A multi-valued neutrosophic qualitative flexible approach based on likelihood for multi-criteria decision-making problems

NASA Astrophysics Data System (ADS)

Peng, Juan-juan; Wang, Jian-qiang; Yang, Wu-E.

2017-01-01

In this paper, multi-criteria decision-making (MCDM) problems based on the qualitative flexible multiple criteria method (QUALIFLEX), in which the criteria values are expressed by multi-valued neutrosophic information, are investigated. First, multi-valued neutrosophic sets (MVNSs), which allow the truth-membership function, indeterminacy-membership function and falsity-membership function to have a set of crisp values between zero and one, are introduced. Then the likelihood of multi-valued neutrosophic number (MVNN) preference relations is defined and the corresponding properties are also discussed. Finally, an extended QUALIFLEX approach based on likelihood is explored to solve MCDM problems where the assessments of alternatives are in the form of MVNNs; furthermore an example is provided to illustrate the application of the proposed method, together with a comparison analysis.

Bayesian analysis of time-series data under case-crossover designs: posterior equivalence and inference.

PubMed

Li, Shi; Mukherjee, Bhramar; Batterman, Stuart; Ghosh, Malay

2013-12-01

Case-crossover designs are widely used to study short-term exposure effects on the risk of acute adverse health events. While the frequentist literature on this topic is vast, there is no Bayesian work in this general area. The contribution of this paper is twofold. First, the paper establishes Bayesian equivalence results that require characterization of the set of priors under which the posterior distributions of the risk ratio parameters based on a case-crossover and time-series analysis are identical. Second, the paper studies inferential issues under case-crossover designs in a Bayesian framework. Traditionally, a conditional logistic regression is used for inference on risk-ratio parameters in case-crossover studies. We consider instead a more general full likelihood-based approach which makes less restrictive assumptions on the risk functions. Formulation of a full likelihood leads to growth in the number of parameters proportional to the sample size. We propose a semi-parametric Bayesian approach using a Dirichlet process prior to handle the random nuisance parameters that appear in a full likelihood formulation. We carry out a simulation study to compare the Bayesian methods based on full and conditional likelihood with the standard frequentist approaches for case-crossover and time-series analysis. The proposed methods are illustrated through the Detroit Asthma Morbidity, Air Quality and Traffic study, which examines the association between acute asthma risk and ambient air pollutant concentrations. © 2013, The International Biometric Society.

Likelihood-Based Random-Effect Meta-Analysis of Binary Events.

PubMed

Amatya, Anup; Bhaumik, Dulal K; Normand, Sharon-Lise; Greenhouse, Joel; Kaizar, Eloise; Neelon, Brian; Gibbons, Robert D

2015-01-01

Meta-analysis has been used extensively for evaluation of efficacy and safety of medical interventions. Its advantages and utilities are well known. However, recent studies have raised questions about the accuracy of the commonly used moment-based meta-analytic methods in general and for rare binary outcomes in particular. The issue is further complicated for studies with heterogeneous effect sizes. Likelihood-based mixed-effects modeling provides an alternative to moment-based methods such as inverse-variance weighted fixed- and random-effects estimators. In this article, we compare and contrast different mixed-effect modeling strategies in the context of meta-analysis. Their performance in estimation and testing of overall effect and heterogeneity are evaluated when combining results from studies with a binary outcome. Models that allow heterogeneity in both baseline rate and treatment effect across studies have low type I and type II error rates, and their estimates are the least biased among the models considered.

New method to incorporate Type B uncertainty into least-squares procedures in radionuclide metrology.

PubMed

Han, Jubong; Lee, K B; Lee, Jong-Man; Park, Tae Soon; Oh, J S; Oh, Pil-Jei

2016-03-01

We discuss a new method to incorporate Type B uncertainty into least-squares procedures. The new method is based on an extension of the likelihood function from which a conventional least-squares function is derived. The extended likelihood function is the product of the original likelihood function with additional PDFs (Probability Density Functions) that characterize the Type B uncertainties. The PDFs are considered to describe one's incomplete knowledge on correction factors being called nuisance parameters. We use the extended likelihood function to make point and interval estimations of parameters in the basically same way as the least-squares function used in the conventional least-squares method is derived. Since the nuisance parameters are not of interest and should be prevented from appearing in the final result, we eliminate such nuisance parameters by using the profile likelihood. As an example, we present a case study for a linear regression analysis with a common component of Type B uncertainty. In this example we compare the analysis results obtained from using our procedure with those from conventional methods. Copyright © 2015. Published by Elsevier Ltd.

Fuzzy multinomial logistic regression analysis: A multi-objective programming approach

NASA Astrophysics Data System (ADS)

Abdalla, Hesham A.; El-Sayed, Amany A.; Hamed, Ramadan

2017-05-01

Parameter estimation for multinomial logistic regression is usually based on maximizing the likelihood function. For large well-balanced datasets, Maximum Likelihood (ML) estimation is a satisfactory approach. Unfortunately, ML can fail completely or at least produce poor results in terms of estimated probabilities and confidence intervals of parameters, specially for small datasets. In this study, a new approach based on fuzzy concepts is proposed to estimate parameters of the multinomial logistic regression. The study assumes that the parameters of multinomial logistic regression are fuzzy. Based on the extension principle stated by Zadeh and Bárdossy's proposition, a multi-objective programming approach is suggested to estimate these fuzzy parameters. A simulation study is used to evaluate the performance of the new approach versus Maximum likelihood (ML) approach. Results show that the new proposed model outperforms ML in cases of small datasets.

Robust Methods for Moderation Analysis with a Two-Level Regression Model.

PubMed

Yang, Miao; Yuan, Ke-Hai

2016-01-01

Moderation analysis has many applications in social sciences. Most widely used estimation methods for moderation analysis assume that errors are normally distributed and homoscedastic. When these assumptions are not met, the results from a classical moderation analysis can be misleading. For more reliable moderation analysis, this article proposes two robust methods with a two-level regression model when the predictors do not contain measurement error. One method is based on maximum likelihood with Student's t distribution and the other is based on M-estimators with Huber-type weights. An algorithm for obtaining the robust estimators is developed. Consistent estimates of standard errors of the robust estimators are provided. The robust approaches are compared against normal-distribution-based maximum likelihood (NML) with respect to power and accuracy of parameter estimates through a simulation study. Results show that the robust approaches outperform NML under various distributional conditions. Application of the robust methods is illustrated through a real data example. An R program is developed and documented to facilitate the application of the robust methods.

An Elaboration Likelihood Model Based Longitudinal Analysis of Attitude Change during the Process of IT Acceptance via Education Program

ERIC Educational Resources Information Center

Lee, Woong-Kyu

2012-01-01

The principal objective of this study was to gain insight into attitude changes occurring during IT acceptance from the perspective of elaboration likelihood model (ELM). In particular, the primary target of this study was the process of IT acceptance through an education program. Although the Internet and computers are now quite ubiquitous, and…

Design of simplified maximum-likelihood receivers for multiuser CPM systems.

PubMed

Bing, Li; Bai, Baoming

2014-01-01

A class of simplified maximum-likelihood receivers designed for continuous phase modulation based multiuser systems is proposed. The presented receiver is built upon a front end employing mismatched filters and a maximum-likelihood detector defined in a low-dimensional signal space. The performance of the proposed receivers is analyzed and compared to some existing receivers. Some schemes are designed to implement the proposed receivers and to reveal the roles of different system parameters. Analysis and numerical results show that the proposed receivers can approach the optimum multiuser receivers with significantly (even exponentially in some cases) reduced complexity and marginal performance degradation.

Wald Sequential Probability Ratio Test for Analysis of Orbital Conjunction Data

NASA Technical Reports Server (NTRS)

Carpenter, J. Russell; Markley, F. Landis; Gold, Dara

2013-01-01

We propose a Wald Sequential Probability Ratio Test for analysis of commonly available predictions associated with spacecraft conjunctions. Such predictions generally consist of a relative state and relative state error covariance at the time of closest approach, under the assumption that prediction errors are Gaussian. We show that under these circumstances, the likelihood ratio of the Wald test reduces to an especially simple form, involving the current best estimate of collision probability, and a similar estimate of collision probability that is based on prior assumptions about the likelihood of collision.

Acceleration and sensitivity analysis of lattice kinetic Monte Carlo simulations using parallel processing and rate constant rescaling

NASA Astrophysics Data System (ADS)

Núñez, M.; Robie, T.; Vlachos, D. G.

2017-10-01

Kinetic Monte Carlo (KMC) simulation provides insights into catalytic reactions unobtainable with either experiments or mean-field microkinetic models. Sensitivity analysis of KMC models assesses the robustness of the predictions to parametric perturbations and identifies rate determining steps in a chemical reaction network. Stiffness in the chemical reaction network, a ubiquitous feature, demands lengthy run times for KMC models and renders efficient sensitivity analysis based on the likelihood ratio method unusable. We address the challenge of efficiently conducting KMC simulations and performing accurate sensitivity analysis in systems with unknown time scales by employing two acceleration techniques: rate constant rescaling and parallel processing. We develop statistical criteria that ensure sufficient sampling of non-equilibrium steady state conditions. Our approach provides the twofold benefit of accelerating the simulation itself and enabling likelihood ratio sensitivity analysis, which provides further speedup relative to finite difference sensitivity analysis. As a result, the likelihood ratio method can be applied to real chemistry. We apply our methodology to the water-gas shift reaction on Pt(111).

Neural Mechanisms for Integrating Prior Knowledge and Likelihood in Value-Based Probabilistic Inference

PubMed Central

Ting, Chih-Chung; Yu, Chia-Chen; Maloney, Laurence T.

2015-01-01

In Bayesian decision theory, knowledge about the probabilities of possible outcomes is captured by a prior distribution and a likelihood function. The prior reflects past knowledge and the likelihood summarizes current sensory information. The two combined (integrated) form a posterior distribution that allows estimation of the probability of different possible outcomes. In this study, we investigated the neural mechanisms underlying Bayesian integration using a novel lottery decision task in which both prior knowledge and likelihood information about reward probability were systematically manipulated on a trial-by-trial basis. Consistent with Bayesian integration, as sample size increased, subjects tended to weigh likelihood information more compared with prior information. Using fMRI in humans, we found that the medial prefrontal cortex (mPFC) correlated with the mean of the posterior distribution, a statistic that reflects the integration of prior knowledge and likelihood of reward probability. Subsequent analysis revealed that both prior and likelihood information were represented in mPFC and that the neural representations of prior and likelihood in mPFC reflected changes in the behaviorally estimated weights assigned to these different sources of information in response to changes in the environment. Together, these results establish the role of mPFC in prior-likelihood integration and highlight its involvement in representing and integrating these distinct sources of information. PMID:25632152

Meta-analysis: accuracy of rapid tests for malaria in travelers returning from endemic areas.

PubMed

Marx, Arthur; Pewsner, Daniel; Egger, Matthias; Nüesch, Reto; Bucher, Heiner C; Genton, Blaise; Hatz, Christoph; Jüni, Peter

2005-05-17

Microscopic diagnosis of malaria is unreliable outside specialized centers. Rapid tests have become available in recent years, but their accuracy has not been assessed systematically. To determine the accuracy of rapid diagnostic tests for ruling out malaria in nonimmune travelers returning from malaria-endemic areas. The authors searched MEDLINE, EMBASE, CAB Health, and CINAHL (1988 to September 2004); hand-searched conference proceedings; checked reference lists; and contacted experts and manufacturers. Diagnostic accuracy studies in nonimmune individuals with suspected malaria were included if they compared rapid tests with expert microscopic examination or polymerase chain reaction tests. Data on study and patient characteristics and results were extracted in duplicate. The main outcome was the likelihood ratio for a negative test result (negative likelihood ratio) for Plasmodium falciparum malaria. Likelihood ratios were combined by using random-effects meta-analysis, stratified by the antigen targeted (histidine-rich protein-2 [HRP-2] or parasite lactate dehydrogenase [LDH]) and by test generation. Nomograms of post-test probabilities were constructed. The authors included 21 studies and 5747 individuals. For P. falciparum, HRP-2-based tests were more accurate than parasite LDH-based tests: Negative likelihood ratios were 0.08 and 0.13, respectively (P = 0.019 for difference). Three-band HRP-2 tests had similar negative likelihood ratios but higher positive likelihood ratios compared with 2-band tests (34.7 vs. 98.5; P = 0.003). For P. vivax, negative likelihood ratios tended to be closer to 1.0 for HRP-2-based tests than for parasite LDH-based tests (0.24 vs. 0.13; P = 0.22), but analyses were based on a few heterogeneous studies. Negative likelihood ratios for the diagnosis of P. malariae or P. ovale were close to 1.0 for both types of tests. In febrile travelers returning from sub-Saharan Africa, the typical probability of P. falciparum malaria is estimated at 1.1% (95% CI, 0.6% to 1.9%) after a negative 3-band HRP-2 test result and 97% (CI, 92% to 99%) after a positive test result. Few studies evaluated 3-band HRP-2 tests. The evidence is also limited for species other than P. falciparum because of the few available studies and their more heterogeneous results. Further studies are needed to determine whether the use of rapid diagnostic tests improves outcomes in returning travelers with suspected malaria. Rapid malaria tests may be a useful diagnostic adjunct to microscopy in centers without major expertise in tropical medicine. Initial decisions on treatment initiation and choice of antimalarial drugs can be based on travel history and post-test probabilities after rapid testing. Expert microscopy is still required for species identification and confirmation.

Two models for evaluating landslide hazards

USGS Publications Warehouse

Davis, J.C.; Chung, C.-J.; Ohlmacher, G.C.

2006-01-01

Two alternative procedures for estimating landslide hazards were evaluated using data on topographic digital elevation models (DEMs) and bedrock lithologies in an area adjacent to the Missouri River in Atchison County, Kansas, USA. The two procedures are based on the likelihood ratio model but utilize different assumptions. The empirical likelihood ratio model is based on non-parametric empirical univariate frequency distribution functions under an assumption of conditional independence while the multivariate logistic discriminant model assumes that likelihood ratios can be expressed in terms of logistic functions. The relative hazards of occurrence of landslides were estimated by an empirical likelihood ratio model and by multivariate logistic discriminant analysis. Predictor variables consisted of grids containing topographic elevations, slope angles, and slope aspects calculated from a 30-m DEM. An integer grid of coded bedrock lithologies taken from digitized geologic maps was also used as a predictor variable. Both statistical models yield relative estimates in the form of the proportion of total map area predicted to already contain or to be the site of future landslides. The stabilities of estimates were checked by cross-validation of results from random subsamples, using each of the two procedures. Cell-by-cell comparisons of hazard maps made by the two models show that the two sets of estimates are virtually identical. This suggests that the empirical likelihood ratio and the logistic discriminant analysis models are robust with respect to the conditional independent assumption and the logistic function assumption, respectively, and that either model can be used successfully to evaluate landslide hazards. ?? 2006.

Likelihood analysis of spatial capture-recapture models for stratified or class structured populations

USGS Publications Warehouse

Royle, J. Andrew; Sutherland, Christopher S.; Fuller, Angela K.; Sun, Catherine C.

2015-01-01

We develop a likelihood analysis framework for fitting spatial capture-recapture (SCR) models to data collected on class structured or stratified populations. Our interest is motivated by the necessity of accommodating the problem of missing observations of individual class membership. This is particularly problematic in SCR data arising from DNA analysis of scat, hair or other material, which frequently yields individual identity but fails to identify the sex. Moreover, this can represent a large fraction of the data and, given the typically small sample sizes of many capture-recapture studies based on DNA information, utilization of the data with missing sex information is necessary. We develop the class structured likelihood for the case of missing covariate values, and then we address the scaling of the likelihood so that models with and without class structured parameters can be formally compared regardless of missing values. We apply our class structured model to black bear data collected in New York in which sex could be determined for only 62 of 169 uniquely identified individuals. The models containing sex-specificity of both the intercept of the SCR encounter probability model and the distance coefficient, and including a behavioral response are strongly favored by log-likelihood. Estimated population sex ratio is strongly influenced by sex structure in model parameters illustrating the importance of rigorous modeling of sex differences in capture-recapture models.

Functional reorganisation in chronic pain and neural correlates of pain sensitisation: A coordinate based meta-analysis of 266 cutaneous pain fMRI studies.

PubMed

Tanasescu, Radu; Cottam, William J; Condon, Laura; Tench, Christopher R; Auer, Dorothee P

2016-09-01

Maladaptive mechanisms of pain processing in chronic pain conditions (CP) are poorly understood. We used coordinate based meta-analysis of 266 fMRI pain studies to study functional brain reorganisation in CP and experimental models of hyperalgesia. The pattern of nociceptive brain activation was similar in CP, hyperalgesia and normalgesia in controls. However, elevated likelihood of activation was detected in the left putamen, left frontal gyrus and right insula in CP comparing stimuli of the most painful vs. other site. Meta-analysis of contrast maps showed no difference between CP, controls, mood conditions. In contrast, experimental hyperalgesia induced stronger activation in the bilateral insula, left cingulate and right frontal gyrus. Activation likelihood maps support a shared neural pain signature of cutaneous nociception in CP and controls. We also present a double dissociation between neural correlates of transient and persistent pain sensitisation with general increased activation intensity but unchanged pattern in experimental hyperalgesia and, by contrast, focally increased activation likelihood, but unchanged intensity, in CP when stimulated at the most painful body part. Copyright © 2016. Published by Elsevier Ltd.

Mapping Quantitative Traits in Unselected Families: Algorithms and Examples

PubMed Central

Dupuis, Josée; Shi, Jianxin; Manning, Alisa K.; Benjamin, Emelia J.; Meigs, James B.; Cupples, L. Adrienne; Siegmund, David

2009-01-01

Linkage analysis has been widely used to identify from family data genetic variants influencing quantitative traits. Common approaches have both strengths and limitations. Likelihood ratio tests typically computed in variance component analysis can accommodate large families but are highly sensitive to departure from normality assumptions. Regression-based approaches are more robust but their use has primarily been restricted to nuclear families. In this paper, we develop methods for mapping quantitative traits in moderately large pedigrees. Our methods are based on the score statistic which in contrast to the likelihood ratio statistic, can use nonparametric estimators of variability to achieve robustness of the false positive rate against departures from the hypothesized phenotypic model. Because the score statistic is easier to calculate than the likelihood ratio statistic, our basic mapping methods utilize relatively simple computer code that performs statistical analysis on output from any program that computes estimates of identity-by-descent. This simplicity also permits development and evaluation of methods to deal with multivariate and ordinal phenotypes, and with gene-gene and gene-environment interaction. We demonstrate our methods on simulated data and on fasting insulin, a quantitative trait measured in the Framingham Heart Study. PMID:19278016

Exact likelihood evaluations and foreground marginalization in low resolution WMAP data

NASA Astrophysics Data System (ADS)

Slosar, Anže; Seljak, Uroš; Makarov, Alexey

2004-06-01

The large scale anisotropies of Wilkinson Microwave Anisotropy Probe (WMAP) data have attracted a lot of attention and have been a source of controversy, with many favorite cosmological models being apparently disfavored by the power spectrum estimates at low l. All the existing analyses of theoretical models are based on approximations for the likelihood function, which are likely to be inaccurate on large scales. Here we present exact evaluations of the likelihood of the low multipoles by direct inversion of the theoretical covariance matrix for low resolution WMAP maps. We project out the unwanted galactic contaminants using the WMAP derived maps of these foregrounds. This improves over the template based foreground subtraction used in the original analysis, which can remove some of the cosmological signal and may lead to a suppression of power. As a result we find an increase in power at low multipoles. For the quadrupole the maximum likelihood values are rather uncertain and vary between 140 and 220 μK2. On the other hand, the probability distribution away from the peak is robust and, assuming a uniform prior between 0 and 2000 μK2, the probability of having the true value above 1200 μK2 (as predicted by the simplest cold dark matter model with a cosmological constant) is 10%, a factor of 2.5 higher than predicted by the WMAP likelihood code. We do not find the correlation function to be unusual beyond the low quadrupole value. We develop a fast likelihood evaluation routine that can be used instead of WMAP routines for low l values. We apply it to the Markov chain Monte Carlo analysis to compare the cosmological parameters between the two cases. The new analysis of WMAP either alone or jointly with the Sloan Digital Sky Survey (SDSS) and the Very Small Array (VSA) data reduces the evidence for running to less than 1σ, giving αs=-0.022±0.033 for the combined case. The new analysis prefers about a 1σ lower value of Ωm, a consequence of an increased integrated Sachs-Wolfe (ISW) effect contribution required by the increase in the spectrum at low l. These results suggest that the details of foreground removal and full likelihood analysis are important for parameter estimation from the WMAP data. They are robust in the sense that they do not change significantly with frequency, mask, or details of foreground template marginalization. The marginalization approach presented here is the most conservative method to remove the foregrounds and should be particularly useful in the analysis of polarization, where foreground contamination may be much more severe.

A likelihood-based time series modeling approach for application in dendrochronology to examine the growth-climate relations and forest disturbance history.

PubMed

Lee, E Henry; Wickham, Charlotte; Beedlow, Peter A; Waschmann, Ronald S; Tingey, David T

2017-10-01

A time series intervention analysis (TSIA) of dendrochronological data to infer the tree growth-climate-disturbance relations and forest disturbance history is described. Maximum likelihood is used to estimate the parameters of a structural time series model with components for climate and forest disturbances (i.e., pests, diseases, fire). The statistical method is illustrated with a tree-ring width time series for a mature closed-canopy Douglas-fir stand on the west slopes of the Cascade Mountains of Oregon, USA that is impacted by Swiss needle cast disease caused by the foliar fungus, Phaecryptopus gaeumannii (Rhode) Petrak. The likelihood-based TSIA method is proposed for the field of dendrochronology to understand the interaction of temperature, water, and forest disturbances that are important in forest ecology and climate change studies.

A single-index threshold Cox proportional hazard model for identifying a treatment-sensitive subset based on multiple biomarkers.

PubMed

He, Ye; Lin, Huazhen; Tu, Dongsheng

2018-06-04

In this paper, we introduce a single-index threshold Cox proportional hazard model to select and combine biomarkers to identify patients who may be sensitive to a specific treatment. A penalized smoothed partial likelihood is proposed to estimate the parameters in the model. A simple, efficient, and unified algorithm is presented to maximize this likelihood function. The estimators based on this likelihood function are shown to be consistent and asymptotically normal. Under mild conditions, the proposed estimators also achieve the oracle property. The proposed approach is evaluated through simulation analyses and application to the analysis of data from two clinical trials, one involving patients with locally advanced or metastatic pancreatic cancer and one involving patients with resectable lung cancer. Copyright © 2018 John Wiley & Sons, Ltd.

Robust analysis of semiparametric renewal process models

PubMed Central

Lin, Feng-Chang; Truong, Young K.; Fine, Jason P.

2013-01-01

Summary A rate model is proposed for a modulated renewal process comprising a single long sequence, where the covariate process may not capture the dependencies in the sequence as in standard intensity models. We consider partial likelihood-based inferences under a semiparametric multiplicative rate model, which has been widely studied in the context of independent and identical data. Under an intensity model, gap times in a single long sequence may be used naively in the partial likelihood with variance estimation utilizing the observed information matrix. Under a rate model, the gap times cannot be treated as independent and studying the partial likelihood is much more challenging. We employ a mixing condition in the application of limit theory for stationary sequences to obtain consistency and asymptotic normality. The estimator's variance is quite complicated owing to the unknown gap times dependence structure. We adapt block bootstrapping and cluster variance estimators to the partial likelihood. Simulation studies and an analysis of a semiparametric extension of a popular model for neural spike train data demonstrate the practical utility of the rate approach in comparison with the intensity approach. PMID:24550568

Likelihood ratio meta-analysis: New motivation and approach for an old method.

PubMed

Dormuth, Colin R; Filion, Kristian B; Platt, Robert W

2016-03-01

A 95% confidence interval (CI) in an updated meta-analysis may not have the expected 95% coverage. If a meta-analysis is simply updated with additional data, then the resulting 95% CI will be wrong because it will not have accounted for the fact that the earlier meta-analysis failed or succeeded to exclude the null. This situation can be avoided by using the likelihood ratio (LR) as a measure of evidence that does not depend on type-1 error. We show how an LR-based approach, first advanced by Goodman, can be used in a meta-analysis to pool data from separate studies to quantitatively assess where the total evidence points. The method works by estimating the log-likelihood ratio (LogLR) function from each study. Those functions are then summed to obtain a combined function, which is then used to retrieve the total effect estimate, and a corresponding 'intrinsic' confidence interval. Using as illustrations the CAPRIE trial of clopidogrel versus aspirin in the prevention of ischemic events, and our own meta-analysis of higher potency statins and the risk of acute kidney injury, we show that the LR-based method yields the same point estimate as the traditional analysis, but with an intrinsic confidence interval that is appropriately wider than the traditional 95% CI. The LR-based method can be used to conduct both fixed effect and random effects meta-analyses, it can be applied to old and new meta-analyses alike, and results can be presented in a format that is familiar to a meta-analytic audience. Copyright © 2016 Elsevier Inc. All rights reserved.

A unifying framework for marginalized random intercept models of correlated binary outcomes

PubMed Central

Swihart, Bruce J.; Caffo, Brian S.; Crainiceanu, Ciprian M.

