estimating the burden of disease attributable to diabetes in South Africa in 2000.

PubMed

Bradshaw, Debbie; Norman, Rosana; Pieterse, Desiréé; Levitt, Naomi S

2007-08-01

To estimate the burden of disease attributable to diabetes by sex and age group in South Africa in 2000. The framework adopted for the most recent World Health Organization comparative risk assessment (CRA) methodology was followed. Small community studies used to derive the prevalence of diabetes by population group were weighted proportionately for a national estimate. Population-attributable fractions were calculated and applied to revised burden of disease estimates. Monte Carlo simulation-modelling techniques were used for uncertainty analysis. South Africa. Adults 30 years and older. Mortality and disability-adjusted life years (DALYs) for ischaemic heart disease (IHD), stroke, hypertensive disease and renal failure. Of South Africans aged >or= 30 years, 5.5% had diabetes which increased with age. Overall, about 14% of IHD, 10% of stroke, 12% of hypertensive disease and 12% of renal disease burden in adult males and females (30+ years) were attributable to diabetes. Diabetes was estimated to have caused 22,412 (95% uncertainty interval 20,755 - 24,872) or 4.3% (95% uncertainty interval 4.0 - 4.8%) of all deaths in South Africa in 2000. Since most of these occurred in middle or old age, the loss of healthy life years comprises a smaller proportion of the total 258,028 DALYs (95% uncertainty interval 236,856 - 290,849) in South Africa in 2000, accounting for 1.6% (95% uncertainty interval 1.5 - 1.8%) of the total burden. Diabetes is an important direct and indirect cause of burden in South Africa. Primary prevention of the disease through multi-level interventions and improved management at primary health care level are needed.

Robust characterization of small grating boxes using rotating stage Mueller matrix polarimeter

NASA Astrophysics Data System (ADS)

Foldyna, M.; De Martino, A.; Licitra, C.; Foucher, J.

2010-03-01

In this paper we demonstrate the robustness of the Mueller matrix polarimetry used in multiple-azimuth configuration. We first demonstrate the efficiency of the method for the characterization of small pitch gratings filling 250 μm wide square boxes. We used a Mueller matrix polarimeter directly installed in the clean room has motorized rotating stage allowing the access to arbitrary conical grating configurations. The projected beam spot size could be reduced to 60x25 μm, but for the measurements reported here this size was 100x100 μm. The optimal values of parameters of a trapezoidal profile model, acquired for each azimuthal angle separately using a non-linear least-square minimization algorithm, are shown for a typical grating. Further statistical analysis of the azimuth-dependent dimensional parameters provided realistic estimates of the confidence interval giving direct information about the accuracy of the results. The mean values and the standard deviations were calculated for 21 different grating boxes featuring in total 399 measured spectra and fits. The results for all boxes are summarized in a table which compares the optical method to the 3D-AFM. The essential conclusion of our work is that the 3D-AFM values always fall into the confidence intervals provided by the optical method, which means that we have successfully estimated the accuracy of our results without using direct comparison with another, non-optical, method. Moreover, this approach may provide a way to improve the accuracy of grating profile modeling by minimizing the standard deviations evaluated from multiple-azimuths results.

Parameter Estimation for Compact Binaries with Ground-Based Gravitational-Wave Observations Using the LALInference

NASA Technical Reports Server (NTRS)

Veitch, J.; Raymond, V.; Farr, B.; Farr, W.; Graff, P.; Vitale, S.; Aylott, B.; Blackburn, K.; Christensen, N.; Coughlin, M.

2015-01-01

The Advanced LIGO and Advanced Virgo gravitational wave (GW) detectors will begin operation in the coming years, with compact binary coalescence events a likely source for the first detections. The gravitational waveforms emitted directly encode information about the sources, including the masses and spins of the compact objects. Recovering the physical parameters of the sources from the GW observations is a key analysis task. This work describes the LALInference software library for Bayesian parameter estimation of compact binary signals, which builds on several previous methods to provide a well-tested toolkit which has already been used for several studies. We show that our implementation is able to correctly recover the parameters of compact binary signals from simulated data from the advanced GW detectors. We demonstrate this with a detailed comparison on three compact binary systems: a binary neutron star (BNS), a neutron star - black hole binary (NSBH) and a binary black hole (BBH), where we show a cross-comparison of results obtained using three independent sampling algorithms. These systems were analysed with non-spinning, aligned spin and generic spin configurations respectively, showing that consistent results can be obtained even with the full 15-dimensional parameter space of the generic spin configurations. We also demonstrate statistically that the Bayesian credible intervals we recover correspond to frequentist confidence intervals under correct prior assumptions by analysing a set of 100 signals drawn from the prior. We discuss the computational cost of these algorithms, and describe the general and problem-specific sampling techniques we have used to improve the efficiency of sampling the compact binary coalescence (CBC) parameter space.

Speed- and Circuit-Based High-Intensity Interval Training on Recovery Oxygen Consumption

PubMed Central

SCHLEPPENBACH, LINDSAY N.; EZER, ANDREAS B.; GRONEMUS, SARAH A.; WIDENSKI, KATELYN R.; BRAUN, SAORI I.; JANOT, JEFFREY M.

2017-01-01

Due to the current obesity epidemic in the United States, there is growing interest in efficient, effective ways to increase energy expenditure and weight loss. Research has shown that high-intensity exercise elicits a higher Excess Post-Exercise Oxygen Consumption (EPOC) throughout the day compared to steady-state exercise. Currently, there is no single research study that examines the differences in Recovery Oxygen Consumption (ROC) resulting from high-intensity interval training (HIIT) modalities. The purpose of this study is to review the impact of circuit training (CT) and speed interval training (SIT), on ROC in both regular exercising and sedentary populations. A total of 26 participants were recruited from the UW-Eau Claire campus and divided into regularly exercising and sedentary groups, according to self-reported exercise participation status. Oxygen consumption was measured during and after two HIIT sessions and was used to estimate caloric expenditure. There was no significant difference in caloric expenditure during and after exercise among individuals who regularly exercise and individuals who are sedentary. There was also no significant difference in ROC between regular exercisers and sedentary or between SIT and CT. However, there was a significantly higher caloric expenditure in SIT vs. CT regardless of exercise status. It is recommended that individuals engage in SIT vs. CT when the goal is to maximize overall caloric expenditure. With respect to ROC, individuals can choose either modalities of HIIT to achieve similar effects on increased oxygen consumption post-exercise. PMID:29170696

Speed- and Circuit-Based High-Intensity Interval Training on Recovery Oxygen Consumption.

PubMed

Schleppenbach, Lindsay N; Ezer, Andreas B; Gronemus, Sarah A; Widenski, Katelyn R; Braun, Saori I; Janot, Jeffrey M

2017-01-01

Due to the current obesity epidemic in the United States, there is growing interest in efficient, effective ways to increase energy expenditure and weight loss. Research has shown that high-intensity exercise elicits a higher Excess Post-Exercise Oxygen Consumption (EPOC) throughout the day compared to steady-state exercise. Currently, there is no single research study that examines the differences in Recovery Oxygen Consumption (ROC) resulting from high-intensity interval training (HIIT) modalities. The purpose of this study is to review the impact of circuit training (CT) and speed interval training (SIT), on ROC in both regular exercising and sedentary populations. A total of 26 participants were recruited from the UW-Eau Claire campus and divided into regularly exercising and sedentary groups, according to self-reported exercise participation status. Oxygen consumption was measured during and after two HIIT sessions and was used to estimate caloric expenditure. There was no significant difference in caloric expenditure during and after exercise among individuals who regularly exercise and individuals who are sedentary. There was also no significant difference in ROC between regular exercisers and sedentary or between SIT and CT. However, there was a significantly higher caloric expenditure in SIT vs. CT regardless of exercise status. It is recommended that individuals engage in SIT vs. CT when the goal is to maximize overall caloric expenditure. With respect to ROC, individuals can choose either modalities of HIIT to achieve similar effects on increased oxygen consumption post-exercise.

Bullying and mental health and suicidal behaviour among 14- to 15-year-olds in a representative sample of Australian children.

PubMed

Ford, Rebecca; King, Tania; Priest, Naomi; Kavanagh, Anne

2017-09-01

To provide the first Australian population-based estimates of the association between bullying and adverse mental health outcomes and suicidality among Australian adolescents. Analysis of data from 3537 adolescents, aged 14-15 years from Wave 6 of the K-cohort of Longitudinal Study of Australian Children was conducted. We used Poisson and linear regression to estimate associations between bullying type (none, relational-verbal, physical, both types) and role (no role, victim, bully, victim and bully), and mental health (measured by the Strengths and Difficulties Questionnaire, symptoms of anxiety and depression) and suicidality. Adolescents involved in bullying had significantly increased Strengths and Difficulties Questionnaire, depression and anxiety scores in all bullying roles and types. In terms of self-harm and suicidality, bully-victims had the highest risk of self-harm (prevalence rate ratio 4.7, 95% confidence interval [3.26, 6.83]), suicidal ideation (prevalence rate ratio 4.3, 95% confidence interval [2.83, 6.49]), suicidal plan (prevalence rate ratio 4.1, 95% confidence interval [2.54, 6.58]) and attempts (prevalence rate ratio 2.7, 95% confidence interval [1.39, 5.13]), followed by victims then bullies. The experience of both relational-verbal and physical bullying was associated with the highest risk of self-harm (prevalence rate ratio 4.6, 95% confidence interval [3.15, 6.60]), suicidal ideation or plans (prevalence rate ratio 4.6, 95% confidence interval [3.05, 6.95]; and 4.8, 95% confidence interval [3.01, 7.64], respectively) or suicide attempts (prevalence rate ratio 3.5, 95% confidence interval [1.90, 6.30]). This study presents the first national, population-based estimates of the associations between bullying by peers and mental health outcomes in Australian adolescents. The markedly increased risk of poor mental health outcomes, self-harm and suicidal ideation and behaviours among adolescents who experienced bullying highlights the importance of addressing bullying in school settings.