2013-01-01

We demonstrate that many current approaches for marginal modeling of correlated binary outcomes produce likelihoods that are equivalent to the copula-based models herein. These general copula models of underlying latent threshold random variables yield likelihood-based models for marginal fixed effects estimation and interpretation in the analysis of correlated binary data with exchangeable correlation structures. Moreover, we propose a nomenclature and set of model relationships that substantially elucidates the complex area of marginalized random intercept models for binary data. A diverse collection of didactic mathematical and numerical examples are given to illustrate concepts. PMID:25342871

Combining classifiers using their receiver operating characteristics and maximum likelihood estimation.

PubMed

Haker, Steven; Wells, William M; Warfield, Simon K; Talos, Ion-Florin; Bhagwat, Jui G; Goldberg-Zimring, Daniel; Mian, Asim; Ohno-Machado, Lucila; Zou, Kelly H

2005-01-01

In any medical domain, it is common to have more than one test (classifier) to diagnose a disease. In image analysis, for example, there is often more than one reader or more than one algorithm applied to a certain data set. Combining of classifiers is often helpful, but determining the way in which classifiers should be combined is not trivial. Standard strategies are based on learning classifier combination functions from data. We describe a simple strategy to combine results from classifiers that have not been applied to a common data set, and therefore can not undergo this type of joint training. The strategy, which assumes conditional independence of classifiers, is based on the calculation of a combined Receiver Operating Characteristic (ROC) curve, using maximum likelihood analysis to determine a combination rule for each ROC operating point. We offer some insights into the use of ROC analysis in the field of medical imaging.

Combining Classifiers Using Their Receiver Operating Characteristics and Maximum Likelihood Estimation*

PubMed Central

Haker, Steven; Wells, William M.; Warfield, Simon K.; Talos, Ion-Florin; Bhagwat, Jui G.; Goldberg-Zimring, Daniel; Mian, Asim; Ohno-Machado, Lucila; Zou, Kelly H.

2010-01-01

In any medical domain, it is common to have more than one test (classifier) to diagnose a disease. In image analysis, for example, there is often more than one reader or more than one algorithm applied to a certain data set. Combining of classifiers is often helpful, but determining the way in which classifiers should be combined is not trivial. Standard strategies are based on learning classifier combination functions from data. We describe a simple strategy to combine results from classifiers that have not been applied to a common data set, and therefore can not undergo this type of joint training. The strategy, which assumes conditional independence of classifiers, is based on the calculation of a combined Receiver Operating Characteristic (ROC) curve, using maximum likelihood analysis to determine a combination rule for each ROC operating point. We offer some insights into the use of ROC analysis in the field of medical imaging. PMID:16685884

Workplace air measurements and likelihood of exposure to manufactured nano-objects, agglomerates, and aggregates

NASA Astrophysics Data System (ADS)

Brouwer, Derk H.; van Duuren-Stuurman, Birgit; Berges, Markus; Bard, Delphine; Jankowska, Elzbieta; Moehlmann, Carsten; Pelzer, Johannes; Mark, Dave

2013-11-01

Manufactured nano-objects, agglomerates, and aggregates (NOAA) may have adverse effect on human health, but little is known about occupational risks since actual estimates of exposure are lacking. In a large-scale workplace air-monitoring campaign, 19 enterprises were visited and 120 potential exposure scenarios were measured. A multi-metric exposure assessment approach was followed and a decision logic was developed to afford analysis of all results in concert. The overall evaluation was classified by categories of likelihood of exposure. At task level about 53 % showed increased particle number or surface area concentration compared to "background" level, whereas 72 % of the TEM samples revealed an indication that NOAA were present in the workplace. For 54 out of the 120 task-based exposure scenarios, an overall evaluation could be made based on all parameters of the decision logic. For only 1 exposure scenario (approximately 2 %), the highest level of potential likelihood was assigned, whereas in total in 56 % of the exposure scenarios the overall evaluation revealed the lowest level of likelihood. However, for the remaining 42 % exposure to NOAA could not be excluded.

Three regularities of recognition memory: the role of bias.

PubMed

Hilford, Andrew; Maloney, Laurence T; Glanzer, Murray; Kim, Kisok

2015-12-01

A basic assumption of Signal Detection Theory is that decisions are made on the basis of likelihood ratios. In a preceding paper, Glanzer, Hilford, and Maloney (Psychonomic Bulletin & Review, 16, 431-455, 2009) showed that the likelihood ratio assumption implies that three regularities will occur in recognition memory: (1) the Mirror Effect, (2) the Variance Effect, (3) the normalized Receiver Operating Characteristic (z-ROC) Length Effect. The paper offered formal proofs and computational demonstrations that decisions based on likelihood ratios produce the three regularities. A survey of data based on group ROCs from 36 studies validated the likelihood ratio assumption by showing that its three implied regularities are ubiquitous. The study noted, however, that bias, another basic factor in Signal Detection Theory, can obscure the Mirror Effect. In this paper we examine how bias affects the regularities at the theoretical level. The theoretical analysis shows: (1) how bias obscures the Mirror Effect, not the other two regularities, and (2) four ways to counter that obscuring. We then report the results of five experiments that support the theoretical analysis. The analyses and the experimental results also demonstrate: (1) that the three regularities govern individual, as well as group, performance, (2) alternative explanations of the regularities are ruled out, and (3) that Signal Detection Theory, correctly applied, gives a simple and unified explanation of recognition memory data.

On Bayesian Testing of Additive Conjoint Measurement Axioms Using Synthetic Likelihood.

PubMed

Karabatsos, George

2018-06-01

This article introduces a Bayesian method for testing the axioms of additive conjoint measurement. The method is based on an importance sampling algorithm that performs likelihood-free, approximate Bayesian inference using a synthetic likelihood to overcome the analytical intractability of this testing problem. This new method improves upon previous methods because it provides an omnibus test of the entire hierarchy of cancellation axioms, beyond double cancellation. It does so while accounting for the posterior uncertainty that is inherent in the empirical orderings that are implied by these axioms, together. The new method is illustrated through a test of the cancellation axioms on a classic survey data set, and through the analysis of simulated data.

Univariate and bivariate likelihood-based meta-analysis methods performed comparably when marginal sensitivity and specificity were the targets of inference.

PubMed

Dahabreh, Issa J; Trikalinos, Thomas A; Lau, Joseph; Schmid, Christopher H

2017-03-01

To compare statistical methods for meta-analysis of sensitivity and specificity of medical tests (e.g., diagnostic or screening tests). We constructed a database of PubMed-indexed meta-analyses of test performance from which 2 × 2 tables for each included study could be extracted. We reanalyzed the data using univariate and bivariate random effects models fit with inverse variance and maximum likelihood methods. Analyses were performed using both normal and binomial likelihoods to describe within-study variability. The bivariate model using the binomial likelihood was also fit using a fully Bayesian approach. We use two worked examples-thoracic computerized tomography to detect aortic injury and rapid prescreening of Papanicolaou smears to detect cytological abnormalities-to highlight that different meta-analysis approaches can produce different results. We also present results from reanalysis of 308 meta-analyses of sensitivity and specificity. Models using the normal approximation produced sensitivity and specificity estimates closer to 50% and smaller standard errors compared to models using the binomial likelihood; absolute differences of 5% or greater were observed in 12% and 5% of meta-analyses for sensitivity and specificity, respectively. Results from univariate and bivariate random effects models were similar, regardless of estimation method. Maximum likelihood and Bayesian methods produced almost identical summary estimates under the bivariate model; however, Bayesian analyses indicated greater uncertainty around those estimates. Bivariate models produced imprecise estimates of the between-study correlation of sensitivity and specificity. Differences between methods were larger with increasing proportion of studies that were small or required a continuity correction. The binomial likelihood should be used to model within-study variability. Univariate and bivariate models give similar estimates of the marginal distributions for sensitivity and specificity. Bayesian methods fully quantify uncertainty and their ability to incorporate external evidence may be useful for imprecisely estimated parameters. Copyright © 2017 Elsevier Inc. All rights reserved.

Accurate Structural Correlations from Maximum Likelihood Superpositions

PubMed Central

Theobald, Douglas L; Wuttke, Deborah S

2008-01-01

The cores of globular proteins are densely packed, resulting in complicated networks of structural interactions. These interactions in turn give rise to dynamic structural correlations over a wide range of time scales. Accurate analysis of these complex correlations is crucial for understanding biomolecular mechanisms and for relating structure to function. Here we report a highly accurate technique for inferring the major modes of structural correlation in macromolecules using likelihood-based statistical analysis of sets of structures. This method is generally applicable to any ensemble of related molecules, including families of nuclear magnetic resonance (NMR) models, different crystal forms of a protein, and structural alignments of homologous proteins, as well as molecular dynamics trajectories. Dominant modes of structural correlation are determined using principal components analysis (PCA) of the maximum likelihood estimate of the correlation matrix. The correlations we identify are inherently independent of the statistical uncertainty and dynamic heterogeneity associated with the structural coordinates. We additionally present an easily interpretable method (“PCA plots”) for displaying these positional correlations by color-coding them onto a macromolecular structure. Maximum likelihood PCA of structural superpositions, and the structural PCA plots that illustrate the results, will facilitate the accurate determination of dynamic structural correlations analyzed in diverse fields of structural biology. PMID:18282091

RY-Coding and Non-Homogeneous Models Can Ameliorate the Maximum-Likelihood Inferences From Nucleotide Sequence Data with Parallel Compositional Heterogeneity.

PubMed

Ishikawa, Sohta A; Inagaki, Yuji; Hashimoto, Tetsuo

2012-01-01

In phylogenetic analyses of nucleotide sequences, 'homogeneous' substitution models, which assume the stationarity of base composition across a tree, are widely used, albeit individual sequences may bear distinctive base frequencies. In the worst-case scenario, a homogeneous model-based analysis can yield an artifactual union of two distantly related sequences that achieved similar base frequencies in parallel. Such potential difficulty can be countered by two approaches, 'RY-coding' and 'non-homogeneous' models. The former approach converts four bases into purine and pyrimidine to normalize base frequencies across a tree, while the heterogeneity in base frequency is explicitly incorporated in the latter approach. The two approaches have been applied to real-world sequence data; however, their basic properties have not been fully examined by pioneering simulation studies. Here, we assessed the performances of the maximum-likelihood analyses incorporating RY-coding and a non-homogeneous model (RY-coding and non-homogeneous analyses) on simulated data with parallel convergence to similar base composition. Both RY-coding and non-homogeneous analyses showed superior performances compared with homogeneous model-based analyses. Curiously, the performance of RY-coding analysis appeared to be significantly affected by a setting of the substitution process for sequence simulation relative to that of non-homogeneous analysis. The performance of a non-homogeneous analysis was also validated by analyzing a real-world sequence data set with significant base heterogeneity.

Maximum likelihood estimation and EM algorithm of Copas-like selection model for publication bias correction.

PubMed

Ning, Jing; Chen, Yong; Piao, Jin

2017-07-01

Publication bias occurs when the published research results are systematically unrepresentative of the population of studies that have been conducted, and is a potential threat to meaningful meta-analysis. The Copas selection model provides a flexible framework for correcting estimates and offers considerable insight into the publication bias. However, maximizing the observed likelihood under the Copas selection model is challenging because the observed data contain very little information on the latent variable. In this article, we study a Copas-like selection model and propose an expectation-maximization (EM) algorithm for estimation based on the full likelihood. Empirical simulation studies show that the EM algorithm and its associated inferential procedure performs well and avoids the non-convergence problem when maximizing the observed likelihood. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Predicting Likelihood of Surgery Prior to First Visit in Patients with Back and Lower Extremity Symptoms: A simple mathematical model based on over 8000 patients.

PubMed

Boden, Lauren M; Boden, Stephanie A; Premkumar, Ajay; Gottschalk, Michael B; Boden, Scott D

2018-02-09

Retrospective analysis of prospectively collected data. To create a data-driven triage system stratifying patients by likelihood of undergoing spinal surgery within one year of presentation. Low back pain (LBP) and radicular lower extremity (LE) symptoms are common musculoskeletal problems. There is currently no standard data-derived triage process based on information that can be obtained prior to the initial physician-patient encounter to direct patients to the optimal physician type. We analyzed patient-reported data from 8006 patients with a chief complaint of LBP and/or LE radicular symptoms who presented to surgeons at a large multidisciplinary spine center between September 1, 2005 and June 30, 2016. Univariate and multivariate analysis identified independent risk factors for undergoing spinal surgery within one year of initial visit. A model incorporating these risk factors was created using a random sample of 80% of the total patients in our cohort, and validated on the remaining 20%. The baseline one-year surgery rate within our cohort was 39% for all patients and 42% for patients with LE symptoms. Those identified as high likelihood by the center's existing triage process had a surgery rate of 45%. The new triage scoring system proposed in this study was able to identify a high likelihood group in which 58% underwent surgery, which is a 46% higher surgery rate than in non-triaged patients and a 29% improvement from our institution's existing triage system. The data-driven triage model and scoring system derived and validated in this study (Spine Surgery Likelihood model [SSL-11]), significantly improved existing processes in predicting the likelihood of undergoing spinal surgery within one year of initial presentation. This triage system will allow centers to more selectively screen for surgical candidates and more effectively direct patients to surgeons or non-operative spine specialists. 4.

The Determinants of Place of Death: An Evidence-Based Analysis

PubMed Central

Costa, V

2014-01-01

Background According to a conceptual model described in this analysis, place of death is determined by an interplay of factors associated with the illness, the individual, and the environment. Objectives Our objective was to evaluate the determinants of place of death for adult patients who have been diagnosed with an advanced, life-limiting condition and are not expected to stabilize or improve. Data Sources A literature search was performed using Ovid MEDLINE, Ovid MEDLINE In-Process and Other Non-Indexed Citations, Ovid Embase, EBSCO Cumulative Index to Nursing & Allied Health Literature (CINAHL), and EBM Reviews, for studies published from January 1, 2004, to September 24, 2013. Review Methods Different places of death are considered in this analysis—home, nursing home, inpatient hospice, and inpatient palliative care unit, compared with hospital. We selected factors to evaluate from a list of possible predictors—i.e., determinants—of death. We extracted the adjusted odds ratios and 95% confidence intervals of each determinant, performed a meta-analysis if appropriate, and conducted a stratified analysis if substantial heterogeneity was observed. Results From a literature search yielding 5,899 citations, we included 2 systematic reviews and 29 observational studies. Factors that increased the likelihood of home death included multidisciplinary home palliative care, patient preference, having an informal caregiver, and the caregiver's ability to cope. Factors increasing the likelihood of a nursing home death included the availability of palliative care in the nursing home and the existence of advance directives. A cancer diagnosis and the involvement of home care services increased the likelihood of dying in an inpatient palliative care unit. A cancer diagnosis and a longer time between referral to palliative care and death increased the likelihood of inpatient hospice death. The quality of the evidence was considered low. Limitations Our results are based on those of retrospective observational studies. Conclusions The results obtained were consistent with previously published systematic reviews. The analysis identified several factors that are associated with place of death. PMID:26351550

Fast maximum likelihood estimation of mutation rates using a birth-death process.

PubMed

Wu, Xiaowei; Zhu, Hongxiao

2015-02-07

Since fluctuation analysis was first introduced by Luria and Delbrück in 1943, it has been widely used to make inference about spontaneous mutation rates in cultured cells. Under certain model assumptions, the probability distribution of the number of mutants that appear in a fluctuation experiment can be derived explicitly, which provides the basis of mutation rate estimation. It has been shown that, among various existing estimators, the maximum likelihood estimator usually demonstrates some desirable properties such as consistency and lower mean squared error. However, its application in real experimental data is often hindered by slow computation of likelihood due to the recursive form of the mutant-count distribution. We propose a fast maximum likelihood estimator of mutation rates, MLE-BD, based on a birth-death process model with non-differential growth assumption. Simulation studies demonstrate that, compared with the conventional maximum likelihood estimator derived from the Luria-Delbrück distribution, MLE-BD achieves substantial improvement on computational speed and is applicable to arbitrarily large number of mutants. In addition, it still retains good accuracy on point estimation. Published by Elsevier Ltd.

Approximate likelihood calculation on a phylogeny for Bayesian estimation of divergence times.

PubMed

dos Reis, Mario; Yang, Ziheng

2011-07-01

The molecular clock provides a powerful way to estimate species divergence times. If information on some species divergence times is available from the fossil or geological record, it can be used to calibrate a phylogeny and estimate divergence times for all nodes in the tree. The Bayesian method provides a natural framework to incorporate different sources of information concerning divergence times, such as information in the fossil and molecular data. Current models of sequence evolution are intractable in a Bayesian setting, and Markov chain Monte Carlo (MCMC) is used to generate the posterior distribution of divergence times and evolutionary rates. This method is computationally expensive, as it involves the repeated calculation of the likelihood function. Here, we explore the use of Taylor expansion to approximate the likelihood during MCMC iteration. The approximation is much faster than conventional likelihood calculation. However, the approximation is expected to be poor when the proposed parameters are far from the likelihood peak. We explore the use of parameter transforms (square root, logarithm, and arcsine) to improve the approximation to the likelihood curve. We found that the new methods, particularly the arcsine-based transform, provided very good approximations under relaxed clock models and also under the global clock model when the global clock is not seriously violated. The approximation is poorer for analysis under the global clock when the global clock is seriously wrong and should thus not be used. The results suggest that the approximate method may be useful for Bayesian dating analysis using large data sets.

An exclusive human milk-based diet in extremely premature infants reduces the probability of remaining on total parenteral nutrition: a reanalysis of the data.

PubMed

Ghandehari, Heli; Lee, Martin L; Rechtman, David J

2012-04-25

We have previously shown that an exclusively human milk-based diet is beneficial for extremely premature infants who are at risk for necrotizing enterocolitis (NEC). However, no significant difference in the other primary study endpoint, the length of time on total parenteral nutrition (TPN), was found. The current analysis re-evaluates these data from a different statistical perspective considering the probability or likelihood of needing TPN on any given day rather than the number of days on TPN. This study consisted of 207 premature infants randomized into three groups: one group receiving a control diet of human milk, formula and bovine-based fortifier ("control diet"), and the other two groups receiving only human milk and human milk-based fortifier starting at different times in the enteral feeding process (at feeding volumes of 40 or 100 mL/kg/day; "HM40" and "HM100", respectively). The counting process Cox proportional hazards survival model was used to determine the likelihood of needing TPN in each group. The two groups on the completely human-based diet had an 11-14 % reduction in the likelihood of needing nutrition via TPN when compared to infants on the control diet (p = 0.0001 and p = 0.001, respectively for the HM40 and HM100 groups, respectively). This was even more pronounced if the initial period of TPN was excluded (p < 0.0001 for both the HM40 and HM100 groups). A completely human milk-based diet significantly reduces the likelihood of TPN use for extremely premature infants when compared to a diet including cow-based products. This likelihood may be reduced even further when the human milk fortifier is initiated earlier in the feeding process. This study was registered at http://www.clinicaltrials.gov reg. # NCT00506584.

Determinants of Standard Errors of MLEs in Confirmatory Factor Analysis

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Cheng, Ying; Zhang, Wei

2010-01-01

This paper studies changes of standard errors (SE) of the normal-distribution-based maximum likelihood estimates (MLE) for confirmatory factor models as model parameters vary. Using logical analysis, simplified formulas and numerical verification, monotonic relationships between SEs and factor loadings as well as unique variances are found.…

Climate reconstruction analysis using coexistence likelihood estimation (CRACLE): a method for the estimation of climate using vegetation.

PubMed

Harbert, Robert S; Nixon, Kevin C

2015-08-01

• Plant distributions have long been understood to be correlated with the environmental conditions to which species are adapted. Climate is one of the major components driving species distributions. Therefore, it is expected that the plants coexisting in a community are reflective of the local environment, particularly climate.• Presented here is a method for the estimation of climate from local plant species coexistence data. The method, Climate Reconstruction Analysis using Coexistence Likelihood Estimation (CRACLE), is a likelihood-based method that employs specimen collection data at a global scale for the inference of species climate tolerance. CRACLE calculates the maximum joint likelihood of coexistence given individual species climate tolerance characterization to estimate the expected climate.• Plant distribution data for more than 4000 species were used to show that this method accurately infers expected climate profiles for 165 sites with diverse climatic conditions. Estimates differ from the WorldClim global climate model by less than 1.5°C on average for mean annual temperature and less than ∼250 mm for mean annual precipitation. This is a significant improvement upon other plant-based climate-proxy methods.• CRACLE validates long hypothesized interactions between climate and local associations of plant species. Furthermore, CRACLE successfully estimates climate that is consistent with the widely used WorldClim model and therefore may be applied to the quantitative estimation of paleoclimate in future studies. © 2015 Botanical Society of America, Inc.

Diagnostic Accuracy of Coronary Computed Tomography Before Aortic Valve Replacement: Systematic Review and Meta-Analysis.

PubMed

Chaikriangkrai, Kongkiat; Jhun, Hye Yeon; Shantha, Ghanshyam Palamaner Subash; Abdulhak, Aref Bin; Tandon, Rudhir; Alqasrawi, Musab; Klappa, Anthony; Pancholy, Samir; Deshmukh, Abhishek; Bhama, Jay; Sigurdsson, Gardar

2018-07-01

In aortic stenosis patients referred for surgical and transcatheter aortic valve replacement (AVR), the evidence of diagnostic accuracy of coronary computed tomography angiography (CCTA) has been limited. The objective of this study was to investigate the diagnostic accuracy of CCTA for significant coronary artery disease (CAD) in patients referred for AVR using invasive coronary angiography (ICA) as the gold standard. We searched databases for all diagnostic studies of CCTA in patients referred for AVR, which reported diagnostic testing characteristics on patient-based analysis required to pool summary sensitivity, specificity, positive-likelihood ratio, and negative-likelihood ratio. Significant CAD in both CCTA and ICA was defined by >50% stenosis in any coronary artery, coronary stent, or bypass graft. Thirteen studies evaluated 1498 patients (mean age, 74 y; 47% men; 76% transcatheter AVR). The pooled prevalence of significant stenosis determined by ICA was 43%. Hierarchical summary receiver-operating characteristic analysis demonstrated a summary area under curve of 0.96. The pooled sensitivity, specificity, and positive-likelihood and negative-likelihood ratios of CCTA in identifying significant stenosis determined by ICA were 95%, 79%, 4.48, and 0.06, respectively. In subgroup analysis, the diagnostic profiles of CCTA were comparable between surgical and transcatheter AVR. Despite the higher prevalence of significant CAD in patients with aortic stenosis than with other valvular heart diseases, our meta-analysis has shown that CCTA has a suitable diagnostic accuracy profile as a gatekeeper test for ICA. Our study illustrates a need for further study of the potential role of CCTA in preoperative planning for AVR.

Empirical likelihood method for non-ignorable missing data problems.

PubMed

Guan, Zhong; Qin, Jing

2017-01-01

Missing response problem is ubiquitous in survey sampling, medical, social science and epidemiology studies. It is well known that non-ignorable missing is the most difficult missing data problem where the missing of a response depends on its own value. In statistical literature, unlike the ignorable missing data problem, not many papers on non-ignorable missing data are available except for the full parametric model based approach. In this paper we study a semiparametric model for non-ignorable missing data in which the missing probability is known up to some parameters, but the underlying distributions are not specified. By employing Owen (1988)'s empirical likelihood method we can obtain the constrained maximum empirical likelihood estimators of the parameters in the missing probability and the mean response which are shown to be asymptotically normal. Moreover the likelihood ratio statistic can be used to test whether the missing of the responses is non-ignorable or completely at random. The theoretical results are confirmed by a simulation study. As an illustration, the analysis of a real AIDS trial data shows that the missing of CD4 counts around two years are non-ignorable and the sample mean based on observed data only is biased.

Handling Missing Data With Multilevel Structural Equation Modeling and Full Information Maximum Likelihood Techniques.

PubMed

Schminkey, Donna L; von Oertzen, Timo; Bullock, Linda

2016-08-01

With increasing access to population-based data and electronic health records for secondary analysis, missing data are common. In the social and behavioral sciences, missing data frequently are handled with multiple imputation methods or full information maximum likelihood (FIML) techniques, but healthcare researchers have not embraced these methodologies to the same extent and more often use either traditional imputation techniques or complete case analysis, which can compromise power and introduce unintended bias. This article is a review of options for handling missing data, concluding with a case study demonstrating the utility of multilevel structural equation modeling using full information maximum likelihood (MSEM with FIML) to handle large amounts of missing data. MSEM with FIML is a parsimonious and hypothesis-driven strategy to cope with large amounts of missing data without compromising power or introducing bias. This technique is relevant for nurse researchers faced with ever-increasing amounts of electronic data and decreasing research budgets. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

On equivalent parameter learning in simplified feature space based on Bayesian asymptotic analysis.

PubMed

Yamazaki, Keisuke

2012-07-01

Parametric models for sequential data, such as hidden Markov models, stochastic context-free grammars, and linear dynamical systems, are widely used in time-series analysis and structural data analysis. Computation of the likelihood function is one of primary considerations in many learning methods. Iterative calculation of the likelihood such as the model selection is still time-consuming though there are effective algorithms based on dynamic programming. The present paper studies parameter learning in a simplified feature space to reduce the computational cost. Simplifying data is a common technique seen in feature selection and dimension reduction though an oversimplified space causes adverse learning results. Therefore, we mathematically investigate a condition of the feature map to have an asymptotically equivalent convergence point of estimated parameters, referred to as the vicarious map. As a demonstration to find vicarious maps, we consider the feature space, which limits the length of data, and derive a necessary length for parameter learning in hidden Markov models. Copyright © 2012 Elsevier Ltd. All rights reserved.

Detecting the contagion effect in mass killings; a constructive example of the statistical advantages of unbinned likelihood methods.

PubMed

Towers, Sherry; Mubayi, Anuj; Castillo-Chavez, Carlos

2018-01-01

When attempting to statistically distinguish between a null and an alternative hypothesis, many researchers in the life and social sciences turn to binned statistical analysis methods, or methods that are simply based on the moments of a distribution (such as the mean, and variance). These methods have the advantage of simplicity of implementation, and simplicity of explanation. However, when null and alternative hypotheses manifest themselves in subtle differences in patterns in the data, binned analysis methods may be insensitive to these differences, and researchers may erroneously fail to reject the null hypothesis when in fact more sensitive statistical analysis methods might produce a different result when the null hypothesis is actually false. Here, with a focus on two recent conflicting studies of contagion in mass killings as instructive examples, we discuss how the use of unbinned likelihood methods makes optimal use of the information in the data; a fact that has been long known in statistical theory, but perhaps is not as widely appreciated amongst general researchers in the life and social sciences. In 2015, Towers et al published a paper that quantified the long-suspected contagion effect in mass killings. However, in 2017, Lankford & Tomek subsequently published a paper, based upon the same data, that claimed to contradict the results of the earlier study. The former used unbinned likelihood methods, and the latter used binned methods, and comparison of distribution moments. Using these analyses, we also discuss how visualization of the data can aid in determination of the most appropriate statistical analysis methods to distinguish between a null and alternate hypothesis. We also discuss the importance of assessment of the robustness of analysis results to methodological assumptions made (for example, arbitrary choices of number of bins and bin widths when using binned methods); an issue that is widely overlooked in the literature, but is critical to analysis reproducibility and robustness. When an analysis cannot distinguish between a null and alternate hypothesis, care must be taken to ensure that the analysis methodology itself maximizes the use of information in the data that can distinguish between the two hypotheses. The use of binned methods by Lankford & Tomek (2017), that examined how many mass killings fell within a 14 day window from a previous mass killing, substantially reduced the sensitivity of their analysis to contagion effects. The unbinned likelihood methods used by Towers et al (2015) did not suffer from this problem. While a binned analysis might be favorable for simplicity and clarity of presentation, unbinned likelihood methods are preferable when effects might be somewhat subtle.

Detecting the contagion effect in mass killings; a constructive example of the statistical advantages of unbinned likelihood methods

PubMed Central

Mubayi, Anuj; Castillo-Chavez, Carlos

2018-01-01

Background When attempting to statistically distinguish between a null and an alternative hypothesis, many researchers in the life and social sciences turn to binned statistical analysis methods, or methods that are simply based on the moments of a distribution (such as the mean, and variance). These methods have the advantage of simplicity of implementation, and simplicity of explanation. However, when null and alternative hypotheses manifest themselves in subtle differences in patterns in the data, binned analysis methods may be insensitive to these differences, and researchers may erroneously fail to reject the null hypothesis when in fact more sensitive statistical analysis methods might produce a different result when the null hypothesis is actually false. Here, with a focus on two recent conflicting studies of contagion in mass killings as instructive examples, we discuss how the use of unbinned likelihood methods makes optimal use of the information in the data; a fact that has been long known in statistical theory, but perhaps is not as widely appreciated amongst general researchers in the life and social sciences. Methods In 2015, Towers et al published a paper that quantified the long-suspected contagion effect in mass killings. However, in 2017, Lankford & Tomek subsequently published a paper, based upon the same data, that claimed to contradict the results of the earlier study. The former used unbinned likelihood methods, and the latter used binned methods, and comparison of distribution moments. Using these analyses, we also discuss how visualization of the data can aid in determination of the most appropriate statistical analysis methods to distinguish between a null and alternate hypothesis. We also discuss the importance of assessment of the robustness of analysis results to methodological assumptions made (for example, arbitrary choices of number of bins and bin widths when using binned methods); an issue that is widely overlooked in the literature, but is critical to analysis reproducibility and robustness. Conclusions When an analysis cannot distinguish between a null and alternate hypothesis, care must be taken to ensure that the analysis methodology itself maximizes the use of information in the data that can distinguish between the two hypotheses. The use of binned methods by Lankford & Tomek (2017), that examined how many mass killings fell within a 14 day window from a previous mass killing, substantially reduced the sensitivity of their analysis to contagion effects. The unbinned likelihood methods used by Towers et al (2015) did not suffer from this problem. While a binned analysis might be favorable for simplicity and clarity of presentation, unbinned likelihood methods are preferable when effects might be somewhat subtle. PMID:29742115

Object recognition and localization from 3D point clouds by maximum-likelihood estimation

NASA Astrophysics Data System (ADS)

Dantanarayana, Harshana G.; Huntley, Jonathan M.

2017-08-01

We present an algorithm based on maximum-likelihood analysis for the automated recognition of objects, and estimation of their pose, from 3D point clouds. Surfaces segmented from depth images are used as the features, unlike `interest point'-based algorithms which normally discard such data. Compared to the 6D Hough transform, it has negligible memory requirements, and is computationally efficient compared to iterative closest point algorithms. The same method is applicable to both the initial recognition/pose estimation problem as well as subsequent pose refinement through appropriate choice of the dispersion of the probability density functions. This single unified approach therefore avoids the usual requirement for different algorithms for these two tasks. In addition to the theoretical description, a simple 2 degrees of freedom (d.f.) example is given, followed by a full 6 d.f. analysis of 3D point cloud data from a cluttered scene acquired by a projected fringe-based scanner, which demonstrated an RMS alignment error as low as 0.3 mm.

Treatment Retention Among Patients Participating in Coordinated Specialty Care for First-Episode Psychosis: a Mixed-Methods Analysis.

PubMed

Hamilton, Jane E; Srivastava, Devika; Womack, Danica; Brown, Ashlie; Schulz, Brian; Macakanja, April; Walker, April; Wu, Mon-Ju; Williamson, Mark; Cho, Raymond Y

2018-06-05

Young adults experiencing first-episode psychosis have historically been difficult to retain in mental health treatment. Communities across the United States are implementing Coordinated Specialty Care to improve outcomes for individuals experiencing first-episode psychosis. This mixed-methods research study examined the relationship between program services and treatment retention, operationalized as the likelihood of remaining in the program for 9 months or more. In the adjusted analysis, male gender and participation in home-based cognitive behavioral therapy were associated with an increased likelihood of remaining in treatment. The key informant interview findings suggest the shared decision-making process and the breadth, flexibility, and focus on functional recovery of the home-based cognitive behavioral therapy intervention may have positively influenced treatment retention. These findings suggest the use of shared decision-making and improved access to home-based cognitive behavioral therapy for first-episode psychosis patients may improve outcomes for this vulnerable population.

Choosing relatives for DNA identification of missing persons.

PubMed

Ge, Jianye; Budowle, Bruce; Chakraborty, Ranajit

2011-01-01

DNA-based analysis is integral to missing person identification cases. When direct references are not available, indirect relative references can be used to identify missing persons by kinship analysis. Generally, more reference relatives render greater accuracy of identification. However, it is costly to type multiple references. Thus, at times, decisions may need to be made on which relatives to type. In this study, pedigrees for 37 common reference scenarios with 13 CODIS STRs were simulated to rank the information content of different combinations of relatives. The results confirm that first-order relatives (parents and fullsibs) are the most preferred relatives to identify missing persons; fullsibs are also informative. Less genetic dependence between references provides a higher on average likelihood ratio. Distant relatives may not be helpful solely by autosomal markers. But lineage-based Y chromosome and mitochondrial DNA markers can increase the likelihood ratio or serve as filters to exclude putative relationships. © 2010 American Academy of Forensic Sciences.

Zero-inflated Poisson model based likelihood ratio test for drug safety signal detection.

PubMed

Huang, Lan; Zheng, Dan; Zalkikar, Jyoti; Tiwari, Ram

2017-02-01

In recent decades, numerous methods have been developed for data mining of large drug safety databases, such as Food and Drug Administration's (FDA's) Adverse Event Reporting System, where data matrices are formed by drugs such as columns and adverse events as rows. Often, a large number of cells in these data matrices have zero cell counts and some of them are "true zeros" indicating that the drug-adverse event pairs cannot occur, and these zero counts are distinguished from the other zero counts that are modeled zero counts and simply indicate that the drug-adverse event pairs have not occurred yet or have not been reported yet. In this paper, a zero-inflated Poisson model based likelihood ratio test method is proposed to identify drug-adverse event pairs that have disproportionately high reporting rates, which are also called signals. The maximum likelihood estimates of the model parameters of zero-inflated Poisson model based likelihood ratio test are obtained using the expectation and maximization algorithm. The zero-inflated Poisson model based likelihood ratio test is also modified to handle the stratified analyses for binary and categorical covariates (e.g. gender and age) in the data. The proposed zero-inflated Poisson model based likelihood ratio test method is shown to asymptotically control the type I error and false discovery rate, and its finite sample performance for signal detection is evaluated through a simulation study. The simulation results show that the zero-inflated Poisson model based likelihood ratio test method performs similar to Poisson model based likelihood ratio test method when the estimated percentage of true zeros in the database is small. Both the zero-inflated Poisson model based likelihood ratio test and likelihood ratio test methods are applied to six selected drugs, from the 2006 to 2011 Adverse Event Reporting System database, with varying percentages of observed zero-count cells.

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

Jason L. Wright

Finding and identifying Cryptography is a growing concern in the malware analysis community. In this paper, a heuristic method for determining the likelihood that a given function contains a cryptographic algorithm is discussed and the results of applying this method in various environments is shown. The algorithm is based on frequency analysis of opcodes that make up each function within a binary.

Reliable and More Powerful Methods for Power Analysis in Structural Equation Modeling

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Zhang, Zhiyong; Zhao, Yanyun

2017-01-01

The normal-distribution-based likelihood ratio statistic T[subscript ml] = nF[subscript ml] is widely used for power analysis in structural Equation modeling (SEM). In such an analysis, power and sample size are computed by assuming that T[subscript ml] follows a central chi-square distribution under H[subscript 0] and a noncentral chi-square…

Major Accidents (Gray Swans) Likelihood Modeling Using Accident Precursors and Approximate Reasoning.

PubMed

Khakzad, Nima; Khan, Faisal; Amyotte, Paul

2015-07-01

Compared to the remarkable progress in risk analysis of normal accidents, the risk analysis of major accidents has not been so well-established, partly due to the complexity of such accidents and partly due to low probabilities involved. The issue of low probabilities normally arises from the scarcity of major accidents' relevant data since such accidents are few and far between. In this work, knowing that major accidents are frequently preceded by accident precursors, a novel precursor-based methodology has been developed for likelihood modeling of major accidents in critical infrastructures based on a unique combination of accident precursor data, information theory, and approximate reasoning. For this purpose, we have introduced an innovative application of information analysis to identify the most informative near accident of a major accident. The observed data of the near accident were then used to establish predictive scenarios to foresee the occurrence of the major accident. We verified the methodology using offshore blowouts in the Gulf of Mexico, and then demonstrated its application to dam breaches in the United Sates. © 2015 Society for Risk Analysis.

Maximal likelihood correspondence estimation for face recognition across pose.

PubMed

Li, Shaoxin; Liu, Xin; Chai, Xiujuan; Zhang, Haihong; Lao, Shihong; Shan, Shiguang

2014-10-01

Due to the misalignment of image features, the performance of many conventional face recognition methods degrades considerably in across pose scenario. To address this problem, many image matching-based methods are proposed to estimate semantic correspondence between faces in different poses. In this paper, we aim to solve two critical problems in previous image matching-based correspondence learning methods: 1) fail to fully exploit face specific structure information in correspondence estimation and 2) fail to learn personalized correspondence for each probe image. To this end, we first build a model, termed as morphable displacement field (MDF), to encode face specific structure information of semantic correspondence from a set of real samples of correspondences calculated from 3D face models. Then, we propose a maximal likelihood correspondence estimation (MLCE) method to learn personalized correspondence based on maximal likelihood frontal face assumption. After obtaining the semantic correspondence encoded in the learned displacement, we can synthesize virtual frontal images of the profile faces for subsequent recognition. Using linear discriminant analysis method with pixel-intensity features, state-of-the-art performance is achieved on three multipose benchmarks, i.e., CMU-PIE, FERET, and MultiPIE databases. Owe to the rational MDF regularization and the usage of novel maximal likelihood objective, the proposed MLCE method can reliably learn correspondence between faces in different poses even in complex wild environment, i.e., labeled face in the wild database.

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion.

PubMed

Zafar, Raheel; Dass, Sarat C; Malik, Aamir Saeed

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.

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.

Neandertal admixture in Eurasia confirmed by maximum-likelihood analysis of three genomes.

PubMed

Lohse, Konrad; Frantz, Laurent A F

2014-04-01

Although there has been much interest in estimating histories of divergence and admixture from genomic data, it has proved difficult to distinguish recent admixture from long-term structure in the ancestral population. Thus, recent genome-wide analyses based on summary statistics have sparked controversy about the possibility of interbreeding between Neandertals and modern humans in Eurasia. Here we derive the probability of full mutational configurations in nonrecombining sequence blocks under both admixture and ancestral structure scenarios. Dividing the genome into short blocks gives an efficient way to compute maximum-likelihood estimates of parameters. We apply this likelihood scheme to triplets of human and Neandertal genomes and compare the relative support for a model of admixture from Neandertals into Eurasian populations after their expansion out of Africa against a history of persistent structure in their common ancestral population in Africa. Our analysis allows us to conclusively reject a model of ancestral structure in Africa and instead reveals strong support for Neandertal admixture in Eurasia at a higher rate (3.4-7.3%) than suggested previously. Using analysis and simulations we show that our inference is more powerful than previous summary statistics and robust to realistic levels of recombination.

Neandertal Admixture in Eurasia Confirmed by Maximum-Likelihood Analysis of Three Genomes

PubMed Central

Lohse, Konrad; Frantz, Laurent A. F.

2014-01-01

Although there has been much interest in estimating histories of divergence and admixture from genomic data, it has proved difficult to distinguish recent admixture from long-term structure in the ancestral population. Thus, recent genome-wide analyses based on summary statistics have sparked controversy about the possibility of interbreeding between Neandertals and modern humans in Eurasia. Here we derive the probability of full mutational configurations in nonrecombining sequence blocks under both admixture and ancestral structure scenarios. Dividing the genome into short blocks gives an efficient way to compute maximum-likelihood estimates of parameters. We apply this likelihood scheme to triplets of human and Neandertal genomes and compare the relative support for a model of admixture from Neandertals into Eurasian populations after their expansion out of Africa against a history of persistent structure in their common ancestral population in Africa. Our analysis allows us to conclusively reject a model of ancestral structure in Africa and instead reveals strong support for Neandertal admixture in Eurasia at a higher rate (3.4−7.3%) than suggested previously. Using analysis and simulations we show that our inference is more powerful than previous summary statistics and robust to realistic levels of recombination. PMID:24532731

A Bayesian Alternative for Multi-objective Ecohydrological Model Specification

NASA Astrophysics Data System (ADS)

Tang, Y.; Marshall, L. A.; Sharma, A.; Ajami, H.

2015-12-01

Process-based ecohydrological models combine the study of hydrological, physical, biogeochemical and ecological processes of the catchments, which are usually more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov Chain Monte Carlo (MCMC) techniques. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological framework. In our study, a formal Bayesian approach is implemented in an ecohydrological model which combines a hydrological model (HyMOD) and a dynamic vegetation model (DVM). Simulations focused on one objective likelihood (Streamflow/LAI) and multi-objective likelihoods (Streamflow and LAI) with different weights are compared. Uniform, weakly informative and strongly informative prior distributions are used in different simulations. The Kullback-leibler divergence (KLD) is used to measure the dis(similarity) between different priors and corresponding posterior distributions to examine the parameter sensitivity. Results show that different prior distributions can strongly influence posterior distributions for parameters, especially when the available data is limited or parameters are insensitive to the available data. We demonstrate differences in optimized parameters and uncertainty limits in different cases based on multi-objective likelihoods vs. single objective likelihoods. We also demonstrate the importance of appropriately defining the weights of objectives in multi-objective calibration according to different data types.

Regression estimators for generic health-related quality of life and quality-adjusted life years.

PubMed

Basu, Anirban; Manca, Andrea

2012-01-01

To develop regression models for outcomes with truncated supports, such as health-related quality of life (HRQoL) data, and account for features typical of such data such as a skewed distribution, spikes at 1 or 0, and heteroskedasticity. Regression estimators based on features of the Beta distribution. First, both a single equation and a 2-part model are presented, along with estimation algorithms based on maximum-likelihood, quasi-likelihood, and Bayesian Markov-chain Monte Carlo methods. A novel Bayesian quasi-likelihood estimator is proposed. Second, a simulation exercise is presented to assess the performance of the proposed estimators against ordinary least squares (OLS) regression for a variety of HRQoL distributions that are encountered in practice. Finally, the performance of the proposed estimators is assessed by using them to quantify the treatment effect on QALYs in the EVALUATE hysterectomy trial. Overall model fit is studied using several goodness-of-fit tests such as Pearson's correlation test, link and reset tests, and a modified Hosmer-Lemeshow test. The simulation results indicate that the proposed methods are more robust in estimating covariate effects than OLS, especially when the effects are large or the HRQoL distribution has a large spike at 1. Quasi-likelihood techniques are more robust than maximum likelihood estimators. When applied to the EVALUATE trial, all but the maximum likelihood estimators produce unbiased estimates of the treatment effect. One and 2-part Beta regression models provide flexible approaches to regress the outcomes with truncated supports, such as HRQoL, on covariates, after accounting for many idiosyncratic features of the outcomes distribution. This work will provide applied researchers with a practical set of tools to model outcomes in cost-effectiveness analysis.

MODEL-BASED CLUSTERING FOR CLASSIFICATION OF AQUATIC SYSTEMS AND DIAGNOSIS OF ECOLOGICAL STRESS

EPA Science Inventory

Clustering approaches were developed using the classification likelihood, the mixture likelihood, and also using a randomization approach with a model index. Using a clustering approach based on the mixture and classification likelihoods, we have developed an algorithm that...

A Likelihood-Based Framework for Association Analysis of Allele-Specific Copy Numbers.

PubMed

Hu, Y J; Lin, D Y; Sun, W; Zeng, D

2014-10-01

Copy number variants (CNVs) and single nucleotide polymorphisms (SNPs) co-exist throughout the human genome and jointly contribute to phenotypic variations. Thus, it is desirable to consider both types of variants, as characterized by allele-specific copy numbers (ASCNs), in association studies of complex human diseases. Current SNP genotyping technologies capture the CNV and SNP information simultaneously via fluorescent intensity measurements. The common practice of calling ASCNs from the intensity measurements and then using the ASCN calls in downstream association analysis has important limitations. First, the association tests are prone to false-positive findings when differential measurement errors between cases and controls arise from differences in DNA quality or handling. Second, the uncertainties in the ASCN calls are ignored. We present a general framework for the integrated analysis of CNVs and SNPs, including the analysis of total copy numbers as a special case. Our approach combines the ASCN calling and the association analysis into a single step while allowing for differential measurement errors. We construct likelihood functions that properly account for case-control sampling and measurement errors. We establish the asymptotic properties of the maximum likelihood estimators and develop EM algorithms to implement the corresponding inference procedures. The advantages of the proposed methods over the existing ones are demonstrated through realistic simulation studies and an application to a genome-wide association study of schizophrenia. Extensions to next-generation sequencing data are discussed.

Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets

PubMed Central

Cha, Kenny H.; Hadjiiski, Lubomir; Samala, Ravi K.; Chan, Heang-Ping; Caoili, Elaine M.; Cohan, Richard H.

2016-01-01

Purpose: The authors are developing a computerized system for bladder segmentation in CT urography (CTU) as a critical component for computer-aided detection of bladder cancer. Methods: A deep-learning convolutional neural network (DL-CNN) was trained to distinguish between the inside and the outside of the bladder using 160 000 regions of interest (ROI) from CTU images. The trained DL-CNN was used to estimate the likelihood of an ROI being inside the bladder for ROIs centered at each voxel in a CTU case, resulting in a likelihood map. Thresholding and hole-filling were applied to the map to generate the initial contour for the bladder, which was then refined by 3D and 2D level sets. The segmentation performance was evaluated using 173 cases: 81 cases in the training set (42 lesions, 21 wall thickenings, and 18 normal bladders) and 92 cases in the test set (43 lesions, 36 wall thickenings, and 13 normal bladders). The computerized segmentation accuracy using the DL likelihood map was compared to that using a likelihood map generated by Haar features and a random forest classifier, and that using our previous conjoint level set analysis and segmentation system (CLASS) without using a likelihood map. All methods were evaluated relative to the 3D hand-segmented reference contours. Results: With DL-CNN-based likelihood map and level sets, the average volume intersection ratio, average percent volume error, average absolute volume error, average minimum distance, and the Jaccard index for the test set were 81.9% ± 12.1%, 10.2% ± 16.2%, 14.0% ± 13.0%, 3.6 ± 2.0 mm, and 76.2% ± 11.8%, respectively. With the Haar-feature-based likelihood map and level sets, the corresponding values were 74.3% ± 12.7%, 13.0% ± 22.3%, 20.5% ± 15.7%, 5.7 ± 2.6 mm, and 66.7% ± 12.6%, respectively. With our previous CLASS with local contour refinement (LCR) method, the corresponding values were 78.0% ± 14.7%, 16.5% ± 16.8%, 18.2% ± 15.0%, 3.8 ± 2.3 mm, and 73.9% ± 13.5%, respectively. Conclusions: The authors demonstrated that the DL-CNN can overcome the strong boundary between two regions that have large difference in gray levels and provides a seamless mask to guide level set segmentation, which has been a problem for many gradient-based segmentation methods. Compared to our previous CLASS with LCR method, which required two user inputs to initialize the segmentation, DL-CNN with level sets achieved better segmentation performance while using a single user input. Compared to the Haar-feature-based likelihood map, the DL-CNN-based likelihood map could guide the level sets to achieve better segmentation. The results demonstrate the feasibility of our new approach of using DL-CNN in combination with level sets for segmentation of the bladder. PMID:27036584

Search for Point Sources of Ultra-High-Energy Cosmic Rays above 4.0 × 1019 eV Using a Maximum Likelihood Ratio Test

NASA Astrophysics Data System (ADS)

Abbasi, R. U.; Abu-Zayyad, T.; Amann, J. F.; Archbold, G.; Atkins, R.; Bellido, J. A.; Belov, K.; Belz, J. W.; Ben-Zvi, S. Y.; Bergman, D. R.; Boyer, J. H.; Burt, G. W.; Cao, Z.; Clay, R. W.; Connolly, B. M.; Dawson, B. R.; Deng, W.; Farrar, G. R.; Fedorova, Y.; Findlay, J.; Finley, C. B.; Hanlon, W. F.; Hoffman, C. M.; Holzscheiter, M. H.; Hughes, G. A.; Hüntemeyer, P.; Jui, C. C. H.; Kim, K.; Kirn, M. A.; Knapp, B. C.; Loh, E. C.; Maestas, M. M.; Manago, N.; Mannel, E. J.; Marek, L. J.; Martens, K.; Matthews, J. A. J.; Matthews, J. N.; O'Neill, A.; Painter, C. A.; Perera, L.; Reil, K.; Riehle, R.; Roberts, M. D.; Sasaki, M.; Schnetzer, S. R.; Seman, M.; Simpson, K. M.; Sinnis, G.; Smith, J. D.; Snow, R.; Sokolsky, P.; Song, C.; Springer, R. W.; Stokes, B. T.; Thomas, J. R.; Thomas, S. B.; Thomson, G. B.; Tupa, D.; Westerhoff, S.; Wiencke, L. R.; Zech, A.

2005-04-01

We present the results of a search for cosmic-ray point sources at energies in excess of 4.0×1019 eV in the combined data sets recorded by the Akeno Giant Air Shower Array and High Resolution Fly's Eye stereo experiments. The analysis is based on a maximum likelihood ratio test using the probability density function for each event rather than requiring an a priori choice of a fixed angular bin size. No statistically significant clustering of events consistent with a point source is found.

A partial differential equation-based general framework adapted to Rayleigh's, Rician's and Gaussian's distributed noise for restoration and enhancement of magnetic resonance image.

PubMed

Yadav, Ram Bharos; Srivastava, Subodh; Srivastava, Rajeev

2016-01-01

The proposed framework is obtained by casting the noise removal problem into a variational framework. This framework automatically identifies the various types of noise present in the magnetic resonance image and filters them by choosing an appropriate filter. This filter includes two terms: the first term is a data likelihood term and the second term is a prior function. The first term is obtained by minimizing the negative log likelihood of the corresponding probability density functions: Gaussian or Rayleigh or Rician. Further, due to the ill-posedness of the likelihood term, a prior function is needed. This paper examines three partial differential equation based priors which include total variation based prior, anisotropic diffusion based prior, and a complex diffusion (CD) based prior. A regularization parameter is used to balance the trade-off between data fidelity term and prior. The finite difference scheme is used for discretization of the proposed method. The performance analysis and comparative study of the proposed method with other standard methods is presented for brain web dataset at varying noise levels in terms of peak signal-to-noise ratio, mean square error, structure similarity index map, and correlation parameter. From the simulation results, it is observed that the proposed framework with CD based prior is performing better in comparison to other priors in consideration.

Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models

DOE PAGES

Benedict, Matthew N.; Mundy, Michael B.; Henry, Christopher S.; ...