An efficient sampling approach for variance-based sensitivity analysis based on the law of total variance in the successive intervals without overlapping

NASA Astrophysics Data System (ADS)

Yun, Wanying; Lu, Zhenzhou; Jiang, Xian

2018-06-01

To efficiently execute the variance-based global sensitivity analysis, the law of total variance in the successive intervals without overlapping is proved at first, on which an efficient space-partition sampling-based approach is subsequently proposed in this paper. Through partitioning the sample points of output into different subsets according to different inputs, the proposed approach can efficiently evaluate all the main effects concurrently by one group of sample points. In addition, there is no need for optimizing the partition scheme in the proposed approach. The maximum length of subintervals is decreased by increasing the number of sample points of model input variables in the proposed approach, which guarantees the convergence condition of the space-partition approach well. Furthermore, a new interpretation on the thought of partition is illuminated from the perspective of the variance ratio function. Finally, three test examples and one engineering application are employed to demonstrate the accuracy, efficiency and robustness of the proposed approach.

MODFLOW 2000 Head Uncertainty, a First-Order Second Moment Method

USGS Publications Warehouse

Glasgow, H.S.; Fortney, M.D.; Lee, J.; Graettinger, A.J.; Reeves, H.W.

2003-01-01

A computationally efficient method to estimate the variance and covariance in piezometric head results computed through MODFLOW 2000 using a first-order second moment (FOSM) approach is presented. This methodology employs a first-order Taylor series expansion to combine model sensitivity with uncertainty in geologic data. MODFLOW 2000 is used to calculate both the ground water head and the sensitivity of head to changes in input data. From a limited number of samples, geologic data are extrapolated and their associated uncertainties are computed through a conditional probability calculation. Combining the spatially related sensitivity and input uncertainty produces the variance-covariance matrix, the diagonal of which is used to yield the standard deviation in MODFLOW 2000 head. The variance in piezometric head can be used for calibrating the model, estimating confidence intervals, directing exploration, and evaluating the reliability of a design. A case study illustrates the approach, where aquifer transmissivity is the spatially related uncertain geologic input data. The FOSM methodology is shown to be applicable for calculating output uncertainty for (1) spatially related input and output data, and (2) multiple input parameters (transmissivity and recharge).

Maternal Risk Exposure and Adult Daughters' Health, Schooling, and Employment: A Constructed Cohort Analysis of 50 Developing Countries.

PubMed

Li, Qingfeng; Tsui, Amy O

2016-06-01

This study analyzes the relationships between maternal risk factors present at the time of daughters' births-namely, young mother, high parity, and short preceding birth interval-and their subsequent adult developmental, reproductive, and socioeconomic outcomes. Pseudo-cohorts are constructed using female respondent data from 189 cross-sectional rounds of Demographic and Health Surveys conducted in 50 developing countries between 1986 and 2013. Generalized linear models are estimated to test the relationships and calculate cohort-level outcome proportions with the systematic elimination of the three maternal risk factors. The simulation exercise for the full sample of 2,546 pseudo-cohorts shows that the combined elimination of risk exposures is associated with lower mean proportions of adult daughters experiencing child mortality, having a small infant at birth, and having a low body mass index. Among sub-Saharan African cohorts, the estimated changes are larger, particularly for years of schooling. The pseudo-cohort approach can enable longitudinal testing of life course hypotheses using large-scale, standardized, repeated cross-sectional data and with considerable resource efficiency.

Evaluation of marginal failures of dental composite restorations by acoustic emission analysis.

PubMed

Gu, Ja-Uk; Choi, Nak-Sam

2013-01-01

In this study, a nondestructive method based on acoustic emission (AE) analysis was developed to evaluate the marginal failure states of dental composite restorations. Three types of ring-shaped substrates, which were modeled after a Class I cavity, were prepared from polymethyl methacrylate, stainless steel, and human molar teeth. A bonding agent and a composite resin were applied to the ring-shaped substrates and cured by light exposure. At each time-interval measurement, the tooth substrate presented a higher number of AE hits than polymethyl methacrylate and steel substrates. Marginal disintegration estimations derived from cumulative AE hits and cumulative AE energy parameters showed that a signification portion of marginal gap formation was already realized within 1 min at the initial light-curing stage. Estimation based on cumulative AE energy gave a higher level of marginal failure than that based on AE hits. It was concluded that the AE analysis method developed in this study was a viable approach in predicting the clinical survival of dental composite restorations efficiently within a short test period.

A comparison of turtle sampling methods in a small lake in Standing Stone State Park, Overton County, Tennessee

USGS Publications Warehouse

Weber, A.; Layzer, James B.

2011-01-01

We used basking traps and hoop nets to sample turtles in Standing Stone Lake at 2-week intervals from May to November 2006. In alternate weeks, we conducted visual basking surveys. We collected and observed four species of turtles: spiny softshell (Apalone spinifera), northern map turtle (Graptemys geographica), pond slider (Trachernys scripta), and snapping turtle (Chelydra serpentina). Relative abundances varied greatly among sampling methods. To varying degrees, all methods were species selective. Population estimates from mark and recaptures of three species, basking counts, and hoop net catches indicated that pond sliders were the most abundant species, but northern map turtles were 8× more abundant than pond sliders in basking trap catches. We saw relatively few snapping turtles basking even though population estimates indicated they were the second most abundant species. Populations of all species were dominated by adult individuals. Sex ratios of three species differed significantly from 1:1. Visual surveys were the most efficient method for determining the presence of species, but capture methods were necessary to obtain size and sex data.

Estimating source parameters from deformation data, with an application to the March 1997 earthquake swarm off the Izu Peninsula, Japan

NASA Astrophysics Data System (ADS)

Cervelli, P.; Murray, M. H.; Segall, P.; Aoki, Y.; Kato, T.

2001-06-01

We have applied two Monte Carlo optimization techniques, simulated annealing and random cost, to the inversion of deformation data for fault and magma chamber geometry. These techniques involve an element of randomness that permits them to escape local minima and ultimately converge to the global minimum of misfit space. We have tested the Monte Carlo algorithms on two synthetic data sets. We have also compared them to one another in terms of their efficiency and reliability. We have applied the bootstrap method to estimate confidence intervals for the source parameters, including the correlations inherent in the data. Additionally, we present methods that use the information from the bootstrapping procedure to visualize the correlations between the different model parameters. We have applied these techniques to GPS, tilt, and leveling data from the March 1997 earthquake swarm off of the Izu Peninsula, Japan. Using the two Monte Carlo algorithms, we have inferred two sources, a dike and a fault, that fit the deformation data and the patterns of seismicity and that are consistent with the regional stress field.

Use of claims data to estimate annual cervical cancer screening percentages in Portland metropolitan area, Oregon.

PubMed

Abdullah, Nasreen; Laing, Robert S; Hariri, Susan; Young, Collette M; Schafer, Sean

2016-04-01

Human papillomavirus (HPV) vaccine should reduce cervical dysplasia before cervical cancer. However, dysplasia diagnosis is screening-dependent. Accurate screening estimates are needed. To estimate the percentage of women in a geographic population that has had cervical cancer screening. We analyzed claims data for (Papanicolau) Pap tests from 2008-2012 to estimate the percentage of insured women aged 18-39 years screened. We estimated screening in uninsured women by dividing the percentage of insured Behavioral Risk Factor Surveillance Survey respondents reporting previous-year testing by the percentage of uninsured respondents reporting previous-year testing, and multiplying this ratio by claims-based estimates of insured women with previous-year screening. We calculated a simple weighted average of the two estimates to estimate overall screening percentage. We estimated credible intervals using Monte-Carlo simulations. During 2008-2012, an annual average of 29.6% of women aged 18-39 years were screened. Screening increased from 2008 to 2009 in all age groups. During 2009-2012, the screening percentages decreased for all groups, but declined most in women aged 18-20 years, from 21.5% to 5.4%. Within age groups, compared to 2009, credible intervals did not overlap during 2011 (except age group 21-29 years) and 2012, and credible intervals in the 18-20 year group did not overlap with older groups in any year. This introduces a novel method to estimate population-level cervical cancer screening. Overall, percentage of women screened in Portland, Oregon fell following changes in screening recommendations released in 2009 and later modified in 2012. Copyright © 2016 Elsevier Ltd. All rights reserved.

IBM system/360 assembly language interval arithmetic software

NASA Technical Reports Server (NTRS)

Phillips, E. J.

1972-01-01

Computer software designed to perform interval arithmetic is described. An interval is defined as the set of all real numbers between two given numbers including or excluding one or both endpoints. Interval arithmetic consists of the various elementary arithmetic operations defined on the set of all intervals, such as interval addition, subtraction, union, etc. One of the main applications of interval arithmetic is in the area of error analysis of computer calculations. For example, it has been used sucessfully to compute bounds on sounding errors in the solution of linear algebraic systems, error bounds in numerical solutions of ordinary differential equations, as well as integral equations and boundary value problems. The described software enables users to implement algorithms of the type described in references efficiently on the IBM 360 system.

Evaluating Protocol Lifecycle Time Intervals in HIV/AIDS Clinical Trials

PubMed Central

Schouten, Jeffrey T.; Dixon, Dennis; Varghese, Suresh; Cope, Marie T.; Marci, Joe; Kagan, Jonathan M.

2014-01-01

Background Identifying efficacious interventions for the prevention and treatment of human diseases depends on the efficient development and implementation of controlled clinical trials. Essential to reducing the time and burden of completing the clinical trial lifecycle is determining which aspects take the longest, delay other stages, and may lead to better resource utilization without diminishing scientific quality, safety, or the protection of human subjects. Purpose In this study we modeled time-to-event data to explore relationships between clinical trial protocol development and implementation times, as well as identify potential correlates of prolonged development and implementation. Methods We obtained time interval and participant accrual data from 111 interventional clinical trials initiated between 2006 and 2011 by NIH’s HIV/AIDS Clinical Trials Networks. We determined the time (in days) required to complete defined phases of clinical trial protocol development and implementation. Kaplan-Meier estimates were used to assess the rates at which protocols reached specified terminal events, stratified by study purpose (therapeutic, prevention) and phase group (pilot/phase I, phase II, and phase III/ IV). We also examined several potential correlates to prolonged development and implementation intervals. Results Even though phase grouping did not determine development or implementation times of either therapeutic or prevention studies, overall we observed wide variation in protocol development times. Moreover, we detected a trend toward phase III/IV therapeutic protocols exhibiting longer developmental (median 2 ½ years) and implementation times (>3years). We also found that protocols exceeding the median number of days for completing the development interval had significantly longer implementation. Limitations The use of a relatively small set of protocols may have limited our ability to detect differences across phase groupings. Some timing effects present for a specific study phase may have been masked by combining protocols into phase groupings. Presence of informative censoring, such as withdrawal of some protocols from development if they began showing signs of lost interest among investigators, complicates interpretation of Kaplan-Meier estimates. Because this study constitutes a retrospective examination over an extended period of time, it does not allow for the precise identification of relative factors impacting timing. Conclusions Delays not only increase the time and cost to complete clinical trials, but they also diminish their usefulness by failing to answer research questions in time. We believe that research analyzing the time spent traversing defined intervals across the clinical trial protocol development and implementation continuum can stimulate business process analyses and reengineering efforts that could lead to reductions in the time from clinical trial concept to results, thereby accelerating progress in clinical research. PMID:24980279

Compressive Sensing of Foot Gait Signals and Its Application for the Estimation of Clinically Relevant Time Series.