2014-10-16

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genesmore » and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface.« less

Likelihood-Based Gene Annotations for Gap Filling and Quality Assessment in Genome-Scale Metabolic Models

PubMed Central

Benedict, Matthew N.; Mundy, Michael B.; Henry, Christopher S.; Chia, Nicholas; Price, Nathan D.

2014-01-01

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface. PMID:25329157

A meta-analysis of disparities in childhood sexual abuse, parental physical abuse, and peer victimization among sexual minority and sexual nonminority individuals.

PubMed

Friedman, Mark S; Marshal, Michael P; Guadamuz, Thomas E; Wei, Chongyi; Wong, Carolyn F; Saewyc, Elizabeth; Stall, Ron

2011-08-01

We compared the likelihood of childhood sexual abuse (under age 18), parental physical abuse, and peer victimization based on sexual orientation. We conducted a meta-analysis of adolescent school-based studies that compared the likelihood of childhood abuse among sexual minorities vs sexual nonminorities. Sexual minority individuals were on average 3.8, 1.2, 1.7, and 2.4 times more likely to experience sexual abuse, parental physical abuse, or assault at school or to miss school through fear, respectively. Moderation analysis showed that disparities between sexual minority and sexual nonminority individuals were larger for (1) males than females for sexual abuse, (2) females than males for assault at school, and (3) bisexual than gay and lesbian for both parental physical abuse and missing school through fear. Disparities did not change between the 1990s and the 2000s. The higher rates of abuse experienced by sexual minority youths may be one of the driving mechanisms underlying higher rates of mental health problems, substance use, risky sexual behavior, and HIV reported by sexual minority adults.

Statistical methods for analysis of radiation effects with tumor and dose location-specific information with application to the WECARE study of asynchronous contralateral breast cancer

PubMed Central

Langholz, Bryan; Thomas, Duncan C.; Stovall, Marilyn; Smith, Susan A.; Boice, John D.; Shore, Roy E.; Bernstein, Leslie; Lynch, Charles F.; Zhang, Xinbo; Bernstein, Jonine L.

2009-01-01

Summary Methods for the analysis of individually matched case-control studies with location-specific radiation dose and tumor location information are described. These include likelihood methods for analyses that just use cases with precise location of tumor information and methods that also include cases with imprecise tumor location information. The theory establishes that each of these likelihood based methods estimates the same radiation rate ratio parameters, within the context of the appropriate model for location and subject level covariate effects. The underlying assumptions are characterized and the potential strengths and limitations of each method are described. The methods are illustrated and compared using the WECARE study of radiation and asynchronous contralateral breast cancer. PMID:18647297

A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2013-01-01

Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example. PMID:23401213

Bivariate categorical data analysis using normal linear conditional multinomial probability model.

PubMed

Sun, Bingrui; Sutradhar, Brajendra

2015-02-10

Bivariate multinomial data such as the left and right eyes retinopathy status data are analyzed either by using a joint bivariate probability model or by exploiting certain odds ratio-based association models. However, the joint bivariate probability model yields marginal probabilities, which are complicated functions of marginal and association parameters for both variables, and the odds ratio-based association model treats the odds ratios involved in the joint probabilities as 'working' parameters, which are consequently estimated through certain arbitrary 'working' regression models. Also, this later odds ratio-based model does not provide any easy interpretations of the correlations between two categorical variables. On the basis of pre-specified marginal probabilities, in this paper, we develop a bivariate normal type linear conditional multinomial probability model to understand the correlations between two categorical variables. The parameters involved in the model are consistently estimated using the optimal likelihood and generalized quasi-likelihood approaches. The proposed model and the inferences are illustrated through an intensive simulation study as well as an analysis of the well-known Wisconsin Diabetic Retinopathy status data. Copyright © 2014 John Wiley & Sons, Ltd.

Uncued Low SNR Detection with Likelihood from Image Multi Bernoulli Filter

NASA Astrophysics Data System (ADS)

Murphy, T.; Holzinger, M.

2016-09-01

Both SSA and SDA necessitate uncued, partially informed detection and orbit determination efforts for small space objects which often produce only low strength electro-optical signatures. General frame to frame detection and tracking of objects includes methods such as moving target indicator, multiple hypothesis testing, direct track-before-detect methods, and random finite set based multiobject tracking. This paper will apply the multi-Bernoilli filter to low signal-to-noise ratio (SNR), uncued detection of space objects for space domain awareness applications. The primary novel innovation in this paper is a detailed analysis of the existing state-of-the-art likelihood functions and a likelihood function, based on a binary hypothesis, previously proposed by the authors. The algorithm is tested on electro-optical imagery obtained from a variety of sensors at Georgia Tech, including the GT-SORT 0.5m Raven-class telescope, and a twenty degree field of view high frame rate CMOS sensor. In particular, a data set of an extended pass of the Hitomi Astro-H satellite approximately 3 days after loss of communication and potential break up is examined.

A New Monte Carlo Method for Estimating Marginal Likelihoods.

PubMed

Wang, Yu-Bo; Chen, Ming-Hui; Kuo, Lynn; Lewis, Paul O

2018-06-01

Evaluating the marginal likelihood in Bayesian analysis is essential for model selection. Estimators based on a single Markov chain Monte Carlo sample from the posterior distribution include the harmonic mean estimator and the inflated density ratio estimator. We propose a new class of Monte Carlo estimators based on this single Markov chain Monte Carlo sample. This class can be thought of as a generalization of the harmonic mean and inflated density ratio estimators using a partition weighted kernel (likelihood times prior). We show that our estimator is consistent and has better theoretical properties than the harmonic mean and inflated density ratio estimators. In addition, we provide guidelines on choosing optimal weights. Simulation studies were conducted to examine the empirical performance of the proposed estimator. We further demonstrate the desirable features of the proposed estimator with two real data sets: one is from a prostate cancer study using an ordinal probit regression model with latent variables; the other is for the power prior construction from two Eastern Cooperative Oncology Group phase III clinical trials using the cure rate survival model with similar objectives.

Should I Text or Call Here? A Situation-Based Analysis of Drivers' Perceived Likelihood of Engaging in Mobile Phone Multitasking.

PubMed

Oviedo-Trespalacios, Oscar; Haque, Md Mazharul; King, Mark; Washington, Simon

2018-05-29

This study investigated how situational characteristics typically encountered in the transport system influence drivers' perceived likelihood of engaging in mobile phone multitasking. The impacts of mobile phone tasks, perceived environmental complexity/risk, and drivers' individual differences were evaluated as relevant individual predictors within the behavioral adaptation framework. An innovative questionnaire, which includes randomized textual and visual scenarios, was administered to collect data from a sample of 447 drivers in South East Queensland-Australia (66% females; n = 296). The likelihood of engaging in a mobile phone task across various scenarios was modeled by a random parameters ordered probit model. Results indicated that drivers who are female, are frequent users of phones for texting/answering calls, have less favorable attitudes towards safety, and are highly disinhibited were more likely to report stronger intentions of engaging in mobile phone multitasking. However, more years with a valid driving license, self-efficacy toward self-regulation in demanding traffic conditions and police enforcement, texting tasks, and demanding traffic conditions were negatively related to self-reported likelihood of mobile phone multitasking. The unobserved heterogeneity warned of riskier groups among female drivers and participants who need a lot of convincing to believe that multitasking while driving is dangerous. This research concludes that behavioral adaptation theory is a robust framework explaining self-regulation of distracted drivers. © 2018 Society for Risk Analysis.

Maximum likelihood-based analysis of single-molecule photon arrival trajectories

NASA Astrophysics Data System (ADS)

Hajdziona, Marta; Molski, Andrzej

2011-02-01

In this work we explore the statistical properties of the maximum likelihood-based analysis of one-color photon arrival trajectories. This approach does not involve binning and, therefore, all of the information contained in an observed photon strajectory is used. We study the accuracy and precision of parameter estimates and the efficiency of the Akaike information criterion and the Bayesian information criterion (BIC) in selecting the true kinetic model. We focus on the low excitation regime where photon trajectories can be modeled as realizations of Markov modulated Poisson processes. The number of observed photons is the key parameter in determining model selection and parameter estimation. For example, the BIC can select the true three-state model from competing two-, three-, and four-state kinetic models even for relatively short trajectories made up of 2 × 103 photons. When the intensity levels are well-separated and 104 photons are observed, the two-state model parameters can be estimated with about 10% precision and those for a three-state model with about 20% precision.

A Molecular Phylogeny of the Chalcidoidea (Hymenoptera)

PubMed Central

Munro, James B.; Heraty, John M.; Burks, Roger A.; Hawks, David; Mottern, Jason; Cruaud, Astrid; Rasplus, Jean-Yves; Jansta, Petr

2011-01-01

Chalcidoidea (Hymenoptera) are extremely diverse with more than 23,000 species described and over 500,000 species estimated to exist. This is the first comprehensive phylogenetic analysis of the superfamily based on a molecular analysis of 18S and 28S ribosomal gene regions for 19 families, 72 subfamilies, 343 genera and 649 species. The 56 outgroups are comprised of Ceraphronoidea and most proctotrupomorph families, including Mymarommatidae. Data alignment and the impact of ambiguous regions are explored using a secondary structure analysis and automated (MAFFT) alignments of the core and pairing regions and regions of ambiguous alignment. Both likelihood and parsimony approaches are used to analyze the data. Overall there is no impact of alignment method, and few but substantial differences between likelihood and parsimony approaches. Monophyly of Chalcidoidea and a sister group relationship between Mymaridae and the remaining Chalcidoidea is strongly supported in all analyses. Either Mymarommatoidea or Diaprioidea are the sister group of Chalcidoidea depending on the analysis. Likelihood analyses place Rotoitidae as the sister group of the remaining Chalcidoidea after Mymaridae, whereas parsimony nests them within Chalcidoidea. Some traditional family groups are supported as monophyletic (Agaonidae, Eucharitidae, Encyrtidae, Eulophidae, Leucospidae, Mymaridae, Ormyridae, Signiphoridae, Tanaostigmatidae and Trichogrammatidae). Several other families are paraphyletic (Perilampidae) or polyphyletic (Aphelinidae, Chalcididae, Eupelmidae, Eurytomidae, Pteromalidae, Tetracampidae and Torymidae). Evolutionary scenarios discussed for Chalcidoidea include the evolution of phytophagy, egg parasitism, sternorrhynchan parasitism, hypermetamorphic development and heteronomy. PMID:22087244

Empirical likelihood based detection procedure for change point in mean residual life functions under random censorship.

PubMed

Chen, Ying-Ju; Ning, Wei; Gupta, Arjun K

2016-05-01

The mean residual life (MRL) function is one of the basic parameters of interest in survival analysis that describes the expected remaining time of an individual after a certain age. The study of changes in the MRL function is practical and interesting because it may help us to identify some factors such as age and gender that may influence the remaining lifetimes of patients after receiving a certain surgery. In this paper, we propose a detection procedure based on the empirical likelihood for the changes in MRL functions with right censored data. Two real examples are also given: Veterans' administration lung cancer study and Stanford heart transplant to illustrate the detecting procedure. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Generalized Likelihood Uncertainty Estimation (GLUE) Using Multi-Optimization Algorithm as Sampling Method

NASA Astrophysics Data System (ADS)

Wang, Z.

2015-12-01

For decades, distributed and lumped hydrological models have furthered our understanding of hydrological system. The development of hydrological simulation in large scale and high precision elaborated the spatial descriptions and hydrological behaviors. Meanwhile, the new trend is also followed by the increment of model complexity and number of parameters, which brings new challenges of uncertainty quantification. Generalized Likelihood Uncertainty Estimation (GLUE) has been widely used in uncertainty analysis for hydrological models referring to Monte Carlo method coupled with Bayesian estimation. However, the stochastic sampling method of prior parameters adopted by GLUE appears inefficient, especially in high dimensional parameter space. The heuristic optimization algorithms utilizing iterative evolution show better convergence speed and optimality-searching performance. In light of the features of heuristic optimization algorithms, this study adopted genetic algorithm, differential evolution, shuffled complex evolving algorithm to search the parameter space and obtain the parameter sets of large likelihoods. Based on the multi-algorithm sampling, hydrological model uncertainty analysis is conducted by the typical GLUE framework. To demonstrate the superiority of the new method, two hydrological models of different complexity are examined. The results shows the adaptive method tends to be efficient in sampling and effective in uncertainty analysis, providing an alternative path for uncertainty quantilization.

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

PubMed Central

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain–computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method. PMID:28558002

A tree island approach to inferring phylogeny in the ant subfamily Formicinae, with especial reference to the evolution of weaving.

PubMed

Johnson, Rebecca N; Agapow, Paul-Michael; Crozier, Ross H

2003-11-01

The ant subfamily Formicinae is a large assemblage (2458 species (J. Nat. Hist. 29 (1995) 1037), including species that weave leaf nests together with larval silk and in which the metapleural gland-the ancestrally defining ant character-has been secondarily lost. We used sequences from two mitochondrial genes (cytochrome b and cytochrome oxidase 2) from 18 formicine and 4 outgroup taxa to derive a robust phylogeny, employing a search for tree islands using 10000 randomly constructed trees as starting points and deriving a maximum likelihood consensus tree from the ML tree and those not significantly different from it. Non-parametric bootstrapping showed that the ML consensus tree fit the data significantly better than three scenarios based on morphology, with that of Bolton (Identification Guide to the Ant Genera of the World, Harvard University Press, Cambridge, MA) being the best among these alternative trees. Trait mapping showed that weaving had arisen at least four times and possibly been lost once. A maximum likelihood analysis showed that loss of the metapleural gland is significantly associated with the weaver life-pattern. The graph of the frequencies with which trees were discovered versus their likelihood indicates that trees with high likelihoods have much larger basins of attraction than those with lower likelihoods. While this result indicates that single searches are more likely to find high- than low-likelihood tree islands, it also indicates that searching only for the single best tree may lose important information.

Modeling gene expression measurement error: a quasi-likelihood approach

PubMed Central

Strimmer, Korbinian

2003-01-01

Background Using suitable error models for gene expression measurements is essential in the statistical analysis of microarray data. However, the true probabilistic model underlying gene expression intensity readings is generally not known. Instead, in currently used approaches some simple parametric model is assumed (usually a transformed normal distribution) or the empirical distribution is estimated. However, both these strategies may not be optimal for gene expression data, as the non-parametric approach ignores known structural information whereas the fully parametric models run the risk of misspecification. A further related problem is the choice of a suitable scale for the model (e.g. observed vs. log-scale). Results Here a simple semi-parametric model for gene expression measurement error is presented. In this approach inference is based an approximate likelihood function (the extended quasi-likelihood). Only partial knowledge about the unknown true distribution is required to construct this function. In case of gene expression this information is available in the form of the postulated (e.g. quadratic) variance structure of the data. As the quasi-likelihood behaves (almost) like a proper likelihood, it allows for the estimation of calibration and variance parameters, and it is also straightforward to obtain corresponding approximate confidence intervals. Unlike most other frameworks, it also allows analysis on any preferred scale, i.e. both on the original linear scale as well as on a transformed scale. It can also be employed in regression approaches to model systematic (e.g. array or dye) effects. Conclusions The quasi-likelihood framework provides a simple and versatile approach to analyze gene expression data that does not make any strong distributional assumptions about the underlying error model. For several simulated as well as real data sets it provides a better fit to the data than competing models. In an example it also improved the power of tests to identify differential expression. PMID:12659637

Addressing Data Analysis Challenges in Gravitational Wave Searches Using the Particle Swarm Optimization Algorithm

NASA Astrophysics Data System (ADS)

Weerathunga, Thilina Shihan

2017-08-01

Gravitational waves are a fundamental prediction of Einstein's General Theory of Relativity. The first experimental proof of their existence was provided by the Nobel Prize winning discovery by Taylor and Hulse of orbital decay in a binary pulsar system. The first detection of gravitational waves incident on earth from an astrophysical source was announced in 2016 by the LIGO Scientific Collaboration, launching the new era of gravitational wave (GW) astronomy. The signal detected was from the merger of two black holes, which is an example of sources called Compact Binary Coalescences (CBCs). Data analysis strategies used in the search for CBC signals are derivatives of the Maximum-Likelihood (ML) method. The ML method applied to data from a network of geographically distributed GW detectors--called fully coherent network analysis--is currently the best approach for estimating source location and GW polarization waveforms. However, in the case of CBCs, especially for lower mass systems (O(1M solar masses)) such as double neutron star binaries, fully coherent network analysis is computationally expensive. The ML method requires locating the global maximum of the likelihood function over a nine dimensional parameter space, where the computation of the likelihood at each point requires correlations involving O(104) to O(106) samples between the data and the corresponding candidate signal waveform template. Approximations, such as semi-coherent coincidence searches, are currently used to circumvent the computational barrier but incur a concomitant loss in sensitivity. We explored the effectiveness of Particle Swarm Optimization (PSO), a well-known algorithm in the field of swarm intelligence, in addressing the fully coherent network analysis problem. As an example, we used a four-detector network consisting of the two LIGO detectors at Hanford and Livingston, Virgo and Kagra, all having initial LIGO noise power spectral densities, and show that PSO can locate the global maximum with less than 240,000 likelihood evaluations for a component mass range of 1.0 to 10.0 solar masses at a realistic coherent network signal to noise ratio of 9.0. Our results show that PSO can successfully deliver a fully-coherent all-sky search with < (1/10 ) the number of likelihood evaluations needed for a grid-based search. Used as a follow-up step, the savings in the number of likelihood evaluations may also reduce latency in obtaining ML estimates of source parameters in semi-coherent searches.

To Leave or Not to Leave? A Regression Discontinuity Analysis of the Impact of Failing the High School Exit Exam

ERIC Educational Resources Information Center

Ou, Dongshu

2010-01-01

The high school exit exam (HSEE) is rapidly becoming a standardized assessment procedure for educational accountability in the United States. I use a unique, state-specific dataset to identify the effects of failing the HSEE on the likelihood of dropping out of high school based on a regression discontinuity design. The analysis shows that…

A new model to predict weak-lensing peak counts. II. Parameter constraint strategies

NASA Astrophysics Data System (ADS)

Lin, Chieh-An; Kilbinger, Martin

2015-11-01

Context. Peak counts have been shown to be an excellent tool for extracting the non-Gaussian part of the weak lensing signal. Recently, we developed a fast stochastic forward model to predict weak-lensing peak counts. Our model is able to reconstruct the underlying distribution of observables for analysis. Aims: In this work, we explore and compare various strategies for constraining a parameter using our model, focusing on the matter density Ωm and the density fluctuation amplitude σ8. Methods: First, we examine the impact from the cosmological dependency of covariances (CDC). Second, we perform the analysis with the copula likelihood, a technique that makes a weaker assumption than does the Gaussian likelihood. Third, direct, non-analytic parameter estimations are applied using the full information of the distribution. Fourth, we obtain constraints with approximate Bayesian computation (ABC), an efficient, robust, and likelihood-free algorithm based on accept-reject sampling. Results: We find that neglecting the CDC effect enlarges parameter contours by 22% and that the covariance-varying copula likelihood is a very good approximation to the true likelihood. The direct techniques work well in spite of noisier contours. Concerning ABC, the iterative process converges quickly to a posterior distribution that is in excellent agreement with results from our other analyses. The time cost for ABC is reduced by two orders of magnitude. Conclusions: The stochastic nature of our weak-lensing peak count model allows us to use various techniques that approach the true underlying probability distribution of observables, without making simplifying assumptions. Our work can be generalized to other observables where forward simulations provide samples of the underlying distribution.

New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.

PubMed

Guindon, Stéphane; Dufayard, Jean-François; Lefort, Vincent; Anisimova, Maria; Hordijk, Wim; Gascuel, Olivier

2010-05-01

PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.

Discerning the clinical relevance of biomarkers in early stage breast cancer.

PubMed

Ballinger, Tarah J; Kassem, Nawal; Shen, Fei; Jiang, Guanglong; Smith, Mary Lou; Railey, Elda; Howell, John; White, Carol B; Schneider, Bryan P

2017-07-01

Prior data suggest that breast cancer patients accept significant toxicity for small benefit. It is unclear whether personalized estimations of risk or benefit likelihood that could be provided by biomarkers alter treatment decisions in the curative setting. A choice-based conjoint (CBC) survey was conducted in 417 HER2-negative breast cancer patients who received chemotherapy in the curative setting. The survey presented pairs of treatment choices derived from common taxane- and anthracycline-based regimens, varying in degree of benefit by risk of recurrence and in toxicity profile, including peripheral neuropathy (PN) and congestive heart failure (CHF). Hypothetical biomarkers shifting benefit and toxicity risk were modeled to determine whether this knowledge alters choice. Previously identified biomarkers were evaluated using this model. Based on CBC analysis, a non-anthracycline regimen was the most preferred. Patients with prior PN had a similar preference for a taxane regimen as those who were PN naïve, but more dramatically shifted preference away from taxanes when PN was described as severe/irreversible. When modeled after hypothetical biomarkers, as the likelihood of PN increased, the preference for taxane-containing regimens decreased; similarly, as the likelihood of CHF increased, the preference for anthracycline regimens decreased. When evaluating validated biomarkers for PN and CHF, this knowledge did alter regimen preference. Patients faced with multi-faceted decisions consider personal experience and perceived risk of recurrent disease. Biomarkers providing information on likelihood of toxicity risk do influence treatment choices, and patients may accept reduced benefit when faced with higher risk of toxicity in the curative setting.

Transactional sex risk across a typology of rural and urban female sex workers in Indonesia: a mixed methods study.

PubMed

Puradiredja, Dewi Ismajani; Coast, Ernestina

2012-01-01

Context-specific typologies of female sex workers (FSWs) are essential for the design of HIV intervention programming. This study develops a novel FSW typology for the analysis of transactional sex risk in rural and urban settings in Indonesia. Mixed methods include a survey of rural and urban FSWs (n=310), in-depth interviews (n=11), key informant interviews (n=5) and ethnographic assessments. Thematic analysis categorises FSWs into 5 distinct groups based on geographical location of their sex work settings, place of solicitation, and whether sex work is their primary occupation. Multiple regression analysis shows that the likelihood of consistent condom use was higher among urban venue-based FSWs for whom sex work is not the only source of income than for any of the other rural and urban FSW groups. This effect was explained by the significantly lower likelihood of consistent condom use by rural venue-based FSWs (adjusted OR: 0.34 95% CI 0.13-0.90, p=0.029). The FSW typology and differences in organisational features and social dynamics are more closely related to the risk of unprotected transactional sex, than levels of condom awareness and availability. Interventions need context-specific strategies to reach the different FSWs identified by this study's typology.

Density-based empirical likelihood procedures for testing symmetry of data distributions and K-sample comparisons.

PubMed

Vexler, Albert; Tanajian, Hovig; Hutson, Alan D

In practice, parametric likelihood-ratio techniques are powerful statistical tools. In this article, we propose and examine novel and simple distribution-free test statistics that efficiently approximate parametric likelihood ratios to analyze and compare distributions of K groups of observations. Using the density-based empirical likelihood methodology, we develop a Stata package that applies to a test for symmetry of data distributions and compares K -sample distributions. Recognizing that recent statistical software packages do not sufficiently address K -sample nonparametric comparisons of data distributions, we propose a new Stata command, vxdbel, to execute exact density-based empirical likelihood-ratio tests using K samples. To calculate p -values of the proposed tests, we use the following methods: 1) a classical technique based on Monte Carlo p -value evaluations; 2) an interpolation technique based on tabulated critical values; and 3) a new hybrid technique that combines methods 1 and 2. The third, cutting-edge method is shown to be very efficient in the context of exact-test p -value computations. This Bayesian-type method considers tabulated critical values as prior information and Monte Carlo generations of test statistic values as data used to depict the likelihood function. In this case, a nonparametric Bayesian method is proposed to compute critical values of exact tests.

Two new methods to fit models for network meta-analysis with random inconsistency effects.

PubMed

Law, Martin; Jackson, Dan; Turner, Rebecca; Rhodes, Kirsty; Viechtbauer, Wolfgang

2016-07-28

Meta-analysis is a valuable tool for combining evidence from multiple studies. Network meta-analysis is becoming more widely used as a means to compare multiple treatments in the same analysis. However, a network meta-analysis may exhibit inconsistency, whereby the treatment effect estimates do not agree across all trial designs, even after taking between-study heterogeneity into account. We propose two new estimation methods for network meta-analysis models with random inconsistency effects. The model we consider is an extension of the conventional random-effects model for meta-analysis to the network meta-analysis setting and allows for potential inconsistency using random inconsistency effects. Our first new estimation method uses a Bayesian framework with empirically-based prior distributions for both the heterogeneity and the inconsistency variances. We fit the model using importance sampling and thereby avoid some of the difficulties that might be associated with using Markov Chain Monte Carlo (MCMC). However, we confirm the accuracy of our importance sampling method by comparing the results to those obtained using MCMC as the gold standard. The second new estimation method we describe uses a likelihood-based approach, implemented in the metafor package, which can be used to obtain (restricted) maximum-likelihood estimates of the model parameters and profile likelihood confidence intervals of the variance components. We illustrate the application of the methods using two contrasting examples. The first uses all-cause mortality as an outcome, and shows little evidence of between-study heterogeneity or inconsistency. The second uses "ear discharge" as an outcome, and exhibits substantial between-study heterogeneity and inconsistency. Both new estimation methods give results similar to those obtained using MCMC. The extent of heterogeneity and inconsistency should be assessed and reported in any network meta-analysis. Our two new methods can be used to fit models for network meta-analysis with random inconsistency effects. They are easily implemented using the accompanying R code in the Additional file 1. Using these estimation methods, the extent of inconsistency can be assessed and reported.

What Do the Stats Tell Us? Engaging Elementary Children in Probabilistic Reasoning Based on Data Analysis

ERIC Educational Resources Information Center

Hourigan, Mairéad; Leavy, Aisling

2016-01-01

As part of Japanese Lesson study research focusing on "comparing and describing likelihoods", fifth grade elementary students used real-world data in decision-making. Sporting statistics facilitated opportunities for informal inference, where data were used to make and justify predictions.

Likelihood-Based Clustering of Meta-Analytic SROC Curves

ERIC Educational Resources Information Center

Holling, Heinz; Bohning, Walailuck; Bohning, Dankmar

2012-01-01

Meta-analysis of diagnostic studies experience the common problem that different studies might not be comparable since they have been using a different cut-off value for the continuous or ordered categorical diagnostic test value defining different regions for which the diagnostic test is defined to be positive. Hence specificities and…

Chemical Safety Information, Site Security and Fuels Regulatory Relief Act: Public Distribution of Off-Site Consequence Analysis Information Fact Sheet

EPA Pesticide Factsheets

Based on assessments of increased risk of terrorist/criminal activity, EPA and DOJ have issued a rule that allows public access to OCA information in ways that are designed to minimize likelihood of chemical accidents and public harm.

Local Influence Analysis of Nonlinear Structural Equation Models

ERIC Educational Resources Information Center

Lee, Sik-Yum; Tang, Nian-Sheng

2004-01-01

By regarding the latent random vectors as hypothetical missing data and based on the conditional expectation of the complete-data log-likelihood function in the EM algorithm, we investigate assessment of local influence of various perturbation schemes in a nonlinear structural equation model. The basic building blocks of local influence analysis…

Models and analysis for multivariate failure time data

NASA Astrophysics Data System (ADS)

Shih, Joanna Huang

The goal of this research is to develop and investigate models and analytic methods for multivariate failure time data. We compare models in terms of direct modeling of the margins, flexibility of dependency structure, local vs. global measures of association, and ease of implementation. In particular, we study copula models, and models produced by right neutral cumulative hazard functions and right neutral hazard functions. We examine the changes of association over time for families of bivariate distributions induced from these models by displaying their density contour plots, conditional density plots, correlation curves of Doksum et al, and local cross ratios of Oakes. We know that bivariate distributions with same margins might exhibit quite different dependency structures. In addition to modeling, we study estimation procedures. For copula models, we investigate three estimation procedures. the first procedure is full maximum likelihood. The second procedure is two-stage maximum likelihood. At stage 1, we estimate the parameters in the margins by maximizing the marginal likelihood. At stage 2, we estimate the dependency structure by fixing the margins at the estimated ones. The third procedure is two-stage partially parametric maximum likelihood. It is similar to the second procedure, but we estimate the margins by the Kaplan-Meier estimate. We derive asymptotic properties for these three estimation procedures and compare their efficiency by Monte-Carlo simulations and direct computations. For models produced by right neutral cumulative hazards and right neutral hazards, we derive the likelihood and investigate the properties of the maximum likelihood estimates. Finally, we develop goodness of fit tests for the dependency structure in the copula models. We derive a test statistic and its asymptotic properties based on the test of homogeneity of Zelterman and Chen (1988), and a graphical diagnostic procedure based on the empirical Bayes approach. We study the performance of these two methods using actual and computer generated data.

Silence That Can Be Dangerous: A Vignette Study to Assess Healthcare Professionals’ Likelihood of Speaking up about Safety Concerns

PubMed Central

Schwappach, David L. B.; Gehring, Katrin

2014-01-01

Purpose To investigate the likelihood of speaking up about patient safety in oncology and to clarify the effect of clinical and situational context factors on the likelihood of voicing concerns. Patients and Methods 1013 nurses and doctors in oncology rated four clinical vignettes describing coworkers’ errors and rule violations in a self-administered factorial survey (65% response rate). Multiple regression analysis was used to model the likelihood of speaking up as outcome of vignette attributes, responder’s evaluations of the situation and personal characteristics. Results Respondents reported a high likelihood of speaking up about patient safety but the variation between and within types of errors and rule violations was substantial. Staff without managerial function provided significantly higher levels of decision difficulty and discomfort to speak up. Based on the information presented in the vignettes, 74%−96% would speak up towards a supervisor failing to check a prescription, 45%−81% would point a coworker to a missed hand disinfection, 82%−94% would speak up towards nurses who violate a safety rule in medication preparation, and 59%−92% would question a doctor violating a safety rule in lumbar puncture. Several vignette attributes predicted the likelihood of speaking up. Perceived potential harm, anticipated discomfort, and decision difficulty were significant predictors of the likelihood of speaking up. Conclusions Clinicians’ willingness to speak up about patient safety is considerably affected by contextual factors. Physicians and nurses without managerial function report substantial discomfort with speaking up. Oncology departments should provide staff with clear guidance and trainings on when and how to voice safety concerns. PMID:25116338

Nature or nurture: the effect of undergraduate rural clinical rotations on pre-existent rural career choice likelihood as measured by the SOMERS Index.

PubMed

Somers, George T; Spencer, Ryan J

2012-04-01

Do undergraduate rural clinical rotations increase the likelihood of medical students to choose a rural career once pre-existent likelihood is accounted for? A prospective, controlled quasi-experiment using self-paired scores on the SOMERS Index of rural career choice likelihood, before and after 3 years of clinical rotations in either mainly rural or mainly urban locations. Monash University medical school, Australia. Fifty-eight undergraduate-entry medical students (35% of the 2002 entry class). The SOMERS Index of rural career choice likelihood and its component indicators. There was an overall decline in SOMERS Index score (22%) and in each of its components (12-41%). Graduating students who attended rural rotations were more likely to choose a rural career on graduation (difference in SOMERS score: 24.1 (95% CI, 15.0-33.3) P<0.0001); however, at entry, students choosing rural rotations had an even greater SOMERS score (difference: 27.1 (95% CI, 18.2-36.1) P<0.0001). Self-paired pre-post reductions in likelihood were not affected by attending mainly rural or urban rotations, nor were there differences based on rural background alone or sex. While rural rotations are an important component of undergraduate medical training, it is the nature of the students choosing to study in rural locations rather than experiences during the course that is the greater influence on rural career choice. In order to improve the rural medical workforce crisis, medical schools should attract more students with pre-existent likelihood to choose a rural career. The SOMERS Index was found to be a useful tool for this quantitative analysis. © 2012 The Authors. Australian Journal of Rural Health © 2012 National Rural Health Alliance Inc.

Basal jawed vertebrate phylogeny inferred from multiple nuclear DNA-coded genes

PubMed Central

Kikugawa, Kanae; Katoh, Kazutaka; Kuraku, Shigehiro; Sakurai, Hiroshi; Ishida, Osamu; Iwabe, Naoyuki; Miyata, Takashi

2004-01-01

Background Phylogenetic analyses of jawed vertebrates based on mitochondrial sequences often result in confusing inferences which are obviously inconsistent with generally accepted trees. In particular, in a hypothesis by Rasmussen and Arnason based on mitochondrial trees, cartilaginous fishes have a terminal position in a paraphyletic cluster of bony fishes. No previous analysis based on nuclear DNA-coded genes could significantly reject the mitochondrial trees of jawed vertebrates. Results We have cloned and sequenced seven nuclear DNA-coded genes from 13 vertebrate species. These sequences, together with sequences available from databases including 13 jawed vertebrates from eight major groups (cartilaginous fishes, bichir, chondrosteans, gar, bowfin, teleost fishes, lungfishes and tetrapods) and an outgroup (a cyclostome and a lancelet), have been subjected to phylogenetic analyses based on the maximum likelihood method. Conclusion Cartilaginous fishes have been inferred to be basal to other jawed vertebrates, which is consistent with the generally accepted view. The minimum log-likelihood difference between the maximum likelihood tree and trees not supporting the basal position of cartilaginous fishes is 18.3 ± 13.1. The hypothesis by Rasmussen and Arnason has been significantly rejected with the minimum log-likelihood difference of 123 ± 23.3. Our tree has also shown that living holosteans, comprising bowfin and gar, form a monophyletic group which is the sister group to teleost fishes. This is consistent with a formerly prevalent view of vertebrate classification, although inconsistent with both of the current morphology-based and mitochondrial sequence-based trees. Furthermore, the bichir has been shown to be the basal ray-finned fish. Tetrapods and lungfish have formed a monophyletic cluster in the tree inferred from the concatenated alignment, being consistent with the currently prevalent view. It also remains possible that tetrapods are more closely related to ray-finned fishes than to lungfishes. PMID:15070407

Species delimitation using Bayes factors: simulations and application to the Sceloporus scalaris species group (Squamata: Phrynosomatidae).

PubMed

Grummer, Jared A; Bryson, Robert W; Reeder, Tod W

2014-03-01

Current molecular methods of species delimitation are limited by the types of species delimitation models and scenarios that can be tested. Bayes factors allow for more flexibility in testing non-nested species delimitation models and hypotheses of individual assignment to alternative lineages. Here, we examined the efficacy of Bayes factors in delimiting species through simulations and empirical data from the Sceloporus scalaris species group. Marginal-likelihood scores of competing species delimitation models, from which Bayes factor values were compared, were estimated with four different methods: harmonic mean estimation (HME), smoothed harmonic mean estimation (sHME), path-sampling/thermodynamic integration (PS), and stepping-stone (SS) analysis. We also performed model selection using a posterior simulation-based analog of the Akaike information criterion through Markov chain Monte Carlo analysis (AICM). Bayes factor species delimitation results from the empirical data were then compared with results from the reversible-jump MCMC (rjMCMC) coalescent-based species delimitation method Bayesian Phylogenetics and Phylogeography (BP&P). Simulation results show that HME and sHME perform poorly compared with PS and SS marginal-likelihood estimators when identifying the true species delimitation model. Furthermore, Bayes factor delimitation (BFD) of species showed improved performance when species limits are tested by reassigning individuals between species, as opposed to either lumping or splitting lineages. In the empirical data, BFD through PS and SS analyses, as well as the rjMCMC method, each provide support for the recognition of all scalaris group taxa as independent evolutionary lineages. Bayes factor species delimitation and BP&P also support the recognition of three previously undescribed lineages. In both simulated and empirical data sets, harmonic and smoothed harmonic mean marginal-likelihood estimators provided much higher marginal-likelihood estimates than PS and SS estimators. The AICM displayed poor repeatability in both simulated and empirical data sets, and produced inconsistent model rankings across replicate runs with the empirical data. Our results suggest that species delimitation through the use of Bayes factors with marginal-likelihood estimates via PS or SS analyses provide a useful and complementary alternative to existing species delimitation methods.

Factors associated with the hospital admission of consumer product-related injuries treated in U.S. hospital emergency departments.

PubMed

Schroeder, Thomas J; Rodgers, Gregory B

2013-10-01

While unintentional injuries and hazard patterns involving consumer products have been studied extensively in recent years, little attention has focused on the characteristics of those who are hospitalized after treatment in emergency departments, as opposed to those treated and released. This study quantifies the impact of the age and sex of the injury victims, and other factors, on the likelihood of hospitalization. The analysis focuses on consumer product injuries, and was based on approximately 400,000 injury cases reported through the U.S. Consumer Product Safety Commission's National Electronic Injury Surveillance System, a national probability sample of U.S. hospital emergency departments. Logistic regression was used to quantify the factors associated with the likelihood of hospitalization. The analysis suggests a smooth U-shaped relationship between the age of the victim and the likelihood of hospitalization, declining from about 3.4% for children under age 5 years to 1.9% for 15-24 year-olds, but then rising to more than 25% for those ages 75 years and older. The likelihood of hospitalization was also significantly affected by the victim's sex, as well as by the types of products involved, fire involvement, and the size and type of hospital at which the injury was treated. This study shows that the probability of hospitalization is strongly correlated with the characteristics of those who are injured, as well as other factors. Published by Elsevier Ltd.

Cross-validation to select Bayesian hierarchical models in phylogenetics.

PubMed

Duchêne, Sebastián; Duchêne, David A; Di Giallonardo, Francesca; Eden, John-Sebastian; Geoghegan, Jemma L; Holt, Kathryn E; Ho, Simon Y W; Holmes, Edward C

2016-05-26

Recent developments in Bayesian phylogenetic models have increased the range of inferences that can be drawn from molecular sequence data. Accordingly, model selection has become an important component of phylogenetic analysis. Methods of model selection generally consider the likelihood of the data under the model in question. In the context of Bayesian phylogenetics, the most common approach involves estimating the marginal likelihood, which is typically done by integrating the likelihood across model parameters, weighted by the prior. Although this method is accurate, it is sensitive to the presence of improper priors. We explored an alternative approach based on cross-validation that is widely used in evolutionary analysis. This involves comparing models according to their predictive performance. We analysed simulated data and a range of viral and bacterial data sets using a cross-validation approach to compare a variety of molecular clock and demographic models. Our results show that cross-validation can be effective in distinguishing between strict- and relaxed-clock models and in identifying demographic models that allow growth in population size over time. In most of our empirical data analyses, the model selected using cross-validation was able to match that selected using marginal-likelihood estimation. The accuracy of cross-validation appears to improve with longer sequence data, particularly when distinguishing between relaxed-clock models. Cross-validation is a useful method for Bayesian phylogenetic model selection. This method can be readily implemented even when considering complex models where selecting an appropriate prior for all parameters may be difficult.

Integral equation methods for computing likelihoods and their derivatives in the stochastic integrate-and-fire model.

PubMed

Paninski, Liam; Haith, Adrian; Szirtes, Gabor

2008-02-01

We recently introduced likelihood-based methods for fitting stochastic integrate-and-fire models to spike train data. The key component of this method involves the likelihood that the model will emit a spike at a given time t. Computing this likelihood is equivalent to computing a Markov first passage time density (the probability that the model voltage crosses threshold for the first time at time t). Here we detail an improved method for computing this likelihood, based on solving a certain integral equation. This integral equation method has several advantages over the techniques discussed in our previous work: in particular, the new method has fewer free parameters and is easily differentiable (for gradient computations). The new method is also easily adaptable for the case in which the model conductance, not just the input current, is time-varying. Finally, we describe how to incorporate large deviations approximations to very small likelihoods.

New robust statistical procedures for the polytomous logistic regression models.

PubMed

Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro

2018-05-17

This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.

A Meta-Analysis of Disparities in Childhood Sexual Abuse, Parental Physical Abuse, and Peer Victimization Among Sexual Minority and Sexual Nonminority Individuals

PubMed Central

Marshal, Michael P.; Guadamuz, Thomas E.; Wei, Chongyi; Wong, Carolyn F.; Saewyc, Elizabeth; Stall, Ron

2011-01-01

Objectives. We compared the likelihood of childhood sexual abuse (under age 18), parental physical abuse, and peer victimization based on sexual orientation. Methods. We conducted a meta-analysis of adolescent school-based studies that compared the likelihood of childhood abuse among sexual minorities vs sexual nonminorities. Results. Sexual minority individuals were on average 3.8, 1.2, 1.7, and 2.4 times more likely to experience sexual abuse, parental physical abuse, or assault at school or to miss school through fear, respectively. Moderation analysis showed that disparities between sexual minority and sexual nonminority individuals were larger for (1) males than females for sexual abuse, (2) females than males for assault at school, and (3) bisexual than gay and lesbian for both parental physical abuse and missing school through fear. Disparities did not change between the 1990s and the 2000s. Conclusions. The higher rates of abuse experienced by sexual minority youths may be one of the driving mechanisms underlying higher rates of mental health problems, substance use, risky sexual behavior, and HIV reported by sexual minority adults. PMID:21680921

Fault and event tree analyses for process systems risk analysis: uncertainty handling formulations.

PubMed

Ferdous, Refaul; Khan, Faisal; Sadiq, Rehan; Amyotte, Paul; Veitch, Brian

2011-01-01

Quantitative risk analysis (QRA) is a systematic approach for evaluating likelihood, consequences, and risk of adverse events. QRA based on event (ETA) and fault tree analyses (FTA) employs two basic assumptions. The first assumption is related to likelihood values of input events, and the second assumption is regarding interdependence among the events (for ETA) or basic events (for FTA). Traditionally, FTA and ETA both use crisp probabilities; however, to deal with uncertainties, the probability distributions of input event likelihoods are assumed. These probability distributions are often hard to come by and even if available, they are subject to incompleteness (partial ignorance) and imprecision. Furthermore, both FTA and ETA assume that events (or basic events) are independent. In practice, these two assumptions are often unrealistic. This article focuses on handling uncertainty in a QRA framework of a process system. Fuzzy set theory and evidence theory are used to describe the uncertainties in the input event likelihoods. A method based on a dependency coefficient is used to express interdependencies of events (or basic events) in ETA and FTA. To demonstrate the approach, two case studies are discussed. © 2010 Society for Risk Analysis.

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.

An Analysis of Factor Extraction Strategies: A Comparison of the Relative Strengths of Principal Axis, Ordinary Least Squares, and Maximum Likelihood in Research Contexts That Include Both Categorical and Continuous Variables

ERIC Educational Resources Information Center

Coughlin, Kevin B.

2013-01-01

This study is intended to provide researchers with empirically derived guidelines for conducting factor analytic studies in research contexts that include dichotomous and continuous levels of measurement. This study is based on the hypotheses that ordinary least squares (OLS) factor analysis will yield more accurate parameter estimates than…

Important factors in the maximum likelihood analysis of flight test maneuvers

NASA Technical Reports Server (NTRS)

Iliff, K. W.; Maine, R. E.; Montgomery, T. D.

1979-01-01

The information presented is based on the experience in the past 12 years at the NASA Dryden Flight Research Center of estimating stability and control derivatives from over 3500 maneuvers from 32 aircraft. The overall approach to the analysis of dynamic flight test data is outlined. General requirements for data and instrumentation are discussed and several examples of the types of problems that may be encountered are presented.

Survival analysis of clinical mastitis data using a nested frailty Cox model fit as a mixed-effects Poisson model.

PubMed

Elghafghuf, Adel; Dufour, Simon; Reyher, Kristen; Dohoo, Ian; Stryhn, Henrik

2014-12-01

Mastitis is a complex disease affecting dairy cows and is considered to be the most costly disease of dairy herds. The hazard of mastitis is a function of many factors, both managerial and environmental, making its control a difficult issue to milk producers. Observational studies of clinical mastitis (CM) often generate datasets with a number of characteristics which influence the analysis of those data: the outcome of interest may be the time to occurrence of a case of mastitis, predictors may change over time (time-dependent predictors), the effects of factors may change over time (time-dependent effects), there are usually multiple hierarchical levels, and datasets may be very large. Analysis of such data often requires expansion of the data into the counting-process format - leading to larger datasets - thus complicating the analysis and requiring excessive computing time. In this study, a nested frailty Cox model with time-dependent predictors and effects was applied to Canadian Bovine Mastitis Research Network data in which 10,831 lactations of 8035 cows from 69 herds were followed through lactation until the first occurrence of CM. The model was fit to the data as a Poisson model with nested normally distributed random effects at the cow and herd levels. Risk factors associated with the hazard of CM during the lactation were identified, such as parity, calving season, herd somatic cell score, pasture access, fore-stripping, and proportion of treated cases of CM in a herd. The analysis showed that most of the predictors had a strong effect early in lactation and also demonstrated substantial variation in the baseline hazard among cows and between herds. A small simulation study for a setting similar to the real data was conducted to evaluate the Poisson maximum likelihood estimation approach with both Gaussian quadrature method and Laplace approximation. Further, the performance of the two methods was compared with the performance of a widely used estimation approach for frailty Cox models based on the penalized partial likelihood. The simulation study showed good performance for the Poisson maximum likelihood approach with Gaussian quadrature and biased variance component estimates for both the Poisson maximum likelihood with Laplace approximation and penalized partial likelihood approaches. Copyright © 2014. Published by Elsevier B.V.

PAMLX: a graphical user interface for PAML.

PubMed

Xu, Bo; Yang, Ziheng

2013-12-01

This note announces pamlX, a graphical user interface/front end for the paml (for Phylogenetic Analysis by Maximum Likelihood) program package (Yang Z. 1997. PAML: a program package for phylogenetic analysis by maximum likelihood. Comput Appl Biosci. 13:555-556; Yang Z. 2007. PAML 4: Phylogenetic analysis by maximum likelihood. Mol Biol Evol. 24:1586-1591). pamlX is written in C++ using the Qt library and communicates with paml programs through files. It can be used to create, edit, and print control files for paml programs and to launch paml runs. The interface is available for free download at http://abacus.gene.ucl.ac.uk/software/paml.html.

Resolving the Effects of Maternal and Offspring Genotype on Dyadic Outcomes in Genome Wide Complex Trait Analysis (“M-GCTA”)

PubMed Central

Pourcain, Beate St.; Smith, George Davey; York, Timothy P.; Evans, David M.

2014-01-01

Genome wide complex trait analysis (GCTA) is extended to include environmental effects of the maternal genotype on offspring phenotype (“maternal effects”, M-GCTA). The model includes parameters for the direct effects of the offspring genotype, maternal effects and the covariance between direct and maternal effects. Analysis of simulated data, conducted in OpenMx, confirmed that model parameters could be recovered by full information maximum likelihood (FIML) and evaluated the biases that arise in conventional GCTA when indirect genetic effects are ignored. Estimates derived from FIML in OpenMx showed very close agreement to those obtained by restricted maximum likelihood using the published algorithm for GCTA. The method was also applied to illustrative perinatal phenotypes from ∼4,000 mother-offspring pairs from the Avon Longitudinal Study of Parents and Children. The relative merits of extended GCTA in contrast to quantitative genetic approaches based on analyzing the phenotypic covariance structure of kinships are considered. PMID:25060210

Cramer-Rao bound analysis of wideband source localization and DOA estimation

NASA Astrophysics Data System (ADS)

Yip, Lean; Chen, Joe C.; Hudson, Ralph E.; Yao, Kung

2002-12-01

In this paper, we derive the Cramér-Rao Bound (CRB) for wideband source localization and DOA estimation. The resulting CRB formula can be decomposed into two terms: one that depends on the signal characteristic and one that depends on the array geometry. For a uniformly spaced circular array (UCA), a concise analytical form of the CRB can be given by using some algebraic approximation. We further define a DOA beamwidth based on the resulting CRB formula. The DOA beamwidth can be used to design the sampling angular spacing for the Maximum-likelihood (ML) algorithm. For a randomly distributed array, we use an elliptical model to determine the largest and smallest effective beamwidth. The effective beamwidth and the CRB analysis of source localization allow us to design an efficient algorithm for the ML estimator. Finally, our simulation results of the Approximated Maximum Likelihood (AML) algorithm are demonstrated to match well to the CRB analysis at high SNR.

Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states

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

Wu, Hao; Mey, Antonia S. J. S.; Noé, Frank

2014-12-07

We propose a discrete transition-based reweighting analysis method (dTRAM) for analyzing configuration-space-discretized simulation trajectories produced at different thermodynamic states (temperatures, Hamiltonians, etc.) dTRAM provides maximum-likelihood estimates of stationary quantities (probabilities, free energies, expectation values) at any thermodynamic state. In contrast to the weighted histogram analysis method (WHAM), dTRAM does not require data to be sampled from global equilibrium, and can thus produce superior estimates for enhanced sampling data such as parallel/simulated tempering, replica exchange, umbrella sampling, or metadynamics. In addition, dTRAM provides optimal estimates of Markov state models (MSMs) from the discretized state-space trajectories at all thermodynamic states. Under suitablemore » conditions, these MSMs can be used to calculate kinetic quantities (e.g., rates, timescales). In the limit of a single thermodynamic state, dTRAM estimates a maximum likelihood reversible MSM, while in the limit of uncorrelated sampling data, dTRAM is identical to WHAM. dTRAM is thus a generalization to both estimators.« less

A review and comparison of Bayesian and likelihood-based inferences in beta regression and zero-or-one-inflated beta regression.

PubMed

Liu, Fang; Eugenio, Evercita C

2018-04-01

Beta regression is an increasingly popular statistical technique in medical research for modeling of outcomes that assume values in (0, 1), such as proportions and patient reported outcomes. When outcomes take values in the intervals [0,1), (0,1], or [0,1], zero-or-one-inflated beta (zoib) regression can be used. We provide a thorough review on beta regression and zoib regression in the modeling, inferential, and computational aspects via the likelihood-based and Bayesian approaches. We demonstrate the statistical and practical importance of correctly modeling the inflation at zero/one rather than ad hoc replacing them with values close to zero/one via simulation studies; the latter approach can lead to biased estimates and invalid inferences. We show via simulation studies that the likelihood-based approach is computationally faster in general than MCMC algorithms used in the Bayesian inferences, but runs the risk of non-convergence, large biases, and sensitivity to starting values in the optimization algorithm especially with clustered/correlated data, data with sparse inflation at zero and one, and data that warrant regularization of the likelihood. The disadvantages of the regular likelihood-based approach make the Bayesian approach an attractive alternative in these cases. Software packages and tools for fitting beta and zoib regressions in both the likelihood-based and Bayesian frameworks are also reviewed.