PubMed

Pant, Jeevan K; Krishnan, Sridhar

2016-07-01

A new signal reconstruction algorithm for compressive sensing based on the minimization of a pseudonorm which promotes block-sparse structure on the first-order difference of the signal is proposed. Involved optimization is carried out by using a sequential version of Fletcher-Reeves' conjugate-gradient algorithm, and the line search is based on Banach's fixed-point theorem. The algorithm is suitable for the reconstruction of foot gait signals which admit block-sparse structure on the first-order difference. An additional algorithm for the estimation of stride-interval, swing-interval, and stance-interval time series from the reconstructed foot gait signals is also proposed. This algorithm is based on finding zero crossing indices of the foot gait signal and using the resulting indices for the computation of time series. Extensive simulation results demonstrate that the proposed signal reconstruction algorithm yields improved signal-to-noise ratio and requires significantly reduced computational effort relative to several competing algorithms over a wide range of compression ratio. For a compression ratio in the range from 88% to 94%, the proposed algorithm is found to offer improved accuracy for the estimation of clinically relevant time-series parameters, namely, the mean value, variance, and spectral index of stride-interval, stance-interval, and swing-interval time series, relative to its nearest competitor algorithm. The improvement in performance for compression ratio as high as 94% indicates that the proposed algorithms would be useful for designing compressive sensing-based systems for long-term telemonitoring of human gait signals.

Re-evaluating neonatal-age models for ungulates: Does model choice affect survival estimates?

USGS Publications Warehouse

Grovenburg, Troy W.; Monteith, Kevin L.; Jacques, Christopher N.; Klaver, Robert W.; DePerno, Christopher S.; Brinkman, Todd J.; Monteith, Kyle B.; Gilbert, Sophie L.; Smith, Joshua B.; Bleich, Vernon C.; Swanson, Christopher C.; Jenks, Jonathan A.

2014-01-01

New-hoof growth is regarded as the most reliable metric for predicting age of newborn ungulates, but variation in estimated age among hoof-growth equations that have been developed may affect estimates of survival in staggered-entry models. We used known-age newborns to evaluate variation in age estimates among existing hoof-growth equations and to determine the consequences of that variation on survival estimates. During 2001–2009, we captured and radiocollared 174 newborn (≤24-hrs old) ungulates: 76 white-tailed deer (Odocoileus virginianus) in Minnesota and South Dakota, 61 mule deer (O. hemionus) in California, and 37 pronghorn (Antilocapra americana) in South Dakota. Estimated age of known-age newborns differed among hoof-growth models and varied by >15 days for white-tailed deer, >20 days for mule deer, and >10 days for pronghorn. Accuracy (i.e., the proportion of neonates assigned to the correct age) in aging newborns using published equations ranged from 0.0% to 39.4% in white-tailed deer, 0.0% to 3.3% in mule deer, and was 0.0% for pronghorns. Results of survival modeling indicated that variability in estimates of age-at-capture affected short-term estimates of survival (i.e., 30 days) for white-tailed deer and mule deer, and survival estimates over a longer time frame (i.e., 120 days) for mule deer. Conversely, survival estimates for pronghorn were not affected by estimates of age. Our analyses indicate that modeling survival in daily intervals is too fine a temporal scale when age-at-capture is unknown given the potential inaccuracies among equations used to estimate age of neonates. Instead, weekly survival intervals are more appropriate because most models accurately predicted ages within 1 week of the known age. Variation among results of neonatal-age models on short- and long-term estimates of survival for known-age young emphasizes the importance of selecting an appropriate hoof-growth equation and appropriately defining intervals (i.e., weekly versus daily) for estimating survival.

Small UAS-Based Wind Feature Identification System Part 1: Integration and Validation

PubMed Central

Rodriguez Salazar, Leopoldo; Cobano, Jose A.; Ollero, Anibal

2016-01-01

This paper presents a system for identification of wind features, such as gusts and wind shear. These are of particular interest in the context of energy-efficient navigation of Small Unmanned Aerial Systems (UAS). The proposed system generates real-time wind vector estimates and a novel algorithm to generate wind field predictions. Estimations are based on the integration of an off-the-shelf navigation system and airspeed readings in a so-called direct approach. Wind predictions use atmospheric models to characterize the wind field with different statistical analyses. During the prediction stage, the system is able to incorporate, in a big-data approach, wind measurements from previous flights in order to enhance the approximations. Wind estimates are classified and fitted into a Weibull probability density function. A Genetic Algorithm (GA) is utilized to determine the shaping and scale parameters of the distribution, which are employed to determine the most probable wind speed at a certain position. The system uses this information to characterize a wind shear or a discrete gust and also utilizes a Gaussian Process regression to characterize continuous gusts. The knowledge of the wind features is crucial for computing energy-efficient trajectories with low cost and payload. Therefore, the system provides a solution that does not require any additional sensors. The system architecture presents a modular decentralized approach, in which the main parts of the system are separated in modules and the exchange of information is managed by a communication handler to enhance upgradeability and maintainability. Validation is done providing preliminary results of both simulations and Software-In-The-Loop testing. Telemetry data collected from real flights, performed in the Seville Metropolitan Area in Andalusia (Spain), was used for testing. Results show that wind estimation and predictions can be calculated at 1 Hz and a wind map can be updated at 0.4 Hz. Predictions show a convergence time with a 95% confidence interval of approximately 30 s. PMID:28025531

Small UAS-Based Wind Feature Identification System Part 1: Integration and Validation.

PubMed

Rodriguez Salazar, Leopoldo; Cobano, Jose A; Ollero, Anibal

2016-12-23

This paper presents a system for identification of wind features, such as gusts and wind shear. These are of particular interest in the context of energy-efficient navigation of Small Unmanned Aerial Systems (UAS). The proposed system generates real-time wind vector estimates and a novel algorithm to generate wind field predictions. Estimations are based on the integration of an off-the-shelf navigation system and airspeed readings in a so-called direct approach. Wind predictions use atmospheric models to characterize the wind field with different statistical analyses. During the prediction stage, the system is able to incorporate, in a big-data approach, wind measurements from previous flights in order to enhance the approximations. Wind estimates are classified and fitted into a Weibull probability density function. A Genetic Algorithm (GA) is utilized to determine the shaping and scale parameters of the distribution, which are employed to determine the most probable wind speed at a certain position. The system uses this information to characterize a wind shear or a discrete gust and also utilizes a Gaussian Process regression to characterize continuous gusts. The knowledge of the wind features is crucial for computing energy-efficient trajectories with low cost and payload. Therefore, the system provides a solution that does not require any additional sensors. The system architecture presents a modular decentralized approach, in which the main parts of the system are separated in modules and the exchange of information is managed by a communication handler to enhance upgradeability and maintainability. Validation is done providing preliminary results of both simulations and Software-In-The-Loop testing. Telemetry data collected from real flights, performed in the Seville Metropolitan Area in Andalusia (Spain), was used for testing. Results show that wind estimation and predictions can be calculated at 1 Hz and a wind map can be updated at 0.4 Hz . Predictions show a convergence time with a 95% confidence interval of approximately 30 s .

The effects of sampling frequency on the climate statistics of the European Centre for Medium-Range Weather Forecasts

NASA Astrophysics Data System (ADS)

Phillips, Thomas J.; Gates, W. Lawrence; Arpe, Klaus

1992-12-01

The effects of sampling frequency on the first- and second-moment statistics of selected European Centre for Medium-Range Weather Forecasts (ECMWF) model variables are investigated in a simulation of "perpetual July" with a diurnal cycle included and with surface and atmospheric fields saved at hourly intervals. The shortest characteristic time scales (as determined by the e-folding time of lagged autocorrelation functions) are those of ground heat fluxes and temperatures, precipitation and runoff, convective processes, cloud properties, and atmospheric vertical motion, while the longest time scales are exhibited by soil temperature and moisture, surface pressure, and atmospheric specific humidity, temperature, and wind. The time scales of surface heat and momentum fluxes and of convective processes are substantially shorter over land than over oceans. An appropriate sampling frequency for each model variable is obtained by comparing the estimates of first- and second-moment statistics determined at intervals ranging from 2 to 24 hours with the "best" estimates obtained from hourly sampling. Relatively accurate estimation of first- and second-moment climate statistics (10% errors in means, 20% errors in variances) can be achieved by sampling a model variable at intervals that usually are longer than the bandwidth of its time series but that often are shorter than its characteristic time scale. For the surface variables, sampling at intervals that are nonintegral divisors of a 24-hour day yields relatively more accurate time-mean statistics because of a reduction in errors associated with aliasing of the diurnal cycle and higher-frequency harmonics. The superior estimates of first-moment statistics are accompanied by inferior estimates of the variance of the daily means due to the presence of systematic biases, but these probably can be avoided by defining a different measure of low-frequency variability. Estimates of the intradiurnal variance of accumulated precipitation and surface runoff also are strongly impacted by the length of the storage interval. In light of these results, several alternative strategies for storage of the EMWF model variables are recommended.

A practical Bayesian stepped wedge design for community-based cluster-randomized clinical trials: The British Columbia Telehealth Trial.