Analysis of the observed and intrinsic durations of Swift/BAT gamma-ray bursts

NASA Astrophysics Data System (ADS)

Tarnopolski, Mariusz

2016-07-01

The duration distribution of 947 GRBs observed by Swift/BAT, as well as its subsample of 347 events with measured redshift, allowing to examine the durations in both the observer and rest frames, are examined. Using a maximum log-likelihood method, mixtures of two and three standard Gaussians are fitted to each sample, and the adequate model is chosen based on the value of the difference in the log-likelihoods, Akaike information criterion and Bayesian information criterion. It is found that a two-Gaussian is a better description than a three-Gaussian, and that the presumed intermediate-duration class is unlikely to be present in the Swift duration data.

An Empirical Comparison of Heterogeneity Variance Estimators in 12,894 Meta-Analyses

ERIC Educational Resources Information Center

Langan, Dean; Higgins, Julian P. T.; Simmonds, Mark

2015-01-01

Heterogeneity in meta-analysis is most commonly estimated using a moment-based approach described by DerSimonian and Laird. However, this method has been shown to produce biased estimates. Alternative methods to estimate heterogeneity include the restricted maximum likelihood approach and those proposed by Paule and Mandel, Sidik and Jonkman, and…

AGE-RELATED DIFFERENCES IN SUSCEPTIBILITY TO CARCINOGENESIS II, APPROACHES FOR APPLICATION AND UNCERTAINTY ANALYSES FOR INDIVIDUAL GENETICALLY ACTING CARCINOGENS

EPA Science Inventory

An earlier paper (Hattis et al., 2003) developed a quantitative likelihood-based statistical analysis of the differences in apparent sensitivity of rodents to mutagenic carcinogens across three life stages (fetal, birth-weaning, and weaning-60 days) relative to exposures in adult...

PHYLOGENETIC RELATIONSHIP OF ALEXANDRIUM MONILATUM (DINOPHYCAE)TO OTHER ALEXANDRIUM SPECIES BASED ON 18S RIBOSOMAL RNA GENE SEQUENCES

EPA Science Inventory

The phylogenetic relationship of Alexandrium monilatum to other Alexandrium spp. was explored using 18S rDNA sequences. Maximum likelihood phylogenetic analysis of the combined rDNA sequences established that A. monilatum paired with Alexandrium taylori and that the pair was the ...

Maximum likelihood-based analysis of single-molecule photon arrival trajectories.

PubMed

Hajdziona, Marta; Molski, Andrzej

2011-02-07

In this work we explore the statistical properties of the maximum likelihood-based analysis of one-color photon arrival trajectories. This approach does not involve binning and, therefore, all of the information contained in an observed photon strajectory is used. We study the accuracy and precision of parameter estimates and the efficiency of the Akaike information criterion and the Bayesian information criterion (BIC) in selecting the true kinetic model. We focus on the low excitation regime where photon trajectories can be modeled as realizations of Markov modulated Poisson processes. The number of observed photons is the key parameter in determining model selection and parameter estimation. For example, the BIC can select the true three-state model from competing two-, three-, and four-state kinetic models even for relatively short trajectories made up of 2 × 10(3) photons. When the intensity levels are well-separated and 10(4) photons are observed, the two-state model parameters can be estimated with about 10% precision and those for a three-state model with about 20% precision.

A Study of Item Bias for Attitudinal Measurement Using Maximum Likelihood Factor Analysis.

ERIC Educational Resources Information Center

Mayberry, Paul W.

A technique for detecting item bias that is responsive to attitudinal measurement considerations is a maximum likelihood factor analysis procedure comparing multivariate factor structures across various subpopulations, often referred to as SIFASP. The SIFASP technique allows for factorial model comparisons in the testing of various hypotheses…

Concurrent Associations of Physical Activity and Screen-Based Sedentary Behavior on Obesity Among US Adolescents: A Latent Class Analysis.

PubMed

Kim, Youngdeok; Barreira, Tiago V; Kang, Minsoo

2016-01-01

Independent associations of physical activity (PA) and sedentary behavior (SB) with obesity are well documented. However, little is known about the combined associations of these behaviors with obesity in adolescents. The present study examines the prevalence of concurrent levels of PA and SB, and their associations with obesity among US adolescents. Data from a total of 12 081 adolescents who participated in the Youth Risk Behaviors Survey during 2012-2013 were analyzed. A latent class analysis was performed to identify latent subgroups with varying combined levels of subjectively measured PA and screen-based SB. Follow-up analysis examined the changes in the likelihood of being obese as determined by the Center for Disease Control and Prevention Growth Chart between latent subgroups. Four latent subgroups with varying combined levels of PA and SB were identified across gender. The likelihood of being obese was significantly greater for the subgroups featuring either or both Low PA or High SB when compared with High PA/Low SB across genders (odds ratio [OR] ranges, 2.1-2.7 for males and 9.6-23.5 for females). Low PA/High SB showed the greater likelihood of being obese compared to subgroups featuring either or both High PA and Low SB (OR ranges, 2.2-23.5) for female adolescents only. The findings imply that promoting sufficient levels of PA while reducing SB should be encouraged in order to reduce obesity risk among adolescents, particularly for males. The risk of obesity for female adolescents can be reduced by engaging in either high levels of PA or low levels of SB.

Application of maximum likelihood methods to laser Thomson scattering measurements of low density plasmas

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

Washeleski, Robert L.; Meyer, Edmond J. IV; King, Lyon B.

2013-10-15

Laser Thomson scattering (LTS) is an established plasma diagnostic technique that has seen recent application to low density plasmas. It is difficult to perform LTS measurements when the scattered signal is weak as a result of low electron number density, poor optical access to the plasma, or both. Photon counting methods are often implemented in order to perform measurements in these low signal conditions. However, photon counting measurements performed with photo-multiplier tubes are time consuming and multi-photon arrivals are incorrectly recorded. In order to overcome these shortcomings a new data analysis method based on maximum likelihood estimation was developed. Themore » key feature of this new data processing method is the inclusion of non-arrival events in determining the scattered Thomson signal. Maximum likelihood estimation and its application to Thomson scattering at low signal levels is presented and application of the new processing method to LTS measurements performed in the plume of a 2-kW Hall-effect thruster is discussed.« less

Application of maximum likelihood methods to laser Thomson scattering measurements of low density plasmas.

PubMed

Washeleski, Robert L; Meyer, Edmond J; King, Lyon B

2013-10-01

Laser Thomson scattering (LTS) is an established plasma diagnostic technique that has seen recent application to low density plasmas. It is difficult to perform LTS measurements when the scattered signal is weak as a result of low electron number density, poor optical access to the plasma, or both. Photon counting methods are often implemented in order to perform measurements in these low signal conditions. However, photon counting measurements performed with photo-multiplier tubes are time consuming and multi-photon arrivals are incorrectly recorded. In order to overcome these shortcomings a new data analysis method based on maximum likelihood estimation was developed. The key feature of this new data processing method is the inclusion of non-arrival events in determining the scattered Thomson signal. Maximum likelihood estimation and its application to Thomson scattering at low signal levels is presented and application of the new processing method to LTS measurements performed in the plume of a 2-kW Hall-effect thruster is discussed.

Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology

NASA Astrophysics Data System (ADS)

Alsing, Justin; Wandelt, Benjamin; Feeney, Stephen

2018-07-01

Many statistical models in cosmology can be simulated forwards but have intractable likelihood functions. Likelihood-free inference methods allow us to perform Bayesian inference from these models using only forward simulations, free from any likelihood assumptions or approximations. Likelihood-free inference generically involves simulating mock data and comparing to the observed data; this comparison in data space suffers from the curse of dimensionality and requires compression of the data to a small number of summary statistics to be tractable. In this paper, we use massive asymptotically optimal data compression to reduce the dimensionality of the data space to just one number per parameter, providing a natural and optimal framework for summary statistic choice for likelihood-free inference. Secondly, we present the first cosmological application of Density Estimation Likelihood-Free Inference (DELFI), which learns a parametrized model for joint distribution of data and parameters, yielding both the parameter posterior and the model evidence. This approach is conceptually simple, requires less tuning than traditional Approximate Bayesian Computation approaches to likelihood-free inference and can give high-fidelity posteriors from orders of magnitude fewer forward simulations. As an additional bonus, it enables parameter inference and Bayesian model comparison simultaneously. We demonstrate DELFI with massive data compression on an analysis of the joint light-curve analysis supernova data, as a simple validation case study. We show that high-fidelity posterior inference is possible for full-scale cosmological data analyses with as few as ˜104 simulations, with substantial scope for further improvement, demonstrating the scalability of likelihood-free inference to large and complex cosmological data sets.

Effect of radiance-to-reflectance transformation and atmosphere removal on maximum likelihood classification accuracy of high-dimensional remote sensing data

NASA Technical Reports Server (NTRS)

Hoffbeck, Joseph P.; Landgrebe, David A.

1994-01-01

Many analysis algorithms for high-dimensional remote sensing data require that the remotely sensed radiance spectra be transformed to approximate reflectance to allow comparison with a library of laboratory reflectance spectra. In maximum likelihood classification, however, the remotely sensed spectra are compared to training samples, thus a transformation to reflectance may or may not be helpful. The effect of several radiance-to-reflectance transformations on maximum likelihood classification accuracy is investigated in this paper. We show that the empirical line approach, LOWTRAN7, flat-field correction, single spectrum method, and internal average reflectance are all non-singular affine transformations, and that non-singular affine transformations have no effect on discriminant analysis feature extraction and maximum likelihood classification accuracy. (An affine transformation is a linear transformation with an optional offset.) Since the Atmosphere Removal Program (ATREM) and the log residue method are not affine transformations, experiments with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were conducted to determine the effect of these transformations on maximum likelihood classification accuracy. The average classification accuracy of the data transformed by ATREM and the log residue method was slightly less than the accuracy of the original radiance data. Since the radiance-to-reflectance transformations allow direct comparison of remotely sensed spectra with laboratory reflectance spectra, they can be quite useful in labeling the training samples required by maximum likelihood classification, but these transformations have only a slight effect or no effect at all on discriminant analysis and maximum likelihood classification accuracy.

Direct costs and cost-effectiveness of dual-source computed tomography and invasive coronary angiography in patients with an intermediate pretest likelihood for coronary artery disease.

PubMed

Dorenkamp, Marc; Bonaventura, Klaus; Sohns, Christian; Becker, Christoph R; Leber, Alexander W

2012-03-01

The study aims to determine the direct costs and comparative cost-effectiveness of latest-generation dual-source computed tomography (DSCT) and invasive coronary angiography for diagnosing coronary artery disease (CAD) in patients suspected of having this disease. The study was based on a previously elaborated cohort with an intermediate pretest likelihood for CAD and on complementary clinical data. Cost calculations were based on a detailed analysis of direct costs, and generally accepted accounting principles were applied. Based on Bayes' theorem, a mathematical model was used to compare the cost-effectiveness of both diagnostic approaches. Total costs included direct costs, induced costs and costs of complications. Effectiveness was defined as the ability of a diagnostic test to accurately identify a patient with CAD. Direct costs amounted to €98.60 for DSCT and to €317.75 for invasive coronary angiography. Analysis of model calculations indicated that cost-effectiveness grew hyperbolically with increasing prevalence of CAD. Given the prevalence of CAD in the study cohort (24%), DSCT was found to be more cost-effective than invasive coronary angiography (€970 vs €1354 for one patient correctly diagnosed as having CAD). At a disease prevalence of 49%, DSCT and invasive angiography were equally effective with costs of €633. Above a threshold value of disease prevalence of 55%, proceeding directly to invasive coronary angiography was more cost-effective than DSCT. With proper patient selection and consideration of disease prevalence, DSCT coronary angiography is cost-effective for diagnosing CAD in patients with an intermediate pretest likelihood for it. However, the range of eligible patients may be smaller than previously reported.

Treatment of uncertainties in the IPCC: a philosophical analysis

NASA Astrophysics Data System (ADS)

Jebeile, J.; Drouet, I.

2014-12-01

The IPCC produces scientific reports out of findings on climate and climate change. Because the findings are uncertain in many respects, the production of reports requires aggregating assessments of uncertainties of different kinds. This difficult task is currently regulated by the Guidance note for lead authors of the IPCC fifth assessment report on consistent treatment of uncertainties. The note recommends that two metrics—i.e. confidence and likelihood— be used for communicating the degree of certainty in findings. Confidence is expressed qualitatively "based on the type, amount, quality, and consistency of evidence […] and the degree of agreement", while likelihood is expressed probabilistically "based on statistical analysis of observations or model results, or expert judgment". Therefore, depending on the evidence evaluated, authors have the choice to present either an assigned level of confidence or a quantified measure of likelihood. But aggregating assessments of uncertainties of these two different kinds express distinct and conflicting methodologies. So the question arises whether the treatment of uncertainties in the IPCC is rationally justified. In order to answer the question, it is worth comparing the IPCC procedures with the formal normative theories of epistemic rationality which have been developed by philosophers. These theories—which include contributions to the philosophy of probability and to bayesian probabilistic confirmation theory—are relevant for our purpose because they are commonly used to assess the rationality of common collective jugement formation based on uncertain knowledge. In this paper we make the comparison and pursue the following objectives: i/we determine whether the IPCC confidence and likelihood can be compared with the notions of uncertainty targeted by or underlying the formal normative theories of epistemic rationality; ii/we investigate whether the formal normative theories of epistemic rationality justify treating uncertainty along those two dimensions, and indicate how this can be avoided.

Photon Counting Data Analysis: Application of the Maximum Likelihood and Related Methods for the Determination of Lifetimes in Mixtures of Rose Bengal and Rhodamine B

DOE PAGES

Santra, Kalyan; Smith, Emily A.; Petrich, Jacob W.; ...

2016-12-12

It is often convenient to know the minimum amount of data needed in order to obtain a result of desired accuracy and precision. It is a necessity in the case of subdiffraction-limited microscopies, such as stimulated emission depletion (STED) microscopy, owing to the limited sample volumes and the extreme sensitivity of the samples to photobleaching and photodamage. We present a detailed comparison of probability-based techniques (the maximum likelihood method and methods based on the binomial and the Poisson distributions) with residual minimization-based techniques for retrieving the fluorescence decay parameters for various two-fluorophore mixtures, as a function of the total numbermore » of photon counts, in time-correlated, single-photon counting experiments. The probability-based techniques proved to be the most robust (insensitive to initial values) in retrieving the target parameters and, in fact, performed equivalently to 2-3 significant figures. This is to be expected, as we demonstrate that the three methods are fundamentally related. Furthermore, methods based on the Poisson and binomial distributions have the desirable feature of providing a bin-by-bin analysis of a single fluorescence decay trace, which thus permits statistics to be acquired using only the one trace for not only the mean and median values of the fluorescence decay parameters but also for the associated standard deviations. Lastly, these probability-based methods lend themselves well to the analysis of the sparse data sets that are encountered in subdiffraction-limited microscopies.« less

Transactional Sex Risk across a Typology of Rural and Urban Female Sex Workers in Indonesia: A Mixed Methods Study

PubMed Central

Puradiredja, Dewi Ismajani; Coast, Ernestina

2012-01-01

Context-specific typologies of female sex workers (FSWs) are essential for the design of HIV intervention programming. This study develops a novel FSW typology for the analysis of transactional sex risk in rural and urban settings in Indonesia. Mixed methods include a survey of rural and urban FSWs (n = 310), in-depth interviews (n = 11), key informant interviews (n = 5) and ethnographic assessments. Thematic analysis categorises FSWs into 5 distinct groups based on geographical location of their sex work settings, place of solicitation, and whether sex work is their primary occupation. Multiple regression analysis shows that the likelihood of consistent condom use was higher among urban venue-based FSWs for whom sex work is not the only source of income than for any of the other rural and urban FSW groups. This effect was explained by the significantly lower likelihood of consistent condom use by rural venue-based FSWs (adjusted OR: 0.34 95% CI 0.13–0.90, p = 0.029). The FSW typology and differences in organisational features and social dynamics are more closely related to the risk of unprotected transactional sex, than levels of condom awareness and availability. Interventions need context-specific strategies to reach the different FSWs identified by this study's typology. PMID:23285205

Vocational Qualifications, Employment Status and Income: 2006 Census Analysis. Technical Paper

ERIC Educational Resources Information Center

Daly, Anne

2011-01-01

Two features of the labour market for vocationally qualified workers are explored in this technical paper: the likelihood of self-employment versus wage employment and the determinants of income. The analysis showed that demographic, occupational and local labour market characteristics all influence the likelihood of self-employment. Self-employed…

High-Dimensional Exploratory Item Factor Analysis by a Metropolis-Hastings Robbins-Monro Algorithm

ERIC Educational Resources Information Center

Cai, Li

2010-01-01

A Metropolis-Hastings Robbins-Monro (MH-RM) algorithm for high-dimensional maximum marginal likelihood exploratory item factor analysis is proposed. The sequence of estimates from the MH-RM algorithm converges with probability one to the maximum likelihood solution. Details on the computer implementation of this algorithm are provided. The…

Integrating Entropy-Based Naïve Bayes and GIS for Spatial Evaluation of Flood Hazard.

PubMed

Liu, Rui; Chen, Yun; Wu, Jianping; Gao, Lei; Barrett, Damian; Xu, Tingbao; Li, Xiaojuan; Li, Linyi; Huang, Chang; Yu, Jia

2017-04-01

Regional flood risk caused by intensive rainfall under extreme climate conditions has increasingly attracted global attention. Mapping and evaluation of flood hazard are vital parts in flood risk assessment. This study develops an integrated framework for estimating spatial likelihood of flood hazard by coupling weighted naïve Bayes (WNB), geographic information system, and remote sensing. The north part of Fitzroy River Basin in Queensland, Australia, was selected as a case study site. The environmental indices, including extreme rainfall, evapotranspiration, net-water index, soil water retention, elevation, slope, drainage proximity, and density, were generated from spatial data representing climate, soil, vegetation, hydrology, and topography. These indices were weighted using the statistics-based entropy method. The weighted indices were input into the WNB-based model to delineate a regional flood risk map that indicates the likelihood of flood occurrence. The resultant map was validated by the maximum inundation extent extracted from moderate resolution imaging spectroradiometer (MODIS) imagery. The evaluation results, including mapping and evaluation of the distribution of flood hazard, are helpful in guiding flood inundation disaster responses for the region. The novel approach presented consists of weighted grid data, image-based sampling and validation, cell-by-cell probability inferring and spatial mapping. It is superior to an existing spatial naive Bayes (NB) method for regional flood hazard assessment. It can also be extended to other likelihood-related environmental hazard studies. © 2016 Society for Risk Analysis.

Weapon carrying and psychopathic-like features in a population-based sample of Finnish adolescents.

PubMed

Saukkonen, Suvi; Laajasalo, Taina; Jokela, Markus; Kivivuori, Janne; Salmi, Venla; Aronen, Eeva T

2016-02-01

We investigated the prevalence of juvenile weapon carrying and psychosocial and personality-related risk factors for carrying different types of weapons in a nationally representative, population-based sample of Finnish adolescents. Specifically, we aimed to investigate psychopathic-like personality features as a risk factor for weapon carrying. The participants were 15-16-year-old adolescents from the Finnish self-report delinquency study (n = 4855). Four different groups were formed based on self-reported weapon carrying: no weapon carrying, carrying knife, gun or other weapon. The associations between psychosocial factors, psychopathic-like features and weapon carrying were examined with multinomial logistic regression analysis. 9% of the participants had carried a weapon in the past 12 months. Adolescents with a history of delinquency, victimization and antisocial friends were more likely to carry weapons in general; however, delinquency and victimization were most strongly related to gun carrying, while perceived peer delinquency (antisocial friends) was most strongly related to carrying a knife. Better academic performance was associated with a reduced likelihood of carrying a gun and knife, while feeling secure correlated with a reduced likelihood of gun carrying only. Psychopathic-like features were related to a higher likelihood of weapon carrying, even after adjusting for other risk factors. The findings of the study suggest that adolescents carrying a weapon have a large cluster of problems in their lives, which may vary based on the type of weapon carried. Furthermore, psychopathic-like features strongly relate to a higher risk of carrying a weapon.

The interaction of socioeconomic status with place of death: a qualitative analysis of physician experiences.

PubMed

Wales, Joshua; Kurahashi, Allison M; Husain, Amna

2018-06-20

Home is a preferred place of death for many people; however, access to a home death may not be equitable. The impact of socioeconomic status on one's ability to die at home has been documented, yet there remains little literature exploring mechanisms that contribute to this disparity. By exploring the experiences and insights of physicians who provide end-of-life care in the home, this study aims to identify the factors perceived to influence patients' likelihood of home death and describe the mechanisms by which they interact with socioeconomic status. In this exploratory qualitative study, we conducted interviews with 9 physicians who provide home-based care at a specialized palliative care centre. Participants were asked about their experiences caring for patients at the end of life, focusing on factors believed to impact likelihood of home death with an emphasis on socioeconomic status, and opportunities for intervention. We relied on participants' perceptions of SES, rather than objective measures. We used an inductive content analysis to identify and describe factors that physicians perceive to influence a patient's likelihood of dying at home. Factors identified by physicians were organized into three categories: patient characteristics, physical environment and support network. Patient preference for home death was seen as a necessary factor. If this was established, participants suggested that having a strong support network to supplement professional care was critical to achieving home death. Finally, safe and sustainable housing were also felt to improve likelihood of home death. Higher SES was perceived to increase the likelihood of a desired home death by affording access to more resources within each of the categories. This included better health and health care understanding, a higher capacity for advocacy, a more stable home environment, and more caregiver support. SES was not perceived to be an isolated factor impacting likelihood of home death, but rather a means to address shortfalls in the three identified categories. Identifying the factors that influence ability is the first step in ensuring home death is accessible to all patients who desire it, regardless of socioeconomic status.

Further Evaluation of Covariate Analysis using Empirical Bayes Estimates in Population Pharmacokinetics: the Perception of Shrinkage and Likelihood Ratio Test.

PubMed

Xu, Xu Steven; Yuan, Min; Yang, Haitao; Feng, Yan; Xu, Jinfeng; Pinheiro, Jose

2017-01-01

Covariate analysis based on population pharmacokinetics (PPK) is used to identify clinically relevant factors. The likelihood ratio test (LRT) based on nonlinear mixed effect model fits is currently recommended for covariate identification, whereas individual empirical Bayesian estimates (EBEs) are considered unreliable due to the presence of shrinkage. The objectives of this research were to investigate the type I error for LRT and EBE approaches, to confirm the similarity of power between the LRT and EBE approaches from a previous report and to explore the influence of shrinkage on LRT and EBE inferences. Using an oral one-compartment PK model with a single covariate impacting on clearance, we conducted a wide range of simulations according to a two-way factorial design. The results revealed that the EBE-based regression not only provided almost identical power for detecting a covariate effect, but also controlled the false positive rate better than the LRT approach. Shrinkage of EBEs is likely not the root cause for decrease in power or inflated false positive rate although the size of the covariate effect tends to be underestimated at high shrinkage. In summary, contrary to the current recommendations, EBEs may be a better choice for statistical tests in PPK covariate analysis compared to LRT. We proposed a three-step covariate modeling approach for population PK analysis to utilize the advantages of EBEs while overcoming their shortcomings, which allows not only markedly reducing the run time for population PK analysis, but also providing more accurate covariate tests.

Analyzing Planck and low redshift data sets with advanced statistical methods

NASA Astrophysics Data System (ADS)

Eifler, Tim

The recent ESA/NASA Planck mission has provided a key data set to constrain cosmology that is most sensitive to physics of the early Universe, such as inflation and primordial NonGaussianity (Planck 2015 results XIII). In combination with cosmological probes of the LargeScale Structure (LSS), the Planck data set is a powerful source of information to investigate late time phenomena (Planck 2015 results XIV), e.g. the accelerated expansion of the Universe, the impact of baryonic physics on the growth of structure, and the alignment of galaxies in their dark matter halos. It is the main objective of this proposal to re-analyze the archival Planck data, 1) with different, more recently developed statistical methods for cosmological parameter inference, and 2) to combine Planck and ground-based observations in an innovative way. We will make the corresponding analysis framework publicly available and believe that it will set a new standard for future CMB-LSS analyses. Advanced statistical methods, such as the Gibbs sampler (Jewell et al 2004, Wandelt et al 2004) have been critical in the analysis of Planck data. More recently, Approximate Bayesian Computation (ABC, see Weyant et al 2012, Akeret et al 2015, Ishida et al 2015, for cosmological applications) has matured to an interesting tool in cosmological likelihood analyses. It circumvents several assumptions that enter the standard Planck (and most LSS) likelihood analyses, most importantly, the assumption that the functional form of the likelihood of the CMB observables is a multivariate Gaussian. Beyond applying new statistical methods to Planck data in order to cross-check and validate existing constraints, we plan to combine Planck and DES data in a new and innovative way and run multi-probe likelihood analyses of CMB and LSS observables. The complexity of multiprobe likelihood analyses scale (non-linearly) with the level of correlations amongst the individual probes that are included. For the multi-probe analysis proposed here we will use the existing CosmoLike software, a computationally efficient analysis framework that is unique in its integrated ansatz of jointly analyzing probes of large-scale structure (LSS) of the Universe. We plan to combine CosmoLike with publicly available CMB analysis software (Camb, CLASS) to include modeling capabilities of CMB temperature, polarization, and lensing measurements. The resulting analysis framework will be capable to independently and jointly analyze data from the CMB and from various probes of the LSS of the Universe. After completion we will utilize this framework to check for consistency amongst the individual probes and subsequently run a joint likelihood analysis of probes that are not in tension. The inclusion of Planck information in a joint likelihood analysis substantially reduces DES uncertainties in cosmological parameters, and allows for unprecedented constraints on parameters that describe astrophysics. In their recent review Observational Probes of Cosmic Acceleration (Weinberg et al 2013) the authors emphasize the value of a balanced program that employs several of the most powerful methods in combination, both to cross-check systematic uncertainties and to take advantage of complementary information. The work we propose follows exactly this idea: 1) cross-checking existing Planck results with alternative methods in the data analysis, 2) checking for consistency of Planck and DES data, and 3) running a joint analysis to constrain cosmology and astrophysics. It is now expedient to develop and refine multi-probe analysis strategies that allow the comparison and inclusion of information from disparate probes to optimally obtain cosmology and astrophysics. Analyzing Planck and DES data poses an ideal opportunity for this purpose and corresponding lessons will be of great value for the science preparation of Euclid and WFIRST.