PubMed

Cunanan, Kristen M; Carlin, Bradley P; Peterson, Kevin A

2016-12-01

Many clinical trial designs are impractical for community-based clinical intervention trials. Stepped wedge trial designs provide practical advantages, but few descriptions exist of their clinical implementational features, statistical design efficiencies, and limitations. Enhance efficiency of stepped wedge trial designs by evaluating the impact of design characteristics on statistical power for the British Columbia Telehealth Trial. The British Columbia Telehealth Trial is a community-based, cluster-randomized, controlled clinical trial in rural and urban British Columbia. To determine the effect of an Internet-based telehealth intervention on healthcare utilization, 1000 subjects with an existing diagnosis of congestive heart failure or type 2 diabetes will be enrolled from 50 clinical practices. Hospital utilization is measured using a composite of disease-specific hospital admissions and emergency visits. The intervention comprises online telehealth data collection and counseling provided to support a disease-specific action plan developed by the primary care provider. The planned intervention is sequentially introduced across all participating practices. We adopt a fully Bayesian, Markov chain Monte Carlo-driven statistical approach, wherein we use simulation to determine the effect of cluster size, sample size, and crossover interval choice on type I error and power to evaluate differences in hospital utilization. For our Bayesian stepped wedge trial design, simulations suggest moderate decreases in power when crossover intervals from control to intervention are reduced from every 3 to 2 weeks, and dramatic decreases in power as the numbers of clusters decrease. Power and type I error performance were not notably affected by the addition of nonzero cluster effects or a temporal trend in hospitalization intensity. Stepped wedge trial designs that intervene in small clusters across longer periods can provide enhanced power to evaluate comparative effectiveness, while offering practical implementation advantages in geographic stratification, temporal change, use of existing data, and resource distribution. Current population estimates were used; however, models may not reflect actual event rates during the trial. In addition, temporal or spatial heterogeneity can bias treatment effect estimates. © The Author(s) 2016.

Calculation of the confidence intervals for transformation parameters in the registration of medical images

PubMed Central

Bansal, Ravi; Staib, Lawrence H.; Laine, Andrew F.; Xu, Dongrong; Liu, Jun; Posecion, Lainie F.; Peterson, Bradley S.

2010-01-01

Images from different individuals typically cannot be registered precisely because anatomical features within the images differ across the people imaged and because the current methods for image registration have inherent technological limitations that interfere with perfect registration. Quantifying the inevitable error in image registration is therefore of crucial importance in assessing the effects that image misregistration may have on subsequent analyses in an imaging study. We have developed a mathematical framework for quantifying errors in registration by computing the confidence intervals of the estimated parameters (3 translations, 3 rotations, and 1 global scale) for the similarity transformation. The presence of noise in images and the variability in anatomy across individuals ensures that estimated registration parameters are always random variables. We assume a functional relation among intensities across voxels in the images, and we use the theory of nonlinear, least-squares estimation to show that the parameters are multivariate Gaussian distributed. We then use the covariance matrix of this distribution to compute the confidence intervals of the transformation parameters. These confidence intervals provide a quantitative assessment of the registration error across the images. Because transformation parameters are nonlinearly related to the coordinates of landmark points in the brain, we subsequently show that the coordinates of those landmark points are also multivariate Gaussian distributed. Using these distributions, we then compute the confidence intervals of the coordinates for landmark points in the image. Each of these confidence intervals in turn provides a quantitative assessment of the registration error at a particular landmark point. Because our method is computationally intensive, however, its current implementation is limited to assessing the error of the parameters in the similarity transformation across images. We assessed the performance of our method in computing the error in estimated similarity parameters by applying that method to real world dataset. Our results showed that the size of the confidence intervals computed using our method decreased – i.e. our confidence in the registration of images from different individuals increased – for increasing amounts of blur in the images. Moreover, the size of the confidence intervals increased for increasing amounts of noise, misregistration, and differing anatomy. Thus, our method precisely quantified confidence in the registration of images that contain varying amounts of misregistration and varying anatomy across individuals. PMID:19138877

Estimating the incidence of rotavirus infection in children from India and Malawi from serial anti-rotavirus IgA titres.

PubMed

Bennett, Aisleen; Nagelkerke, Nico; Heinsbroek, Ellen; Premkumar, Prasanna S; Wnęk, Małgorzata; Kang, Gagandeep; French, Neil; Cunliffe, Nigel A; Bar-Zeev, Naor; Lopman, Ben; Iturriza-Gomara, Miren

2017-01-01

Accurate estimates of rotavirus incidence in infants are crucial given disparities in rotavirus vaccine effectiveness from low-income settings. Sero-surveys are a pragmatic means of estimating incidence however serological data is prone to misclassification. This study used mixture models to estimate incidence of rotavirus infection from anti-rotavirus immunoglobulin A (IgA) titres in infants from Vellore, India, and Karonga, Malawi. IgA titres were measured using serum samples collected at 6 month intervals for 36 months from 373 infants from Vellore and 12 months from 66 infants from Karonga. Mixture models (two component Gaussian mixture distributions) were fit to the difference in titres between time points to estimate risk of sero-positivity and derive incidence estimates. A peak incidence of 1.05(95% confidence interval [CI]: 0.64, 1.64) infections per child-year was observed in the first 6 months of life in Vellore. This declined incrementally with each subsequent time interval. Contrastingly in Karonga incidence was greatest in the second 6 months of life (1.41 infections per child year [95% CI: 0.79, 2.29]). This study demonstrates that infants from Vellore experience peak rotavirus incidence earlier than those from Karonga. Identifying such differences in transmission patterns is important in informing vaccine strategy, particularly where vaccine effectiveness is modest.

Estimating the incidence of rotavirus infection in children from India and Malawi from serial anti-rotavirus IgA titres

PubMed Central

Nagelkerke, Nico; Heinsbroek, Ellen; Premkumar, Prasanna S.; Wnęk, Małgorzata; Kang, Gagandeep; French, Neil; Cunliffe, Nigel A.; Bar-Zeev, Naor

2017-01-01

Accurate estimates of rotavirus incidence in infants are crucial given disparities in rotavirus vaccine effectiveness from low-income settings. Sero-surveys are a pragmatic means of estimating incidence however serological data is prone to misclassification. This study used mixture models to estimate incidence of rotavirus infection from anti-rotavirus immunoglobulin A (IgA) titres in infants from Vellore, India, and Karonga, Malawi. IgA titres were measured using serum samples collected at 6 month intervals for 36 months from 373 infants from Vellore and 12 months from 66 infants from Karonga. Mixture models (two component Gaussian mixture distributions) were fit to the difference in titres between time points to estimate risk of sero-positivity and derive incidence estimates. A peak incidence of 1.05(95% confidence interval [CI]: 0.64, 1.64) infections per child-year was observed in the first 6 months of life in Vellore. This declined incrementally with each subsequent time interval. Contrastingly in Karonga incidence was greatest in the second 6 months of life (1.41 infections per child year [95% CI: 0.79, 2.29]). This study demonstrates that infants from Vellore experience peak rotavirus incidence earlier than those from Karonga. Identifying such differences in transmission patterns is important in informing vaccine strategy, particularly where vaccine effectiveness is modest. PMID:29287122

Selecting Sensitive Parameter Subsets in Dynamical Models With Application to Biomechanical System Identification.

PubMed

Ramadan, Ahmed; Boss, Connor; Choi, Jongeun; Peter Reeves, N; Cholewicki, Jacek; Popovich, John M; Radcliffe, Clark J

2018-07-01

Estimating many parameters of biomechanical systems with limited data may achieve good fit but may also increase 95% confidence intervals in parameter estimates. This results in poor identifiability in the estimation problem. Therefore, we propose a novel method to select sensitive biomechanical model parameters that should be estimated, while fixing the remaining parameters to values obtained from preliminary estimation. Our method relies on identifying the parameters to which the measurement output is most sensitive. The proposed method is based on the Fisher information matrix (FIM). It was compared against the nonlinear least absolute shrinkage and selection operator (LASSO) method to guide modelers on the pros and cons of our FIM method. We present an application identifying a biomechanical parametric model of a head position-tracking task for ten human subjects. Using measured data, our method (1) reduced model complexity by only requiring five out of twelve parameters to be estimated, (2) significantly reduced parameter 95% confidence intervals by up to 89% of the original confidence interval, (3) maintained goodness of fit measured by variance accounted for (VAF) at 82%, (4) reduced computation time, where our FIM method was 164 times faster than the LASSO method, and (5) selected similar sensitive parameters to the LASSO method, where three out of five selected sensitive parameters were shared by FIM and LASSO methods.

Quantifying the impact of time-varying baseline risk adjustment in the self-controlled risk interval design.

PubMed

Li, Lingling; Kulldorff, Martin; Russek-Cohen, Estelle; Kawai, Alison Tse; Hua, Wei

2015-12-01

The self-controlled risk interval design is commonly used to assess the association between an acute exposure and an adverse event of interest, implicitly adjusting for fixed, non-time-varying covariates. Explicit adjustment needs to be made for time-varying covariates, for example, age in young children. It can be performed via either a fixed or random adjustment. The random-adjustment approach can provide valid point and interval estimates but requires access to individual-level data for an unexposed baseline sample. The fixed-adjustment approach does not have this requirement and will provide a valid point estimate but may underestimate the variance. We conducted a comprehensive simulation study to evaluate their performance. We designed the simulation study using empirical data from the Food and Drug Administration-sponsored Mini-Sentinel Post-licensure Rapid Immunization Safety Monitoring Rotavirus Vaccines and Intussusception study in children 5-36.9 weeks of age. The time-varying confounder is age. We considered a variety of design parameters including sample size, relative risk, time-varying baseline risks, and risk interval length. The random-adjustment approach has very good performance in almost all considered settings. The fixed-adjustment approach can be used as a good alternative when the number of events used to estimate the time-varying baseline risks is at least the number of events used to estimate the relative risk, which is almost always the case. We successfully identified settings in which the fixed-adjustment approach can be used as a good alternative and provided guidelines on the selection and implementation of appropriate analyses for the self-controlled risk interval design. Copyright © 2015 John Wiley & Sons, Ltd.

Sampling effects on the identification of roadkill hotspots: Implications for survey design.

PubMed

Santos, Sara M; Marques, J Tiago; Lourenço, André; Medinas, Denis; Barbosa, A Márcia; Beja, Pedro; Mira, António

2015-10-01

Although locating wildlife roadkill hotspots is essential to mitigate road impacts, the influence of study design on hotspot identification remains uncertain. We evaluated how sampling frequency affects the accuracy of hotspot identification, using a dataset of vertebrate roadkills (n = 4427) recorded over a year of daily surveys along 37 km of roads. "True" hotspots were identified using this baseline dataset, as the 500-m segments where the number of road-killed vertebrates exceeded the upper 95% confidence limit of the mean, assuming a Poisson distribution of road-kills per segment. "Estimated" hotspots were identified likewise, using datasets representing progressively lower sampling frequencies, which were produced by extracting data from the baseline dataset at appropriate time intervals (1-30 days). Overall, 24.3% of segments were "true" hotspots, concentrating 40.4% of roadkills. For different groups, "true" hotspots accounted from 6.8% (bats) to 29.7% (small birds) of road segments, concentrating from <40% (frogs and toads, snakes) to >60% (lizards, lagomorphs, carnivores) of roadkills. Spatial congruence between "true" and "estimated" hotspots declined rapidly with increasing time interval between surveys, due primarily to increasing false negatives (i.e., missing "true" hotspots). There were also false positives (i.e., wrong "estimated" hotspots), particularly at low sampling frequencies. Spatial accuracy decay with increasing time interval between surveys was higher for smaller-bodied (amphibians, reptiles, small birds, small mammals) than for larger-bodied species (birds of prey, hedgehogs, lagomorphs, carnivores). Results suggest that widely used surveys at weekly or longer intervals may produce poor estimates of roadkill hotspots, particularly for small-bodied species. Surveying daily or at two-day intervals may be required to achieve high accuracy in hotspot identification for multiple species. Copyright © 2015 Elsevier Ltd. All rights reserved.

Impact of Time to Treatment Initiation in Patients with Human Papillomavirus-positive and -negative Oropharyngeal Squamous Cell Carcinoma.

PubMed

Grønhøj, C; Jensen, D; Dehlendorff, C; Nørregaard, C; Andersen, E; Specht, L; Charabi, B; von Buchwald, C

2018-06-01

The distinct difference in disease phenotype of human papillomavirus-positive (HPV+) and -negative (HPV-) oropharyngeal squamous cell cancer (OPSCC) patients might also be apparent when assessing the effect of time to treatment initiation (TTI). We assessed the overall survival and progression-free survival (PFS) effect from increasing TTI for HPV+ and HPV- OPSCC patients. We examined patients who received curative-intended therapy for OPSCC in eastern Denmark between 2000 and 2014. TTI was the number of days from diagnosis to the initiation of curative treatment. Overall survival and PFS were measured from the start of treatment and estimated with the Kaplan-Meier estimator. Hazard ratios and 95% confidence intervals were estimated with Cox proportional hazard regression. At a median follow-up of 3.6 years (interquartile range 1.86-6.07 years), 1177 patients were included (59% HPV+). In the adjusted analysis for the HPV+ and HPV- patient population, TTI influenced overall survival and PFS, most evident in the HPV- group, where TTI >60 days statistically significantly influenced overall survival but not PFS (overall survival: hazard ratio 1.60; 95% confidence interval 1.04-2.45; PFS: hazard ratio 1.46; 95% confidence interval 0.96-2.22). For patients with a TTI >60 days in the HPV+ group, TTI affected overall survival and PFS similarly, with slightly lower hazard ratio estimates of 1.44 (95% confidence interval 0.83-2.51) and 1.15 (95% confidence interval 0.70-1.88), respectively. For patients treated for a HPV+ or HPV- OPSCC, TTI affects outcome, with the strongest effect for overall survival among HPV- patients. Reducing TTI is an important tool to improve the prognosis. Copyright © 2018. Published by Elsevier Ltd.

Estimating the Post-Mortem Interval of skeletonized remains: The use of Infrared spectroscopy and Raman spectro-microscopy

NASA Astrophysics Data System (ADS)

Creagh, Dudley; Cameron, Alyce

2017-08-01

When skeletonized remains are found it becomes a police task to determine to identify the body and establish the cause of death. It assists investigators if the Post-Mortem Interval (PMI) can be established. Hitherto no reliable qualitative method of estimating the PMI has been found. A quantitative method has yet to be developed. This paper shows that IR spectroscopy and Raman microscopy have the potential to form the basis of a quantitative method.

Procedures for estimating confidence intervals for selected method performance parameters.

PubMed

McClure, F D; Lee, J K

2001-01-01

Procedures for estimating confidence intervals (CIs) for the repeatability variance (sigmar2), reproducibility variance (sigmaR2 = sigmaL2 + sigmar2), laboratory component (sigmaL2), and their corresponding standard deviations sigmar, sigmaR, and sigmaL, respectively, are presented. In addition, CIs for the ratio of the repeatability component to the reproducibility variance (sigmar2/sigmaR2) and the ratio of the laboratory component to the reproducibility variance (sigmaL2/sigmaR2) are also presented.

2014 Gulf of Mexico Hypoxia Forecast

USGS Publications Warehouse

Scavia, Donald; Evans, Mary Anne; Obenour, Dan

2014-01-01

The Gulf of Mexico annual summer hypoxia forecasts are based on average May total nitrogen loads from the Mississippi River basin for that year. The load estimate, recently released by USGS, is 4,761 metric tons per day. Based on that estimate, we predict the area of this summer’s hypoxic zone to be 14,000 square kilometers (95% credible interval, 8,000 to 20,000) – an “average year”. Our forecast hypoxic volume is 50 km3 (95% credible interval, 20 to 77).

Trends and racial and ethnic disparities in the prevalence of pregestational type 1 and type 2 diabetes in Northern California: 1996-2014.

PubMed

Peng, Tiffany Y; Ehrlich, Samantha F; Crites, Yvonne; Kitzmiller, John L; Kuzniewicz, Michael W; Hedderson, Monique M; Ferrara, Assiamira

2017-02-01

Despite concern for adverse perinatal outcomes in women with diabetes mellitus before pregnancy, recent data on the prevalence of pregestational type 1 and type 2 diabetes mellitus in the United States are lacking. The purpose of this study was to estimate changes in the prevalence of overall pregestational diabetes mellitus (all types) and pregestational type 1 and type 2 diabetes mellitus and to estimate whether changes varied by race-ethnicity from 1996-2014. We conducted a cohort study among 655,428 pregnancies at a Northern California integrated health delivery system from 1996-2014. Logistic regression analyses provided estimates of prevalence and trends. The age-adjusted prevalence (per 100 deliveries) of overall pregestational diabetes mellitus increased from 1996-1999 to 2012-2014 (from 0.58 [95% confidence interval, 0.54-0.63] to 1.06 [95% confidence interval, 1.00-1.12]; P trend <.0001). Significant increases occurred in all racial-ethnic groups; the largest relative increase was among Hispanic women (121.8% [95% confidence interval, 84.4-166.7]); the smallest relative increase was among non-Hispanic white women (49.6% [95% confidence interval, 27.5-75.4]). The age-adjusted prevalence of pregestational type 1 and type 2 diabetes mellitus increased from 0.14 (95% confidence interval, 0.12-0.16) to 0.23 (95% confidence interval, 0.21-0.27; P trend <.0001) and from 0.42 (95% confidence interval, 0.38-0.46) to 0.78 (95% confidence interval, 0.73-0.83; P trend <.0001), respectively. The greatest relative increase in the prevalence of type 1 diabetes mellitus was in non-Hispanic white women (118.4% [95% confidence interval, 70.0-180.5]), who had the lowest increases in the prevalence of type 2 diabetes mellitus (13.6% [95% confidence interval, -8.0 to 40.1]). The greatest relative increase in the prevalence of type 2 diabetes mellitus was in Hispanic women (125.2% [95% confidence interval, 84.8-174.4]), followed by African American women (102.0% [95% confidence interval, 38.3-194.3]) and Asian women (93.3% [95% confidence interval, 48.9-150.9]). The prevalence of overall pregestational diabetes mellitus and pregestational type 1 and type 2 diabetes mellitus increased from 1996-1999 to 2012-2014 and racial-ethnic disparities were observed, possibly because of differing prevalence of maternal obesity. Targeted prevention efforts, preconception care, and disease management strategies are needed to reduce the burden of diabetes mellitus and its sequelae. Copyright © 2016 Elsevier Inc. All rights reserved.

Is high-intensity interval training a time-efficient exercise strategy to improve health and fitness?

PubMed

Gillen, Jenna B; Gibala, Martin J

2014-03-01

Growing research suggests that high-intensity interval training (HIIT) is a time-efficient exercise strategy to improve cardiorespiratory and metabolic health. "All out" HIIT models such as Wingate-type exercise are particularly effective, but this type of training may not be safe, tolerable or practical for many individuals. Recent studies, however, have revealed the potential for other models of HIIT, which may be more feasible but are still time-efficient, to stimulate adaptations similar to more demanding low-volume HIIT models and high-volume endurance-type training. As little as 3 HIIT sessions per week, involving ≤10 min of intense exercise within a time commitment of ≤30 min per session, including warm-up, recovery between intervals and cool down, has been shown to improve aerobic capacity, skeletal muscle oxidative capacity, exercise tolerance and markers of disease risk after only a few weeks in both healthy individuals and people with cardiometabolic disorders. Additional research is warranted, as studies conducted have been relatively short-term, with a limited number of measurements performed on small groups of subjects. However, given that "lack of time" remains one of the most commonly cited barriers to regular exercise participation, low-volume HIIT is a time-efficient exercise strategy that warrants consideration by health practitioners and fitness professionals.

High-Intensity Interval Training for Improving Postprandial Hyperglycemia

ERIC Educational Resources Information Center

Little, Jonathan P.; Francois, Monique E.

2014-01-01

High-intensity interval training (HIIT) has garnered attention in recent years as a time-efficient exercise option for improving cardiovascular and metabolic health. New research demonstrates that HIIT may be particularly effective for improving postprandial hyperglycemia in individuals with, or at risk for, type 2 diabetes (T2D). These findings…

Studies on the estimation of the postmortem interval. 3. Rigor mortis (author's transl).