Educational differences in likelihood of attributing breast symptoms to cancer: a vignette-based study.

PubMed

Marcu, Afrodita; Lyratzopoulos, Georgios; Black, Georgia; Vedsted, Peter; Whitaker, Katriina L

2016-10-01

Stage at diagnosis of breast cancer varies by socio-economic status (SES), with lower SES associated with poorer survival. We investigated associations between SES (indexed by education), and the likelihood of attributing breast symptoms to breast cancer. We conducted an online survey with 961 women (47-92 years) with variable educational levels. Two vignettes depicted familiar and unfamiliar breast changes (axillary lump and nipple rash). Without making breast cancer explicit, women were asked 'What do you think this […..] could be?' After the attribution question, women were asked to indicate their level of agreement with a cancer avoidance statement ('I would not want to know if I have breast cancer'). Women were more likely to mention cancer as a possible cause of an axillary lump (64%) compared with nipple rash (30%). In multivariable analysis, low and mid education were independently associated with being less likely to attribute a nipple rash to cancer (OR 0.51, 0.36-0.73 and OR 0.55, 0.40-0.77, respectively). For axillary lump, low education was associated with lower likelihood of mentioning cancer as a possible cause (OR 0.58, 0.41-0.83). Although cancer avoidance was also associated with lower education, the association between education and lower likelihood of making a cancer attribution was independent. Lower education was associated with lower likelihood of making cancer attributions for both symptoms, also after adjustment for cancer avoidance. Lower likelihood of considering cancer may delay symptomatic presentation and contribute to educational differences in stage at diagnosis. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

On Bayesian Testing of Additive Conjoint Measurement Axioms Using Synthetic Likelihood

ERIC Educational Resources Information Center

Karabatsos, George

2017-01-01

This article introduces a Bayesian method for testing the axioms of additive conjoint measurement. The method is based on an importance sampling algorithm that performs likelihood-free, approximate Bayesian inference using a synthetic likelihood to overcome the analytical intractability of this testing problem. This new method improves upon…

Comparative Outcome Analysis of Penicillin-Based Versus Fluoroquinolone-Based Antibiotic Therapy for Community-Acquired Pneumonia

PubMed Central

Wang, Chi-Chuan; Lin, Chia-Hui; Lin, Kuan-Yin; Chuang, Yu-Chung; Sheng, Wang-Huei

2016-01-01

Abstract Community-acquired pneumonia (CAP) is a common but potentially life-threatening condition, but limited information exists on the effectiveness of fluoroquinolones compared to β-lactams in outpatient settings. We aimed to compare the effectiveness and outcomes of penicillins versus respiratory fluoroquinolones for CAP at outpatient clinics. This was a claim-based retrospective cohort study. Patients aged 20 years or older with at least 1 new pneumonia treatment episode were included, and the index penicillin or respiratory fluoroquinolone therapies for a pneumonia episode were at least 5 days in duration. The 2 groups were matched by propensity scores. Cox proportional hazard models were used to compare the rates of hospitalizations/emergence service visits and 30-day mortality. A logistic model was used to compare the likelihood of treatment failure between the 2 groups. After propensity score matching, 2622 matched pairs were included in the final model. The likelihood of treatment failure of fluoroquinolone-based therapy was lower than that of penicillin-based therapy (adjusted odds ratio [AOR], 0.88; 95% confidence interval [95%CI], 0.77–0.99), but no differences were found in hospitalization/emergence service (ES) visits (adjusted hazard ratio [HR], 1.27; 95% CI, 0.92–1.74) and 30-day mortality (adjusted HR, 0.69; 95% CI, 0.30–1.62) between the 2 groups. The likelihood of treatment failure of fluoroquinolone-based therapy was lower than that of penicillin-based therapy for CAP on an outpatient clinic basis. However, this effect may be marginal. Further investigation into the comparative effectiveness of these 2 treatment options is warranted. PMID:26871827

Comparison of two weighted integration models for the cueing task: linear and likelihood

NASA Technical Reports Server (NTRS)

Shimozaki, Steven S.; Eckstein, Miguel P.; Abbey, Craig K.

2003-01-01

In a task in which the observer must detect a signal at two locations, presenting a precue that predicts the location of a signal leads to improved performance with a valid cue (signal location matches the cue), compared to an invalid cue (signal location does not match the cue). The cue validity effect has often been explained with a limited capacity attentional mechanism improving the perceptual quality at the cued location. Alternatively, the cueing effect can also be explained by unlimited capacity models that assume a weighted combination of noisy responses across the two locations. We compare two weighted integration models, a linear model and a sum of weighted likelihoods model based on a Bayesian observer. While qualitatively these models are similar, quantitatively they predict different cue validity effects as the signal-to-noise ratios (SNR) increase. To test these models, 3 observers performed in a cued discrimination task of Gaussian targets with an 80% valid precue across a broad range of SNR's. Analysis of a limited capacity attentional switching model was also included and rejected. The sum of weighted likelihoods model best described the psychophysical results, suggesting that human observers approximate a weighted combination of likelihoods, and not a weighted linear combination.

On the Likelihood Ratio Test for the Number of Factors in Exploratory Factor Analysis

ERIC Educational Resources Information Center

Hayashi, Kentaro; Bentler, Peter M.; Yuan, Ke-Hai

2007-01-01

In the exploratory factor analysis, when the number of factors exceeds the true number of factors, the likelihood ratio test statistic no longer follows the chi-square distribution due to a problem of rank deficiency and nonidentifiability of model parameters. As a result, decisions regarding the number of factors may be incorrect. Several…

Infertility Evaluation and Treatment among Women in the United States

PubMed Central

Kessler, Lawrence M.; Craig, Benjamin M.; Plosker, Shayne M.; Reed, Damon R.; Quinn, Gwendolyn P.

2013-01-01

Objective To examine the characteristics of women seeking infertility evaluation and treatment. Design Cross-sectional survey based on in-person interviews, followed by two-step hurdle analysis. Participants 4,558 married or cohabitating women ages 25–44 Setting U.S. household population of women based on the 2006–2010 National Survey of Family Growth Intervention None Main Outcome Measure(s) Likelihood of seeking preliminary infertility evaluation. Likelihood of seeking infertility treatment once evaluated. Treatment type provided. Results 623 women (13.7%) reported seeking infertility evaluation, of which 328 reported undergoing subsequent infertility treatment. Age at marriage, marital status, education, health insurance status, race/ethnicity, and religion were associated with the likelihood of seeking infertility evaluation. For example, the predicted probability that a non-White woman who married at 25 will seek evaluation was 12%. This probability increased to 34% for White women with a graduate degree who married at age 30. Among women who are evaluated, income, employment status, and ethnicity correlated strongly with the likelihood of seeking infertility treatment. Infertility drug therapy was the most frequent treatment used. Reproductive surgery and in vitro fertilization (IVF) were used the least. Conclusions The use of infertility services is not random and understanding the socio-demographic factors correlated with use may assist new couples with family planning. Roughly 50% of the women evaluated for infertility progressed to treatment, and only a small proportion were treated with more advanced assisted reproductive technologies (ARTs) such as IVF therapy. Future research aimed at improving access to effective healthcare treatments within the boundaries of affordability is warranted. PMID:23849845

Identification of contemporary selection signatures using composite log likelihood and their associations with marbling score in Korean cattle.

PubMed

Ryu, Jihye; Lee, Chaeyoung

2014-12-01

Positive selection not only increases beneficial allele frequency but also causes augmentation of allele frequencies of sequence variants in close proximity. Signals for positive selection were detected by the statistical differences in subsequent allele frequencies. To identify selection signatures in Korean cattle, we applied a composite log-likelihood (CLL)-based method, which calculates a composite likelihood of the allelic frequencies observed across sliding windows of five adjacent loci and compares the value with the critical statistic estimated by 50,000 permutations. Data for a total of 11,799 nucleotide polymorphisms were used with 71 Korean cattle and 209 foreign beef cattle. As a result, 147 signals were identified for Korean cattle based on CLL estimates (P < 0.01). The signals might be candidate genetic factors for meat quality by which the Korean cattle have been selected. Further genetic association analysis with 41 intragenic variants in the selection signatures with the greatest CLL for each chromosome revealed that marbling score was associated with five variants. Intensive association studies with all the selection signatures identified in this study are required to exclude signals associated with other phenotypes or signals falsely detected and thus to identify genetic markers for meat quality. © 2014 Stichting International Foundation for Animal Genetics.

Search for electroweak single top quark production with cdf in proton - anti-proton collisions at √s = 1.96-TeV

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

Walter, Thorsten

2005-06-17

In this thesis two searches for electroweak single top quark production with the CDF experiment have been presented, a cutbased search and an iterated discriminant analysis. Both searches find no significant evidence for electroweak single top production using a data set corresponding to an integrated luminosity of 162 pb -1 collected with CDF. Therefore limits on s- and t-channel single top production are determined using a likelihood technique. For the cutbased search a likelihood function based on lepton charge times pseudorapidity of the non-bottom jet was used if exactly one bottom jet was identified in the event. In case ofmore » two identified bottom jets a likelihood function based on the total number of observed events was used. The systematic uncertainties have been treated in a Bayesian approach, all sources of systematic uncertainties have been integrated out. An improved signal modeling using the MadEvent Monte Carlo program matched to NLO calculations has been used. The obtained limits for the s- and t-channel single top production cross sections are 13.6 pb and 10.1 pb, respectively. To date, these are most stringent limits published for the s- and the t-channel single top quark production modes.« less

Parkinson's disease: a population-based investigation of life satisfaction and employment.

PubMed

Gustafsson, Helena; Nordström, Peter; Stråhle, Stefan; Nordström, Anna

2015-01-01

To investigate relationships between individuals' socioeconomic situations and quality of life in working-aged subjects with Parkinson's disease. A population-based cohort comprising 1,432 people with Parkinson's disease and 1,135 matched controls, who responded to a questionnaire. Logistic regression analysis was performed to identify factors associated with life satisfaction and likelihood of employment. In multivariate analyses, Parkinson's disease was associated with an increased risk of dissatisfaction with life (odds ratio (OR) = 5.4, 95% confidence interval (95% CI) = 4.2-7.1) and reduced likelihood of employment (OR = 0.30, 95% CI = 0.25-0.37). Employers' support was associated with greater likelihood of employment (p < 0.001). Twenty-four percent of people with Parkinson's disease for ≥ 10 years remained employed and 6% worked full-time. People with Parkinson's disease also more frequently experienced work demands that exceeded their capacity; this factor and unemployment independently correlated with greater risk of dissatisfaction with life (both p < 0.05). People with Parkinson's disease have an increased risk of dissatisfaction with life. Employment situation is important for general life satisfaction among working-aged individuals. People with Parkinson's disease appear to find it difficult to meet the challenge of achieving a balanced employment situation.

Evaluation of Smoking Prevention Television Messages Based on the Elaboration Likelihood Model

ERIC Educational Resources Information Center

Flynn, Brian S.; Worden, John K.; Bunn, Janice Yanushka; Connolly, Scott W.; Dorwaldt, Anne L.

2011-01-01

Progress in reducing youth smoking may depend on developing improved methods to communicate with higher risk youth. This study explored the potential of smoking prevention messages based on the Elaboration Likelihood Model (ELM) to address these needs. Structured evaluations of 12 smoking prevention messages based on three strategies derived from…

Anticipating cognitive effort: roles of perceived error-likelihood and time demands.

PubMed

Dunn, Timothy L; Inzlicht, Michael; Risko, Evan F

2017-11-13

Why are some actions evaluated as effortful? In the present set of experiments we address this question by examining individuals' perception of effort when faced with a trade-off between two putative cognitive costs: how much time a task takes vs. how error-prone it is. Specifically, we were interested in whether individuals anticipate engaging in a small amount of hard work (i.e., low time requirement, but high error-likelihood) vs. a large amount of easy work (i.e., high time requirement, but low error-likelihood) as being more effortful. In between-subject designs, Experiments 1 through 3 demonstrated that individuals anticipate options that are high in perceived error-likelihood (yet less time consuming) as more effortful than options that are perceived to be more time consuming (yet low in error-likelihood). Further, when asked to evaluate which of the two tasks was (a) more effortful, (b) more error-prone, and (c) more time consuming, effort-based and error-based choices closely tracked one another, but this was not the case for time-based choices. Utilizing a within-subject design, Experiment 4 demonstrated overall similar pattern of judgments as Experiments 1 through 3. However, both judgments of error-likelihood and time demand similarly predicted effort judgments. Results are discussed within the context of extant accounts of cognitive control, with considerations of how error-likelihood and time demands may independently and conjunctively factor into judgments of cognitive effort.

Rational selection of experimental readout and intervention sites for reducing uncertainties in computational model predictions.

PubMed

Flassig, Robert J; Migal, Iryna; der Zalm, Esther van; Rihko-Struckmann, Liisa; Sundmacher, Kai

2015-01-16

Understanding the dynamics of biological processes can substantially be supported by computational models in the form of nonlinear ordinary differential equations (ODE). Typically, this model class contains many unknown parameters, which are estimated from inadequate and noisy data. Depending on the ODE structure, predictions based on unmeasured states and associated parameters are highly uncertain, even undetermined. For given data, profile likelihood analysis has been proven to be one of the most practically relevant approaches for analyzing the identifiability of an ODE structure, and thus model predictions. In case of highly uncertain or non-identifiable parameters, rational experimental design based on various approaches has shown to significantly reduce parameter uncertainties with minimal amount of effort. In this work we illustrate how to use profile likelihood samples for quantifying the individual contribution of parameter uncertainty to prediction uncertainty. For the uncertainty quantification we introduce the profile likelihood sensitivity (PLS) index. Additionally, for the case of several uncertain parameters, we introduce the PLS entropy to quantify individual contributions to the overall prediction uncertainty. We show how to use these two criteria as an experimental design objective for selecting new, informative readouts in combination with intervention site identification. The characteristics of the proposed multi-criterion objective are illustrated with an in silico example. We further illustrate how an existing practically non-identifiable model for the chlorophyll fluorescence induction in a photosynthetic organism, D. salina, can be rendered identifiable by additional experiments with new readouts. Having data and profile likelihood samples at hand, the here proposed uncertainty quantification based on prediction samples from the profile likelihood provides a simple way for determining individual contributions of parameter uncertainties to uncertainties in model predictions. The uncertainty quantification of specific model predictions allows identifying regions, where model predictions have to be considered with care. Such uncertain regions can be used for a rational experimental design to render initially highly uncertain model predictions into certainty. Finally, our uncertainty quantification directly accounts for parameter interdependencies and parameter sensitivities of the specific prediction.

A Comparison of a Bayesian and a Maximum Likelihood Tailored Testing Procedure.

ERIC Educational Resources Information Center

McKinley, Robert L.; Reckase, Mark D.

A study was conducted to compare tailored testing procedures based on a Bayesian ability estimation technique and on a maximum likelihood ability estimation technique. The Bayesian tailored testing procedure selected items so as to minimize the posterior variance of the ability estimate distribution, while the maximum likelihood tailored testing…

Likelihood-based modification of experimental crystal structure electron density maps

DOEpatents

Terwilliger, Thomas C [Sante Fe, NM

2005-04-16

A maximum-likelihood method for improves an electron density map of an experimental crystal structure. A likelihood of a set of structure factors {F.sub.h } is formed for the experimental crystal structure as (1) the likelihood of having obtained an observed set of structure factors {F.sub.h.sup.OBS } if structure factor set {F.sub.h } was correct, and (2) the likelihood that an electron density map resulting from {F.sub.h } is consistent with selected prior knowledge about the experimental crystal structure. The set of structure factors {F.sub.h } is then adjusted to maximize the likelihood of {F.sub.h } for the experimental crystal structure. An improved electron density map is constructed with the maximized structure factors.

Phylogeny of Salsoleae s.l. (Chenopodiaceae) based on DNA sequence data from ITS, psbB-psbH, and rbcL, with emphasis on taxa of northwestern China

Treesearch

Zhi-Bin Wen; Ming-Li Zhang; Ge-Lin Zhu; Stewart C. Sanderson

2010-01-01

To reconstruct phylogeny and verify the monophyly of major subgroups, a total of 52 species representing almost all species of Salsoleae s.l. in China were sampled, with analysis based on three molecular markers (nrDNA ITS, cpDNA psbB-psbH and rbcL), using maximum parsimony, maximum likelihood, and Bayesian inference methods. Our molecular evidence provides strong...

Multivariate meta-analysis: a robust approach based on the theory of U-statistic.

PubMed

Ma, Yan; Mazumdar, Madhu

2011-10-30

Meta-analysis is the methodology for combining findings from similar research studies asking the same question. When the question of interest involves multiple outcomes, multivariate meta-analysis is used to synthesize the outcomes simultaneously taking into account the correlation between the outcomes. Likelihood-based approaches, in particular restricted maximum likelihood (REML) method, are commonly utilized in this context. REML assumes a multivariate normal distribution for the random-effects model. This assumption is difficult to verify, especially for meta-analysis with small number of component studies. The use of REML also requires iterative estimation between parameters, needing moderately high computation time, especially when the dimension of outcomes is large. A multivariate method of moments (MMM) is available and is shown to perform equally well to REML. However, there is a lack of information on the performance of these two methods when the true data distribution is far from normality. In this paper, we propose a new nonparametric and non-iterative method for multivariate meta-analysis on the basis of the theory of U-statistic and compare the properties of these three procedures under both normal and skewed data through simulation studies. It is shown that the effect on estimates from REML because of non-normal data distribution is marginal and that the estimates from MMM and U-statistic-based approaches are very similar. Therefore, we conclude that for performing multivariate meta-analysis, the U-statistic estimation procedure is a viable alternative to REML and MMM. Easy implementation of all three methods are illustrated by their application to data from two published meta-analysis from the fields of hip fracture and periodontal disease. We discuss ideas for future research based on U-statistic for testing significance of between-study heterogeneity and for extending the work to meta-regression setting. Copyright © 2011 John Wiley & Sons, Ltd.

Empirical Likelihood in Nonignorable Covariate-Missing Data Problems.

PubMed

Xie, Yanmei; Zhang, Biao

2017-04-20

Missing covariate data occurs often in regression analysis, which frequently arises in the health and social sciences as well as in survey sampling. We study methods for the analysis of a nonignorable covariate-missing data problem in an assumed conditional mean function when some covariates are completely observed but other covariates are missing for some subjects. We adopt the semiparametric perspective of Bartlett et al. (Improving upon the efficiency of complete case analysis when covariates are MNAR. Biostatistics 2014;15:719-30) on regression analyses with nonignorable missing covariates, in which they have introduced the use of two working models, the working probability model of missingness and the working conditional score model. In this paper, we study an empirical likelihood approach to nonignorable covariate-missing data problems with the objective of effectively utilizing the two working models in the analysis of covariate-missing data. We propose a unified approach to constructing a system of unbiased estimating equations, where there are more equations than unknown parameters of interest. One useful feature of these unbiased estimating equations is that they naturally incorporate the incomplete data into the data analysis, making it possible to seek efficient estimation of the parameter of interest even when the working regression function is not specified to be the optimal regression function. We apply the general methodology of empirical likelihood to optimally combine these unbiased estimating equations. We propose three maximum empirical likelihood estimators of the underlying regression parameters and compare their efficiencies with other existing competitors. We present a simulation study to compare the finite-sample performance of various methods with respect to bias, efficiency, and robustness to model misspecification. The proposed empirical likelihood method is also illustrated by an analysis of a data set from the US National Health and Nutrition Examination Survey (NHANES).

Area variations in multiple morbidity using a life table methodology.

PubMed

Congdon, Peter

Analysis of healthy life expectancy is typically based on a binary distinction between health and ill-health. By contrast, this paper considers spatial modelling of disease free life expectancy taking account of the number of chronic conditions. Thus the analysis is based on population sub-groups with no disease, those with one disease only, and those with two or more diseases (multiple morbidity). Data on health status is accordingly modelled using a multinomial likelihood. The analysis uses data for 258 small areas in north London, and shows wide differences in the disease burden related to multiple morbidity. Strong associations between area socioeconomic deprivation and multiple morbidity are demonstrated, as well as strong spatial clustering.

The Construct Validity of Higher Order Structure-of-Intellect Abilities in a Battery of Tests Emphasizing the Product of Transformations: A Confirmatory Maximum Likelihood Factor Analysis.

ERIC Educational Resources Information Center

Khattab, Ali-Maher; And Others

1982-01-01

A causal modeling system, using confirmatory maximum likelihood factor analysis with the LISREL IV computer program, evaluated the construct validity underlying the higher order factor structure of a given correlation matrix of 46 structure-of-intellect tests emphasizing the product of transformations. (Author/PN)

Factors Associated With the Likelihood of Hospitalization Following Emergency Department Visits for Behavioral Health Conditions.

PubMed

Hamilton, Jane E; Desai, Pratikkumar V; Hoot, Nathan R; Gearing, Robin E; Jeong, Shin; Meyer, Thomas D; Soares, Jair C; Begley, Charles E

2016-11-01

Behavioral health-related emergency department (ED) visits have been linked with ED overcrowding, an increased demand on limited resources, and a longer length of stay (LOS) due in part to patients being admitted to the hospital but waiting for an inpatient bed. This study examines factors associated with the likelihood of hospital admission for ED patients with behavioral health conditions at 16 hospital-based EDs in a large urban area in the southern United States. Using Andersen's Behavioral Model of Health Service Use for guidance, the study examined the relationship between predisposing (characteristics of the individual, i.e., age, sex, race/ethnicity), enabling (system or structural factors affecting healthcare access), and need (clinical) factors and the likelihood of hospitalization following ED visits for behavioral health conditions (n = 28,716 ED visits). In the adjusted analysis, a logistic fixed-effects model with blockwise entry was used to estimate the relative importance of predisposing, enabling, and need variables added separately as blocks while controlling for variation in unobserved hospital-specific practices across hospitals and time in years. Significant predisposing factors associated with an increased likelihood of hospitalization following an ED visit included increasing age, while African American race was associated with a lower likelihood of hospitalization. Among enabling factors, arrival by emergency transport and a longer ED LOS were associated with a greater likelihood of hospitalization while being uninsured and the availability of community-based behavioral health services within 5 miles of the ED were associated with lower odds. Among need factors, having a discharge diagnosis of schizophrenia/psychotic spectrum disorder, an affective disorder, a personality disorder, dementia, or an impulse control disorder as well as secondary diagnoses of suicidal ideation and/or suicidal behavior increased the likelihood of hospitalization following an ED visit. The block of enabling factors was the strongest predictor of hospitalization following an ED visit compared to predisposing and need factors. Our findings also provide evidence of disparities in hospitalization of the uninsured and racial and ethnic minority patients with ED visits for behavioral health conditions. Thus, improved access to community-based behavioral health services and an increased capacity for inpatient psychiatric hospitals for treating indigent patients may be needed to improve the efficiency of ED services in our region for patients with behavioral health conditions. Among need factors, a discharge diagnosis of schizophrenia/psychotic spectrum disorder, an affective disorder, a personality disorder, an impulse control disorder, or dementia as well as secondary diagnoses of suicidal ideation and/or suicidal behavior increased the likelihood of hospitalization following an ED visit, also suggesting an opportunity for improving the efficiency of ED care through the provision of psychiatric services to stabilize and treat patients with serious mental illness. © 2016 by the Society for Academic Emergency Medicine.

Student Loans and the Likelihood of Graduation: Evidence from Finnish Cohort Data

ERIC Educational Resources Information Center

Asplund, Rita; Abdelkarim, Oussama Ben; Skalli, Ali

2009-01-01

Most countries have experienced soaring enrolment rates into tertiary education. While this is beneficial both to involved students and to knowledge-based societies, it also makes the funding issue even more crucial than it is usually seen to be. This paper presents first results from an analysis of the effectiveness of student loans as a funding…

Functional brain networks in healthy subjects under acupuncture stimulation: An EEG study based on nonlinear synchronization likelihood analysis

NASA Astrophysics Data System (ADS)

Yu, Haitao; Liu, Jing; Cai, Lihui; Wang, Jiang; Cao, Yibin; Hao, Chongqing

2017-02-01

Electroencephalogram (EEG) signal evoked by acupuncture stimulation at "Zusanli" acupoint is analyzed to investigate the modulatory effect of manual acupuncture on the functional brain activity. Power spectral density of EEG signal is first calculated based on the autoregressive Burg method. It is shown that the EEG power is significantly increased during and after acupuncture in delta and theta bands, but decreased in alpha band. Furthermore, synchronization likelihood is used to estimate the nonlinear correlation between each pairwise EEG signals. By applying a threshold to resulting synchronization matrices, functional networks for each band are reconstructed and further quantitatively analyzed to study the impact of acupuncture on network structure. Graph theoretical analysis demonstrates that the functional connectivity of the brain undergoes obvious change under different conditions: pre-acupuncture, acupuncture, and post-acupuncture. The minimum path length is largely decreased and the clustering coefficient keeps increasing during and after acupuncture in delta and theta bands. It is indicated that acupuncture can significantly modulate the functional activity of the brain, and facilitate the information transmission within different brain areas. The obtained results may facilitate our understanding of the long-lasting effect of acupuncture on the brain function.