PubMed

Suzutani, T; Ishibashi, H; Takatori, T

1978-11-01

The authors have devised a method for classifying rigor mortis into 10 types based on its appearance and strength in various parts of a cadaver. By applying the method to the findings of 436 cadavers which were subjected to medico-legal autopsies in our laboratory during the last 10 years, it has been demonstrated that the classifying method is effective for analyzing the phenomenon of onset, persistence and disappearance of rigor mortis statistically. The investigation of the relationship between each type of rigor mortis and the postmortem interval has demonstrated that rigor mortis may be utilized as a basis for estimating the postmortem interval but the values have greater deviation than those described in current textbooks.

A new scheme for processing noisy startracker measurements in spacecraft attitude determination systems

NASA Technical Reports Server (NTRS)

Polites, M. E.

1991-01-01

This paper presents a new approach to processing noisy startracker measurements in spacecraft attitude determination systems. It takes N measurements in each T-second interval and combines them to produce tracker outputs that are estimates of star position at the end of each interval, when the tracker outputs become available. This is an improvement over the standard method, measurement averaging, which generates outputs that are estimates of the average position of the star over each interval. This new scheme is superior to measurement averaging when the spacecraft has some rotation rate as in target tracking or earth pointing. Also, it is not just limited to startracker, but has potential application wherever measurement averaging of sensor outputs is used.

Polar bears from space: assessing satellite imagery as a tool to track Arctic wildlife.

PubMed

Stapleton, Seth; LaRue, Michelle; Lecomte, Nicolas; Atkinson, Stephen; Garshelis, David; Porter, Claire; Atwood, Todd

2014-01-01

Development of efficient techniques for monitoring wildlife is a priority in the Arctic, where the impacts of climate change are acute and remoteness and logistical constraints hinder access. We evaluated high resolution satellite imagery as a tool to track the distribution and abundance of polar bears. We examined satellite images of a small island in Foxe Basin, Canada, occupied by a high density of bears during the summer ice-free season. Bears were distinguished from other light-colored spots by comparing images collected on different dates. A sample of ground-truthed points demonstrated that we accurately classified bears. Independent observers reviewed images and a population estimate was obtained using mark-recapture models. This estimate (N: 94; 95% Confidence Interval: 92-105) was remarkably similar to an abundance estimate derived from a line transect aerial survey conducted a few days earlier (N: 102; 95% CI: 69-152). Our findings suggest that satellite imagery is a promising tool for monitoring polar bears on land, with implications for use with other Arctic wildlife. Large scale applications may require development of automated detection processes to expedite review and analysis. Future research should assess the utility of multi-spectral imagery and examine sites with different environmental characteristics.

A two-stage Monte Carlo approach to the expression of uncertainty with finite sample sizes.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crowder, Stephen Vernon; Moyer, Robert D.

2005-05-01

Proposed supplement I to the GUM outlines a 'propagation of distributions' approach to deriving the distribution of a measurand for any non-linear function and for any set of random inputs. The supplement's proposed Monte Carlo approach assumes that the distributions of the random inputs are known exactly. This implies that the sample sizes are effectively infinite. In this case, the mean of the measurand can be determined precisely using a large number of Monte Carlo simulations. In practice, however, the distributions of the inputs will rarely be known exactly, but must be estimated using possibly small samples. If these approximatedmore » distributions are treated as exact, the uncertainty in estimating the mean is not properly taken into account. In this paper, we propose a two-stage Monte Carlo procedure that explicitly takes into account the finite sample sizes used to estimate parameters of the input distributions. We will illustrate the approach with a case study involving the efficiency of a thermistor mount power sensor. The performance of the proposed approach will be compared to the standard GUM approach for finite samples using simple non-linear measurement equations. We will investigate performance in terms of coverage probabilities of derived confidence intervals.« less

Polar Bears from Space: Assessing Satellite Imagery as a Tool to Track Arctic Wildlife

PubMed Central

Stapleton, Seth; LaRue, Michelle; Lecomte, Nicolas; Atkinson, Stephen; Garshelis, David; Porter, Claire; Atwood, Todd

2014-01-01

Development of efficient techniques for monitoring wildlife is a priority in the Arctic, where the impacts of climate change are acute and remoteness and logistical constraints hinder access. We evaluated high resolution satellite imagery as a tool to track the distribution and abundance of polar bears. We examined satellite images of a small island in Foxe Basin, Canada, occupied by a high density of bears during the summer ice-free season. Bears were distinguished from other light-colored spots by comparing images collected on different dates. A sample of ground-truthed points demonstrated that we accurately classified bears. Independent observers reviewed images and a population estimate was obtained using mark–recapture models. This estimate (: 94; 95% Confidence Interval: 92–105) was remarkably similar to an abundance estimate derived from a line transect aerial survey conducted a few days earlier (: 102; 95% CI: 69–152). Our findings suggest that satellite imagery is a promising tool for monitoring polar bears on land, with implications for use with other Arctic wildlife. Large scale applications may require development of automated detection processes to expedite review and analysis. Future research should assess the utility of multi-spectral imagery and examine sites with different environmental characteristics. PMID:25006979

Resolving the Antarctic contribution to sea-level rise: a hierarchical modelling framework.

PubMed

Zammit-Mangion, Andrew; Rougier, Jonathan; Bamber, Jonathan; Schön, Nana

2014-06-01

Determining the Antarctic contribution to sea-level rise from observational data is a complex problem. The number of physical processes involved (such as ice dynamics and surface climate) exceeds the number of observables, some of which have very poor spatial definition. This has led, in general, to solutions that utilise strong prior assumptions or physically based deterministic models to simplify the problem. Here, we present a new approach for estimating the Antarctic contribution, which only incorporates descriptive aspects of the physically based models in the analysis and in a statistical manner. By combining physical insights with modern spatial statistical modelling techniques, we are able to provide probability distributions on all processes deemed to play a role in both the observed data and the contribution to sea-level rise. Specifically, we use stochastic partial differential equations and their relation to geostatistical fields to capture our physical understanding and employ a Gaussian Markov random field approach for efficient computation. The method, an instantiation of Bayesian hierarchical modelling, naturally incorporates uncertainty in order to reveal credible intervals on all estimated quantities. The estimated sea-level rise contribution using this approach corroborates those found using a statistically independent method. © 2013 The Authors. Environmetrics Published by John Wiley & Sons, Ltd.

Adaptive pre-specification in randomized trials with and without pair-matching.

PubMed

Balzer, Laura B; van der Laan, Mark J; Petersen, Maya L

2016-11-10

In randomized trials, adjustment for measured covariates during the analysis can reduce variance and increase power. To avoid misleading inference, the analysis plan must be pre-specified. However, it is often unclear a priori which baseline covariates (if any) should be adjusted for in the analysis. Consider, for example, the Sustainable East Africa Research in Community Health (SEARCH) trial for HIV prevention and treatment. There are 16 matched pairs of communities and many potential adjustment variables, including region, HIV prevalence, male circumcision coverage, and measures of community-level viral load. In this paper, we propose a rigorous procedure to data-adaptively select the adjustment set, which maximizes the efficiency of the analysis. Specifically, we use cross-validation to select from a pre-specified library the candidate targeted maximum likelihood estimator (TMLE) that minimizes the estimated variance. For further gains in precision, we also propose a collaborative procedure for estimating the known exposure mechanism. Our small sample simulations demonstrate the promise of the methodology to maximize study power, while maintaining nominal confidence interval coverage. We show how our procedure can be tailored to the scientific question (intervention effect for the study sample vs. for the target population) and study design (pair-matched or not). Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Compositional cokriging for mapping the probability risk of groundwater contamination by nitrates.

PubMed

Pardo-Igúzquiza, Eulogio; Chica-Olmo, Mario; Luque-Espinar, Juan A; Rodríguez-Galiano, Víctor

2015-11-01

Contamination by nitrates is an important cause of groundwater pollution and represents a potential risk to human health. Management decisions must be made using probability maps that assess the nitrate concentration potential of exceeding regulatory thresholds. However these maps are obtained with only a small number of sparse monitoring locations where the nitrate concentrations have been measured. It is therefore of great interest to have an efficient methodology for obtaining those probability maps. In this paper, we make use of the fact that the discrete probability density function is a compositional variable. The spatial discrete probability density function is estimated by compositional cokriging. There are several advantages in using this approach: (i) problems of classical indicator cokriging, like estimates outside the interval (0,1) and order relations, are avoided; (ii) secondary variables (e.g. aquifer parameters) can be included in the estimation of the probability maps; (iii) uncertainty maps of the probability maps can be obtained; (iv) finally there are modelling advantages because the variograms and cross-variograms of real variables that do not have the restrictions of indicator variograms and indicator cross-variograms. The methodology was applied to the Vega de Granada aquifer in Southern Spain and the advantages of the compositional cokriging approach were demonstrated. Copyright © 2015 Elsevier B.V. All rights reserved.

Resolving the Antarctic contribution to sea-level rise: a hierarchical modelling framework†

PubMed Central

Zammit-Mangion, Andrew; Rougier, Jonathan; Bamber, Jonathan; Schön, Nana

2014-01-01

Determining the Antarctic contribution to sea-level rise from observational data is a complex problem. The number of physical processes involved (such as ice dynamics and surface climate) exceeds the number of observables, some of which have very poor spatial definition. This has led, in general, to solutions that utilise strong prior assumptions or physically based deterministic models to simplify the problem. Here, we present a new approach for estimating the Antarctic contribution, which only incorporates descriptive aspects of the physically based models in the analysis and in a statistical manner. By combining physical insights with modern spatial statistical modelling techniques, we are able to provide probability distributions on all processes deemed to play a role in both the observed data and the contribution to sea-level rise. Specifically, we use stochastic partial differential equations and their relation to geostatistical fields to capture our physical understanding and employ a Gaussian Markov random field approach for efficient computation. The method, an instantiation of Bayesian hierarchical modelling, naturally incorporates uncertainty in order to reveal credible intervals on all estimated quantities. The estimated sea-level rise contribution using this approach corroborates those found using a statistically independent method. © 2013 The Authors. Environmetrics Published by John Wiley & Sons, Ltd. PMID:25505370

Adaptive Square-Root Cubature-Quadrature Kalman Particle Filter for satellite attitude determination using vector observations

NASA Astrophysics Data System (ADS)

Kiani, Maryam; Pourtakdoust, Seid H.

2014-12-01

A novel algorithm is presented in this study for estimation of spacecraft's attitudes and angular rates from vector observations. In this regard, a new cubature-quadrature particle filter (CQPF) is initially developed that uses the Square-Root Cubature-Quadrature Kalman Filter (SR-CQKF) to generate the importance proposal distribution. The developed CQPF scheme avoids the basic limitation of particle filter (PF) with regards to counting the new measurements. Subsequently, CQPF is enhanced to adjust the sample size at every time step utilizing the idea of confidence intervals, thus improving the efficiency and accuracy of the newly proposed adaptive CQPF (ACQPF). In addition, application of the q-method for filter initialization has intensified the computation burden as well. The current study also applies ACQPF to the problem of attitude estimation of a low Earth orbit (LEO) satellite. For this purpose, the undertaken satellite is equipped with a three-axis magnetometer (TAM) as well as a sun sensor pack that provide noisy geomagnetic field data and Sun direction measurements, respectively. The results and performance of the proposed filter are investigated and compared with those of the extended Kalman filter (EKF) and the standard particle filter (PF) utilizing a Monte Carlo simulation. The comparison demonstrates the viability and the accuracy of the proposed nonlinear estimator.

Polar bears from space: Assessing satellite imagery as a tool to track Arctic wildlife

USGS Publications Warehouse

Stapleton, Seth P.; LaRue, Michelle A.; Lecomte, Nicolas; Atkinson, Stephen N.; Garshelis, David L.; Porter, Claire; Atwood, Todd C.

2014-01-01

Development of efficient techniques for monitoring wildlife is a priority in the Arctic, where the impacts of climate change are acute and remoteness and logistical constraints hinder access. We evaluated high resolution satellite imagery as a tool to track the distribution and abundance of polar bears. We examined satellite images of a small island in Foxe Basin, Canada, occupied by a high density of bears during the summer ice-free season. Bears were distinguished from other light-colored spots by comparing images collected on different dates. A sample of ground-truthed points demonstrated that we accurately classified bears. Independent observers reviewed images and a population estimate was obtained using mark- recapture models. This estimate (N: 94; 95% Confidence Interval: 92-105) was remarkably similar to an abundance estimate derived from a line transect aerial survey conducted a few days earlier (N: 102; 95% CI: 69-152). Our findings suggest that satellite imagery is a promising tool for monitoring polar bears on land, with implications for use with other Arctic wildlife. Large scale applications may require development of automated detection processes to expedite review and analysis. Future research should assess the utility of multi-spectral imagery and examine sites with different environmental characteristics.

Characteristics of the April 2007 Flood at 10 Streamflow-Gaging Stations in Massachusetts

USGS Publications Warehouse

Zarriello, Phillip J.; Carlson, Carl S.

2009-01-01

A large 'nor'easter' storm on April 15-18, 2007, brought heavy rains to the southern New England region that, coupled with normal seasonal high flows and associated wet soil-moisture conditions, caused extensive flooding in many parts of Massachusetts and neighboring states. To characterize the magnitude of the April 2007 flood, a peak-flow frequency analysis was undertaken at 10 selected streamflow-gaging stations in Massachusetts to determine the magnitude of flood flows at 5-, 10-, 25-, 50-, 100-, 200-, and 500-year return intervals. The magnitude of flood flows at various return intervals were determined from the logarithms of the annual peaks fit to a Pearson Type III probability distribution. Analysis included augmenting the station record with longer-term records from one or more nearby stations to provide a common period of comparison that includes notable floods in 1936, 1938, and 1955. The April 2007 peak flow was among the highest recorded or estimated since 1936, often ranking between the 3d and 5th highest peak for that period. In general, the peak-flow frequency analysis indicates the April 2007 peak flow has an estimated return interval between 25 and 50 years; at stations in the northeastern and central areas of the state, the storm was less severe resulting in flows with return intervals of about 5 and 10 years, respectively. At Merrimack River at Lowell, the April 2007 peak flow approached a 100-year return interval that was computed from post-flood control records and the 1936 and 1938 peak flows adjusted for flood control. In general, the magnitude of flood flow for a given return interval computed from the streamflow-gaging station period-of-record was greater than those used to calculate flood profiles in various community flood-insurance studies. In addition, the magnitude of the updated flood flow and current (2008) stage-discharge relation at a given streamflow-gaging station often produced a flood stage that was considerably different than the flood stage indicated in the flood-insurance study flood profile at that station. Equations for estimating the flow magnitudes for 5-, 10-, 25-, 50-, 100-, 200-, and 500-year floods were developed from the relation of the magnitude of flood flows to drainage area calculated from the six streamflow-gaging stations with the longest unaltered record. These equations produced a more conservative estimate of flood flows (higher discharges) than the existing regional equations for estimating flood flows at ungaged rivers in Massachusetts. Large differences in the magnitude of flood flows for various return intervals determined in this study compared to results from existing regional equations and flood insurance studies indicate a need for updating regional analyses and equations for estimating the expected magnitude of flood flows in Massachusetts.

Gradient boosting machine for modeling the energy consumption of commercial buildings

DOE PAGES

Touzani, Samir; Granderson, Jessica; Fernandes, Samuel

2017-11-26

Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradientmore » boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model's performance. The results show that using the gradient boosting machine model improved the R-squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.« less

Gradient boosting machine for modeling the energy consumption of commercial buildings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Touzani, Samir; Granderson, Jessica; Fernandes, Samuel

Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradientmore » boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model's performance. The results show that using the gradient boosting machine model improved the R-squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.« less

An AIS-based approach to calculate atmospheric emissions from the UK fishing fleet

NASA Astrophysics Data System (ADS)

Coello, Jonathan; Williams, Ian; Hudson, Dominic A.; Kemp, Simon

2015-08-01

The fishing industry is heavily reliant on the use of fossil fuel and emits large quantities of greenhouse gases and other atmospheric pollutants. Methods used to calculate fishing vessel emissions inventories have traditionally utilised estimates of fuel efficiency per unit of catch. These methods have weaknesses because they do not easily allow temporal and geographical allocation of emissions. A large proportion of fishing and other small commercial vessels are also omitted from global shipping emissions inventories such as the International Maritime Organisation's Greenhouse Gas Studies. This paper demonstrates an activity-based methodology for the production of temporally- and spatially-resolved emissions inventories using data produced by Automatic Identification Systems (AIS). The methodology addresses the issue of how to use AIS data for fleets where not all vessels use AIS technology and how to assign engine load when vessels are towing trawling or dredging gear. The results of this are compared to a fuel-based methodology using publicly available European Commission fisheries data on fuel efficiency and annual catch. The results show relatively good agreement between the two methodologies, with an estimate of 295.7 kilotons of fuel used and 914.4 kilotons of carbon dioxide emitted between May 2012 and May 2013 using the activity-based methodology. Different methods of calculating speed using AIS data are also compared. The results indicate that using the speed data contained directly in the AIS data is preferable to calculating speed from the distance and time interval between consecutive AIS data points.

Possibility-based robust design optimization for the structural-acoustic system with fuzzy parameters

NASA Astrophysics Data System (ADS)

Yin, Hui; Yu, Dejie; Yin, Shengwen; Xia, Baizhan

2018-03-01

The conventional engineering optimization problems considering uncertainties are based on the probabilistic model. However, the probabilistic model may be unavailable because of the lack of sufficient objective information to construct the precise probability distribution of uncertainties. This paper proposes a possibility-based robust design optimization (PBRDO) framework for the uncertain structural-acoustic system based on the fuzzy set model, which can be constructed by expert opinions. The objective of robust design is to optimize the expectation and variability of system performance with respect to uncertainties simultaneously. In the proposed PBRDO, the entropy of the fuzzy system response is used as the variability index; the weighted sum of the entropy and expectation of the fuzzy response is used as the objective function, and the constraints are established in the possibility context. The computations for the constraints and objective function of PBRDO are a triple-loop and a double-loop nested problem, respectively, whose computational costs are considerable. To improve the computational efficiency, the target performance approach is introduced to transform the calculation of the constraints into a double-loop nested problem. To further improve the computational efficiency, a Chebyshev fuzzy method (CFM) based on the Chebyshev polynomials is proposed to estimate the objective function, and the Chebyshev interval method (CIM) is introduced to estimate the constraints, thereby the optimization problem is transformed into a single-loop one. Numerical results on a shell structural-acoustic system verify the effectiveness and feasibility of the proposed methods.

Methods for estimating selected low-flow frequency statistics and harmonic mean flows for streams in Iowa

USGS Publications Warehouse

Eash, David A.; Barnes, Kimberlee K.

2017-01-01

A statewide study was conducted to develop regression equations for estimating six selected low-flow frequency statistics and harmonic mean flows for ungaged stream sites in Iowa. The estimation equations developed for the six low-flow frequency statistics include: the annual 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years, the annual 30-day mean low flow for a recurrence interval of 5 years, and the seasonal (October 1 through December 31) 1- and 7-day mean low flows for a recurrence interval of 10 years. Estimation equations also were developed for the harmonic-mean-flow statistic. Estimates of these seven selected statistics are provided for 208 U.S. Geological Survey continuous-record streamgages using data through September 30, 2006. The study area comprises streamgages located within Iowa and 50 miles beyond the State's borders. Because trend analyses indicated statistically significant positive trends when considering the entire period of record for the majority of the streamgages, the longest, most recent period of record without a significant trend was determined for each streamgage for use in the study. The median number of years of record used to compute each of these seven selected statistics was 35. Geographic information system software was used to measure 54 selected basin characteristics for each streamgage. Following the removal of two streamgages from the initial data set, data collected for 206 streamgages were compiled to investigate three approaches for regionalization of the seven selected statistics. Regionalization, a process using statistical regression analysis, provides a relation for efficiently transferring information from a group of streamgages in a region to ungaged sites in the region. The three regionalization approaches tested included statewide, regional, and region-of-influence regressions. For the regional regression, the study area was divided into three low-flow regions on the basis of hydrologic characteristics, landform regions, and soil regions. A comparison of root mean square errors and average standard errors of prediction for the statewide, regional, and region-of-influence regressions determined that the regional regression provided the best estimates of the seven selected statistics at ungaged sites in Iowa. Because a significant number of streams in Iowa reach zero flow as their minimum flow during low-flow years, four different types of regression analyses were used: left-censored, logistic, generalized-least-squares, and weighted-least-squares regression. A total of 192 streamgages were included in the development of 27 regression equations for the three low-flow regions. For the northeast and northwest regions, a censoring threshold was used to develop 12 left-censored regression equations to estimate the 6 low-flow frequency statistics for each region. For the southern region a total of 12 regression equations were developed; 6 logistic regression equations were developed to estimate the probability of zero flow for the 6 low-flow frequency statistics and 6 generalized least-squares regression equations were developed to estimate the 6 low-flow frequency statistics, if nonzero flow is estimated first by use of the logistic equations. A weighted-least-squares regression equation was developed for each region to estimate the harmonic-mean-flow statistic. Average standard errors of estimate for the left-censored equations for the northeast region range from 64.7 to 88.1 percent and for the northwest region range from 85.8 to 111.8 percent. Misclassification percentages for the logistic equations for the southern region range from 5.6 to 14.0 percent. Average standard errors of prediction for generalized least-squares equations for the southern region range from 71.7 to 98.9 percent and pseudo coefficients of determination for the generalized-least-squares equations range from 87.7 to 91.8 percent. Average standard errors of prediction for weighted-least-squares equations developed for estimating the harmonic-mean-flow statistic for each of the three regions range from 66.4 to 80.4 percent. The regression equations are applicable only to stream sites in Iowa with low flows not significantly affected by regulation, diversion, or urbanization and with basin characteristics within the range of those used to develop the equations. If the equations are used at ungaged sites on regulated streams, or on streams affected by water-supply and agricultural withdrawals, then the estimates will need to be adjusted by the amount of regulation or withdrawal to estimate the actual flow conditions if that is of interest. Caution is advised when applying the equations for basins with characteristics near the applicable limits of the equations and for basins located in karst topography. A test of two drainage-area ratio methods using 31 pairs of streamgages, for the annual 7-day mean low-flow statistic for a recurrence interval of 10 years, indicates a weighted drainage-area ratio method provides better estimates than regional regression equations for an ungaged site on a gaged stream in Iowa when the drainage-area ratio is between 0.5 and 1.4. These regression equations will be implemented within the U.S. Geological Survey StreamStats web-based geographic-information-system tool. StreamStats allows users to click on any ungaged site on a river and compute estimates of the seven selected statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged sites also are provided. StreamStats also allows users to click on any streamgage in Iowa and estimates computed for these seven selected statistics are provided for the streamgage.

Estimating and testing interactions when explanatory variables are subject to non-classical measurement error.

PubMed

Murad, Havi; Kipnis, Victor; Freedman, Laurence S

2016-10-01

Assessing interactions in linear regression models when covariates have measurement error (ME) is complex.We previously described regression calibration (RC) methods that yield consistent estimators and standard errors for interaction coefficients of normally distributed covariates having classical ME. Here we extend normal based RC (NBRC) and linear RC (LRC) methods to a non-classical ME model, and describe more efficient versions that combine estimates from the main study and internal sub-study. We apply these methods to data from the Observing Protein and Energy Nutrition (OPEN) study. Using simulations we show that (i) for normally distributed covariates efficient NBRC and LRC were nearly unbiased and performed well with sub-study size ≥200; (ii) efficient NBRC had lower MSE than efficient LRC; (iii) the naïve test for a single interaction had type I error probability close to the nominal significance level, whereas efficient NBRC and LRC were slightly anti-conservative but more powerful; (iv) for markedly non-normal covariates, efficient LRC yielded less biased estimators with smaller variance than efficient NBRC. Our simulations suggest that it is preferable to use: (i) efficient NBRC for estimating and testing interaction effects of normally distributed covariates and (ii) efficient LRC for estimating and testing interactions for markedly non-normal covariates. © The Author(s) 2013.

Implementation Of Fuzzy Approach To Improve Time Estimation [Case Study Of A Thermal Power Plant Is Considered

NASA Astrophysics Data System (ADS)

Pradhan, Moumita; Pradhan, Dinesh; Bandyopadhyay, G.

2010-10-01

Fuzzy System has demonstrated their ability to solve different kinds of problem in various application domains. There is an increasing interest to apply fuzzy concept to improve tasks of any system. Here case study of a thermal power plant is considered. Existing time estimation represents time to complete tasks. Applying fuzzy linear approach it becomes clear that after each confidence level least time is taken to complete tasks. As time schedule is less than less amount of cost is needed. Objective of this paper is to show how one system becomes more efficient in applying Fuzzy Linear approach. In this paper we want to optimize the time estimation to perform all tasks in appropriate time schedules. For the case study, optimistic time (to), pessimistic time (tp), most likely time(tm) is considered as data collected from thermal power plant. These time estimates help to calculate expected time(te) which represents time to complete particular task to considering all happenings. Using project evaluation and review technique (PERT) and critical path method (CPM) concept critical path duration (CPD) of this project is calculated. This tells that the probability of fifty percent of the total tasks can be completed in fifty days. Using critical path duration and standard deviation of the critical path, total completion of project can be completed easily after applying normal distribution. Using trapezoidal rule from four time estimates (to, tm, tp, te), we can calculate defuzzyfied value of time estimates. For range of fuzzy, we consider four confidence interval level say 0.4, 0.6, 0.8,1. From our study, it is seen that time estimates at confidence level between 0.4 and 0.8 gives the better result compared to other confidence levels.

Large Sample Confidence Intervals for Item Response Theory Reliability Coefficients

ERIC Educational Resources Information Center

Andersson, Björn; Xin, Tao

2018-01-01

In applications of item response theory (IRT), an estimate of the reliability of the ability estimates or sum scores is often reported. However, analytical expressions for the standard errors of the estimators of the reliability coefficients are not available in the literature and therefore the variability associated with the estimated reliability…

How to Deal with Interval-Censored Data Practically while Assessing the Progression-Free Survival: A Step-by-Step Guide Using SAS and R Software.

PubMed

Dugué, Audrey Emmanuelle; Pulido, Marina; Chabaud, Sylvie; Belin, Lisa; Gal, Jocelyn

2016-12-01

We describe how to estimate progression-free survival while dealing with interval-censored data in the setting of clinical trials in oncology. Three procedures with SAS and R statistical software are described: one allowing for a nonparametric maximum likelihood estimation of the survival curve using the EM-ICM (Expectation and Maximization-Iterative Convex Minorant) algorithm as described by Wellner and Zhan in 1997; a sensitivity analysis procedure in which the progression time is assigned (i) at the midpoint, (ii) at the upper limit (reflecting the standard analysis when the progression time is assigned at the first radiologic exam showing progressive disease), or (iii) at the lower limit of the censoring interval; and finally, two multiple imputations are described considering a uniform or the nonparametric maximum likelihood estimation (NPMLE) distribution. Clin Cancer Res; 22(23); 5629-35. ©2016 AACR. ©2016 American Association for Cancer Research.

Estimation of desmosponge (Porifera, Demospongiae) larval settlement rates from short-term recruitment rates: Preliminary experiments

NASA Astrophysics Data System (ADS)

Zea, Sven

1992-09-01

During a study of the spatial and temporal patterns of desmosponge (Porifera, Demospongiae) recruitment on rocky and coral reef habitats of Santa Marta, Colombian Caribbean Sea, preliminary attempts were made to estimate actual settlement rates from short-term (1 to a few days) recruitment censuses. Short-term recruitment rates on black, acrylic plastic plates attached to open, non-cryptic substratum by anchor screws were low and variable (0 5 recruits/plate in 1 2 days, sets of n=5 10 plates), but reflected the depth and seasonal trends found using mid-term (1 to a few months) censusing intervals. Moreover, mortality of recruits during 1 2 day intervals was low (0 12%). Thus, short-term censusing intervals can be used to estimate actual settlement rates. To be able to make statistical comparisons, however, it is necessary to increase the number of recruits per census by pooling data of n plates per set, and to have more than one set per site or treatment.

Verification of a model for the detection of intrauterine growth restriction (IUGR) by receiver operating characteristics (ROC)

NASA Astrophysics Data System (ADS)

Liu, Pengbo; Mongelli, Max; Mondry, Adrian

2004-07-01

The purpose of this study is to verify by Receiver Operating Characteristics (ROC) a mathematical model supporting the hypothesis that IUGR can be diagnosed by estimating growth velocity. The ROC compare computerized simulation results with clinical data from 325 pregnant British women. Each patient had 6 consecutive ultrasound examinations for fetal abdominal circumference (fac). Customized and un-customized fetal weights were calculated according to Hadlock"s formula. IUGR was diagnosed by the clinical standard, i.e. estimated weight below the tenth percentile. Growth velocity was estimated by calculating the changes of fac (Dzfac/dt) at various time intervals from 3 to 10 weeks. Finally, ROC was used to compare the methods. At 3~4 weeks scan interval, the area under the ROC curve is 0.68 for customized data and 0.66 for the uncustomized data with 95% confidence interval. Comparison between simulation data and real pregnancies verified that the model is clinically acceptable.

Estimating means and variances: The comparative efficiency of composite and grab samples.

PubMed

Brumelle, S; Nemetz, P; Casey, D

1984-03-01

This paper compares the efficiencies of two sampling techniques for estimating a population mean and variance. One procedure, called grab sampling, consists of collecting and analyzing one sample per period. The second procedure, called composite sampling, collectsn samples per period which are then pooled and analyzed as a single sample. We review the well known fact that composite sampling provides a superior estimate of the mean. However, it is somewhat surprising that composite sampling does not always generate a more efficient estimate of the variance. For populations with platykurtic distributions, grab sampling gives a more efficient estimate of the variance, whereas composite sampling is better for leptokurtic distributions. These conditions on kurtosis can be related to peakedness and skewness. For example, a necessary condition for composite sampling to provide a more efficient estimate of the variance is that the population density function evaluated at the mean (i.e.f(μ)) be greater than[Formula: see text]. If[Formula: see text], then a grab sample is more efficient. In spite of this result, however, composite sampling does provide a smaller estimate of standard error than does grab sampling in the context of estimating population means.