Robustness of fit indices to outliers and leverage observations in structural equation modeling.

PubMed

Yuan, Ke-Hai; Zhong, Xiaoling

2013-06-01

Normal-distribution-based maximum likelihood (NML) is the most widely used method in structural equation modeling (SEM), although practical data tend to be nonnormally distributed. The effect of nonnormally distributed data or data contamination on the normal-distribution-based likelihood ratio (LR) statistic is well understood due to many analytical and empirical studies. In SEM, fit indices are used as widely as the LR statistic. In addition to NML, robust procedures have been developed for more efficient and less biased parameter estimates with practical data. This article studies the effect of outliers and leverage observations on fit indices following NML and two robust methods. Analysis and empirical results indicate that good leverage observations following NML and one of the robust methods lead most fit indices to give more support to the substantive model. While outliers tend to make a good model superficially bad according to many fit indices following NML, they have little effect on those following the two robust procedures. Implications of the results to data analysis are discussed, and recommendations are provided regarding the use of estimation methods and interpretation of fit indices. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Estimating Function Approaches for Spatial Point Processes

NASA Astrophysics Data System (ADS)

Deng, Chong

Spatial point pattern data consist of locations of events that are often of interest in biological and ecological studies. Such data are commonly viewed as a realization from a stochastic process called spatial point process. To fit a parametric spatial point process model to such data, likelihood-based methods have been widely studied. However, while maximum likelihood estimation is often too computationally intensive for Cox and cluster processes, pairwise likelihood methods such as composite likelihood, Palm likelihood usually suffer from the loss of information due to the ignorance of correlation among pairs. For many types of correlated data other than spatial point processes, when likelihood-based approaches are not desirable, estimating functions have been widely used for model fitting. In this dissertation, we explore the estimating function approaches for fitting spatial point process models. These approaches, which are based on the asymptotic optimal estimating function theories, can be used to incorporate the correlation among data and yield more efficient estimators. We conducted a series of studies to demonstrate that these estmating function approaches are good alternatives to balance the trade-off between computation complexity and estimating efficiency. First, we propose a new estimating procedure that improves the efficiency of pairwise composite likelihood method in estimating clustering parameters. Our approach combines estimating functions derived from pairwise composite likeli-hood estimation and estimating functions that account for correlations among the pairwise contributions. Our method can be used to fit a variety of parametric spatial point process models and can yield more efficient estimators for the clustering parameters than pairwise composite likelihood estimation. We demonstrate its efficacy through a simulation study and an application to the longleaf pine data. Second, we further explore the quasi-likelihood approach on fitting second-order intensity function of spatial point processes. However, the original second-order quasi-likelihood is barely feasible due to the intense computation and high memory requirement needed to solve a large linear system. Motivated by the existence of geometric regular patterns in the stationary point processes, we find a lower dimension representation of the optimal weight function and propose a reduced second-order quasi-likelihood approach. Through a simulation study, we show that the proposed method not only demonstrates superior performance in fitting the clustering parameter but also merits in the relaxation of the constraint of the tuning parameter, H. Third, we studied the quasi-likelihood type estimating funciton that is optimal in a certain class of first-order estimating functions for estimating the regression parameter in spatial point process models. Then, by using a novel spectral representation, we construct an implementation that is computationally much more efficient and can be applied to more general setup than the original quasi-likelihood method.

Bayesian multimodel inference of soil microbial respiration models: Theory, application and future prospective

NASA Astrophysics Data System (ADS)

Elshall, A. S.; Ye, M.; Niu, G. Y.; Barron-Gafford, G.

2015-12-01

Models in biogeoscience involve uncertainties in observation data, model inputs, model structure, model processes and modeling scenarios. To accommodate for different sources of uncertainty, multimodal analysis such as model combination, model selection, model elimination or model discrimination are becoming more popular. To illustrate theoretical and practical challenges of multimodal analysis, we use an example about microbial soil respiration modeling. Global soil respiration releases more than ten times more carbon dioxide to the atmosphere than all anthropogenic emissions. Thus, improving our understanding of microbial soil respiration is essential for improving climate change models. This study focuses on a poorly understood phenomena, which is the soil microbial respiration pulses in response to episodic rainfall pulses (the "Birch effect"). We hypothesize that the "Birch effect" is generated by the following three mechanisms. To test our hypothesis, we developed and assessed five evolving microbial-enzyme models against field measurements from a semiarid Savannah that is characterized by pulsed precipitation. These five model evolve step-wise such that the first model includes none of these three mechanism, while the fifth model includes the three mechanisms. The basic component of Bayesian multimodal analysis is the estimation of marginal likelihood to rank the candidate models based on their overall likelihood with respect to observation data. The first part of the study focuses on using this Bayesian scheme to discriminate between these five candidate models. The second part discusses some theoretical and practical challenges, which are mainly the effect of likelihood function selection and the marginal likelihood estimation methods on both model ranking and Bayesian model averaging. The study shows that making valid inference from scientific data is not a trivial task, since we are not only uncertain about the candidate scientific models, but also about the statistical methods that are used to discriminate between these models.

COSMIC MICROWAVE BACKGROUND LIKELIHOOD APPROXIMATION FOR BANDED PROBABILITY DISTRIBUTIONS

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

Gjerløw, E.; Mikkelsen, K.; Eriksen, H. K.

We investigate sets of random variables that can be arranged sequentially such that a given variable only depends conditionally on its immediate predecessor. For such sets, we show that the full joint probability distribution may be expressed exclusively in terms of uni- and bivariate marginals. Under the assumption that the cosmic microwave background (CMB) power spectrum likelihood only exhibits correlations within a banded multipole range, Δl{sub C}, we apply this expression to two outstanding problems in CMB likelihood analysis. First, we derive a statistically well-defined hybrid likelihood estimator, merging two independent (e.g., low- and high-l) likelihoods into a single expressionmore » that properly accounts for correlations between the two. Applying this expression to the Wilkinson Microwave Anisotropy Probe (WMAP) likelihood, we verify that the effect of correlations on cosmological parameters in the transition region is negligible in terms of cosmological parameters for WMAP; the largest relative shift seen for any parameter is 0.06σ. However, because this may not hold for other experimental setups (e.g., for different instrumental noise properties or analysis masks), but must rather be verified on a case-by-case basis, we recommend our new hybridization scheme for future experiments for statistical self-consistency reasons. Second, we use the same expression to improve the convergence rate of the Blackwell-Rao likelihood estimator, reducing the required number of Monte Carlo samples by several orders of magnitude, and thereby extend it to high-l applications.« less

ARMA-Based SEM When the Number of Time Points T Exceeds the Number of Cases N: Raw Data Maximum Likelihood.

ERIC Educational Resources Information Center

Hamaker, Ellen L.; Dolan, Conor V.; Molenaar, Peter C. M.

2003-01-01

Demonstrated, through simulation, that stationary autoregressive moving average (ARMA) models may be fitted readily when T>N, using normal theory raw maximum likelihood structural equation modeling. Also provides some illustrations based on real data. (SLD)

Technical Note: Approximate Bayesian parameterization of a process-based tropical forest model

NASA Astrophysics Data System (ADS)

Hartig, F.; Dislich, C.; Wiegand, T.; Huth, A.

2014-02-01

Inverse parameter estimation of process-based models is a long-standing problem in many scientific disciplines. A key question for inverse parameter estimation is how to define the metric that quantifies how well model predictions fit to the data. This metric can be expressed by general cost or objective functions, but statistical inversion methods require a particular metric, the probability of observing the data given the model parameters, known as the likelihood. For technical and computational reasons, likelihoods for process-based stochastic models are usually based on general assumptions about variability in the observed data, and not on the stochasticity generated by the model. Only in recent years have new methods become available that allow the generation of likelihoods directly from stochastic simulations. Previous applications of these approximate Bayesian methods have concentrated on relatively simple models. Here, we report on the application of a simulation-based likelihood approximation for FORMIND, a parameter-rich individual-based model of tropical forest dynamics. We show that approximate Bayesian inference, based on a parametric likelihood approximation placed in a conventional Markov chain Monte Carlo (MCMC) sampler, performs well in retrieving known parameter values from virtual inventory data generated by the forest model. We analyze the results of the parameter estimation, examine its sensitivity to the choice and aggregation of model outputs and observed data (summary statistics), and demonstrate the application of this method by fitting the FORMIND model to field data from an Ecuadorian tropical forest. Finally, we discuss how this approach differs from approximate Bayesian computation (ABC), another method commonly used to generate simulation-based likelihood approximations. Our results demonstrate that simulation-based inference, which offers considerable conceptual advantages over more traditional methods for inverse parameter estimation, can be successfully applied to process-based models of high complexity. The methodology is particularly suitable for heterogeneous and complex data structures and can easily be adjusted to other model types, including most stochastic population and individual-based models. Our study therefore provides a blueprint for a fairly general approach to parameter estimation of stochastic process-based models.

Land cover mapping after the tsunami event over Nanggroe Aceh Darussalam (NAD) province, Indonesia

NASA Astrophysics Data System (ADS)

Lim, H. S.; MatJafri, M. Z.; Abdullah, K.; Alias, A. N.; Mohd. Saleh, N.; Wong, C. J.; Surbakti, M. S.

2008-03-01

Remote sensing offers an important means of detecting and analyzing temporal changes occurring in our landscape. This research used remote sensing to quantify land use/land cover changes at the Nanggroe Aceh Darussalam (Nad) province, Indonesia on a regional scale. The objective of this paper is to assess the changed produced from the analysis of Landsat TM data. A Landsat TM image was used to develop land cover classification map for the 27 March 2005. Four supervised classifications techniques (Maximum Likelihood, Minimum Distance-to- Mean, Parallelepiped and Parallelepiped with Maximum Likelihood Classifier Tiebreaker classifier) were performed to the satellite image. Training sites and accuracy assessment were needed for supervised classification techniques. The training sites were established using polygons based on the colour image. High detection accuracy (>80%) and overall Kappa (>0.80) were achieved by the Parallelepiped with Maximum Likelihood Classifier Tiebreaker classifier in this study. This preliminary study has produced a promising result. This indicates that land cover mapping can be carried out using remote sensing classification method of the satellite digital imagery.

Wavelet-based image analysis system for soil texture analysis

NASA Astrophysics Data System (ADS)

Sun, Yun; Long, Zhiling; Jang, Ping-Rey; Plodinec, M. John

2003-05-01

Soil texture is defined as the relative proportion of clay, silt and sand found in a given soil sample. It is an important physical property of soil that affects such phenomena as plant growth and agricultural fertility. Traditional methods used to determine soil texture are either time consuming (hydrometer), or subjective and experience-demanding (field tactile evaluation). Considering that textural patterns observed at soil surfaces are uniquely associated with soil textures, we propose an innovative approach to soil texture analysis, in which wavelet frames-based features representing texture contents of soil images are extracted and categorized by applying a maximum likelihood criterion. The soil texture analysis system has been tested successfully with an accuracy of 91% in classifying soil samples into one of three general categories of soil textures. In comparison with the common methods, this wavelet-based image analysis approach is convenient, efficient, fast, and objective.

Determining the linkage of disease-resistance genes to molecular markers: the LOD-SCORE method revisited with regard to necessary sample sizes.

PubMed

Hühn, M

1995-05-01

Some approaches to molecular marker-assisted linkage detection for a dominant disease-resistance trait based on a segregating F2 population are discussed. Analysis of two-point linkage is carried out by the traditional measure of maximum lod score. It depends on (1) the maximum-likelihood estimate of the recombination fraction between the marker and the disease-resistance gene locus, (2) the observed absolute frequencies, and (3) the unknown number of tested individuals. If one replaces the absolute frequencies by expressions depending on the unknown sample size and the maximum-likelihood estimate of recombination value, the conventional rule for significant linkage (maximum lod score exceeds a given linkage threshold) can be resolved for the sample size. For each sub-population used for linkage analysis [susceptible (= recessive) individuals, resistant (= dominant) individuals, complete F2] this approach gives a lower bound for the necessary number of individuals required for the detection of significant two-point linkage by the lod-score method.

Automated detection of exudates for diabetic retinopathy screening

NASA Astrophysics Data System (ADS)

Fleming, Alan D.; Philip, Sam; Goatman, Keith A.; Williams, Graeme J.; Olson, John A.; Sharp, Peter F.

2007-12-01

Automated image analysis is being widely sought to reduce the workload required for grading images resulting from diabetic retinopathy screening programmes. The recognition of exudates in retinal images is an important goal for automated analysis since these are one of the indicators that the disease has progressed to a stage requiring referral to an ophthalmologist. Candidate exudates were detected using a multi-scale morphological process. Based on local properties, the likelihoods of a candidate being a member of classes exudate, drusen or background were determined. This leads to a likelihood of the image containing exudates which can be thresholded to create a binary decision. Compared to a clinical reference standard, images containing exudates were detected with sensitivity 95.0% and specificity 84.6% in a test set of 13 219 images of which 300 contained exudates. Depending on requirements, this method could form part of an automated system to detect images showing either any diabetic retinopathy or referable diabetic retinopathy.

Markov Chain Monte Carlo: an introduction for epidemiologists

PubMed Central

Hamra, Ghassan; MacLehose, Richard; Richardson, David

2013-01-01

Markov Chain Monte Carlo (MCMC) methods are increasingly popular among epidemiologists. The reason for this may in part be that MCMC offers an appealing approach to handling some difficult types of analyses. Additionally, MCMC methods are those most commonly used for Bayesian analysis. However, epidemiologists are still largely unfamiliar with MCMC. They may lack familiarity either with he implementation of MCMC or with interpretation of the resultant output. As with tutorials outlining the calculus behind maximum likelihood in previous decades, a simple description of the machinery of MCMC is needed. We provide an introduction to conducting analyses with MCMC, and show that, given the same data and under certain model specifications, the results of an MCMC simulation match those of methods based on standard maximum-likelihood estimation (MLE). In addition, we highlight examples of instances in which MCMC approaches to data analysis provide a clear advantage over MLE. We hope that this brief tutorial will encourage epidemiologists to consider MCMC approaches as part of their analytic tool-kit. PMID:23569196

Coalescent-based species tree inference from gene tree topologies under incomplete lineage sorting by maximum likelihood.

PubMed

Wu, Yufeng

2012-03-01

Incomplete lineage sorting can cause incongruence between the phylogenetic history of genes (the gene tree) and that of the species (the species tree), which can complicate the inference of phylogenies. In this article, I present a new coalescent-based algorithm for species tree inference with maximum likelihood. I first describe an improved method for computing the probability of a gene tree topology given a species tree, which is much faster than an existing algorithm by Degnan and Salter (2005). Based on this method, I develop a practical algorithm that takes a set of gene tree topologies and infers species trees with maximum likelihood. This algorithm searches for the best species tree by starting from initial species trees and performing heuristic search to obtain better trees with higher likelihood. This algorithm, called STELLS (which stands for Species Tree InfErence with Likelihood for Lineage Sorting), has been implemented in a program that is downloadable from the author's web page. The simulation results show that the STELLS algorithm is more accurate than an existing maximum likelihood method for many datasets, especially when there is noise in gene trees. I also show that the STELLS algorithm is efficient and can be applied to real biological datasets. © 2011 The Author. Evolution© 2011 The Society for the Study of Evolution.

A Variance Distribution Model of Surface EMG Signals Based on Inverse Gamma Distribution.

PubMed

Hayashi, Hideaki; Furui, Akira; Kurita, Yuichi; Tsuji, Toshio

2017-11-01

Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force. Objective: This paper describes the formulation of a surface electromyogram (EMG) model capable of representing the variance distribution of EMG signals. Methods: In the model, EMG signals are handled based on a Gaussian white noise process with a mean of zero for each variance value. EMG signal variance is taken as a random variable that follows inverse gamma distribution, allowing the representation of noise superimposed onto this variance. Variance distribution estimation based on marginal likelihood maximization is also outlined in this paper. The procedure can be approximated using rectified and smoothed EMG signals, thereby allowing the determination of distribution parameters in real time at low computational cost. Results: A simulation experiment was performed to evaluate the accuracy of distribution estimation using artificially generated EMG signals, with results demonstrating that the proposed model's accuracy is higher than that of maximum-likelihood-based estimation. Analysis of variance distribution using real EMG data also suggested a relationship between variance distribution and signal-dependent noise. Conclusion: The study reported here was conducted to examine the performance of a proposed surface EMG model capable of representing variance distribution and a related distribution parameter estimation method. Experiments using artificial and real EMG data demonstrated the validity of the model. Significance: Variance distribution estimated using the proposed model exhibits potential in the estimation of muscle force.

General Practitioners' Attitudes Toward a Web-Based Mental Health Service for Adolescents: Implications for Service Design and Delivery.

PubMed

Subotic-Kerry, Mirjana; King, Catherine; O'Moore, Kathleen; Achilles, Melinda; O'Dea, Bridianne

2018-03-23

Anxiety disorders and depression are prevalent among youth. General practitioners (GPs) are often the first point of professional contact for treating health problems in young people. A Web-based mental health service delivered in partnership with schools may facilitate increased access to psychological care among adolescents. However, for such a model to be implemented successfully, GPs' views need to be measured. This study aimed to examine the needs and attitudes of GPs toward a Web-based mental health service for adolescents, and to identify the factors that may affect the provision of this type of service and likelihood of integration. Findings will inform the content and overall service design. GPs were interviewed individually about the proposed Web-based service. Qualitative analysis of transcripts was performed using thematic coding. A short follow-up questionnaire was delivered to assess background characteristics, level of acceptability, and likelihood of integration of the Web-based mental health service. A total of 13 GPs participated in the interview and 11 completed a follow-up online questionnaire. Findings suggest strong support for the proposed Web-based mental health service. A wide range of factors were found to influence the likelihood of GPs integrating a Web-based service into their clinical practice. Coordinated collaboration with parents, students, school counselors, and other mental health care professionals were considered important by nearly all GPs. Confidence in Web-based care, noncompliance of adolescents and GPs, accessibility, privacy, and confidentiality were identified as potential barriers to adopting the proposed Web-based service. GPs were open to a proposed Web-based service for the monitoring and management of anxiety and depression in adolescents, provided that a collaborative approach to care is used, the feedback regarding the client is clear, and privacy and security provisions are assured. ©Mirjana Subotic-Kerry, Catherine King, Kathleen O'Moore, Melinda Achilles, Bridianne O'Dea. Originally published in JMIR Human Factors (http://humanfactors.jmir.org), 23.03.2018.

Parameter estimation in astronomy through application of the likelihood ratio. [satellite data analysis techniques

NASA Technical Reports Server (NTRS)

Cash, W.

1979-01-01

Many problems in the experimental estimation of parameters for models can be solved through use of the likelihood ratio test. Applications of the likelihood ratio, with particular attention to photon counting experiments, are discussed. The procedures presented solve a greater range of problems than those currently in use, yet are no more difficult to apply. The procedures are proved analytically, and examples from current problems in astronomy are discussed.

Application of the Elaboration Likelihood Model of Attitude Change to Assertion Training.

ERIC Educational Resources Information Center

Ernst, John M.; Heesacker, Martin

1993-01-01

College students (n=113) participated in study comparing effects of elaboration likelihood model (ELM) based assertion workshop with those of typical assertion workshop. ELM-based workshop was significantly better at producing favorable attitude change, greater intention to act assertively, and more favorable evaluations of workshop content.…

A semiparametric Bayesian proportional hazards model for interval censored data with frailty effects.

PubMed

Henschel, Volkmar; Engel, Jutta; Hölzel, Dieter; Mansmann, Ulrich

2009-02-10

Multivariate analysis of interval censored event data based on classical likelihood methods is notoriously cumbersome. Likelihood inference for models which additionally include random effects are not available at all. Developed algorithms bear problems for practical users like: matrix inversion, slow convergence, no assessment of statistical uncertainty. MCMC procedures combined with imputation are used to implement hierarchical models for interval censored data within a Bayesian framework. Two examples from clinical practice demonstrate the handling of clustered interval censored event times as well as multilayer random effects for inter-institutional quality assessment. The software developed is called survBayes and is freely available at CRAN. The proposed software supports the solution of complex analyses in many fields of clinical epidemiology as well as health services research.

Organizational adoption of preemployment drug testing.

PubMed

Spell, C S; Blum, T C

2001-04-01

This study explored the adoption of preemployment drug testing by 360 organizations. Survival models were developed that included internal organizational and labor market factors hypothesized to affect the likelihood of adoption of drug testing. Also considered was another set of variables that included social and political variables based on institutional theory. An event history analysis using Cox regressions indicated that both internal organizational and environmental variables predicted adoption of drug testing. Results indicate that the higher the proportion of drug testers in the worksite's industry, the more likely it would be to adopt drug testing. Also, the extent to which an organization uses an internal labor market, voluntary turnover rate, and the extent to which management perceives drugs to be a problem were related to likelihood of adoption of drug testing.

Measurement of the top-quark mass in all-hadronic decays in pp collisions at CDF II.

PubMed

Aaltonen, T; Abulencia, A; Adelman, J; Affolder, T; Akimoto, T; Albrow, M G; Ambrose, D; Amerio, S; Amidei, D; Anastassov, A; Anikeev, K; Annovi, A; Antos, J; Aoki, M; Apollinari, G; Arguin, J-F; Arisawa, T; Artikov, A; Ashmanskas, W; Attal, A; Azfar, F; Azzi-Bacchetta, P; Azzurri, P; Bacchetta, N; Badgett, W; Barbaro-Galtieri, A; Barnes, V E; Barnett, B A; Baroiant, S; Bartsch, V; Bauer, G; Bedeschi, F; Behari, S; Belforte, S; Bellettini, G; Bellinger, J; Belloni, A; Benjamin, D; Beretvas, A; Beringer, J; Berry, T; Bhatti, A; Binkley, M; Bisello, D; Blair, R E; Blocker, C; Blumenfeld, B; Bocci, A; Bodek, A; Boisvert, V; Bolla, G; Bolshov, A; Bortoletto, D; Boudreau, J; Boveia, A; Brau, B; Brigliadori, L; Bromberg, C; Brubaker, E; Budagov, J; Budd, H S; Budd, S; Budroni, S; Burkett, K; Busetto, G; Bussey, P; Byrum, K L; Cabrera, S; Campanelli, M; Campbell, M; Canelli, F; Canepa, A; Carillo, S; Carlsmith, D; Carosi, R; Casarsa, M; Castro, A; Catastini, P; Cauz, D; Cavalli-Sforza, M; Cerri, A; Cerrito, L; Chang, S H; Chen, Y C; Chertok, M; Chiarelli, G; Chlachidze, G; Chlebana, F; Cho, I; Cho, K; Chokheli, D; Chou, J P; Choudalakis, G; Chuang, S H; Chung, K; Chung, W H; Chung, Y S; Ciljak, M; Ciobanu, C I; Ciocci, M A; Clark, A; Clark, D; Coca, M; Compostella, G; Convery, M E; Conway, J; Cooper, B; Copic, K; Cordelli, M; Cortiana, G; Crescioli, F; Almenar, C Cuenca; Cuevas, J; Culbertson, R; Cully, J C; Cyr, D; Daronco, S; Datta, M; D'Auria, S; Davies, T; D'Onofrio, M; Dagenhart, D; de Barbaro, P; De Cecco, S; Deisher, A; De Lentdecker, G; Dell'orso, M; Delli Paoli, F; Demortier, L; Deng, J; Deninno, M; De Pedis, D; Derwent, P F; Di Giovanni, G P; Dionisi, C; Di Ruzza, B; Dittmann, J R; Dituro, P; Dörr, C; Donati, S; Donega, M; Dong, P; Donini, J; Dorigo, T; Dube, S; Efron, J; Erbacher, R; Errede, D; Errede, S; Eusebi, R; Fang, H C; Farrington, S; Fedorko, I; Fedorko, W T; Feild, R G; Feindt, M; Fernandez, J P; Field, R; Flanagan, G; Foland, A; Forrester, S; Foster, G W; Franklin, M; Freeman, J C; Furic, I; Gallinaro, M; Galyardt, J; Garcia, J E; Garberson, F; Garfinkel, A F; Gay, C; Gerberich, H; Gerdes, D; Giagu, S; Giannetti, P; Gibson, A; Gibson, K; Gimmell, J L; Ginsburg, C; Giokaris, N; Giordani, M; Giromini, P; Giunta, M; Giurgiu, G; Glagolev, V; Glenzinski, D; Gold, M; Goldschmidt, N; Goldstein, J; Golossanov, A; Gomez, G; Gomez-Ceballos, G; Goncharov, M; González, O; Gorelov, I; Goshaw, A T; Goulianos, K; Gresele, A; Griffiths, M; Grinstein, S; Grosso-Pilcher, C; Grundler, U; da Costa, J Guimaraes; Gunay-Unalan, Z; Haber, C; Hahn, K; Hahn, S R; Halkiadakis, E; Hamilton, A; Han, B-Y; Han, J Y; Handler, R; Happacher, F; Hara, K; Hare, M; Harper, S; Harr, R F; Harris, R M; Hartz, M; Hatakeyama, K; Hauser, J; Heijboer, A; Heinemann, B; Heinrich, J; Henderson, C; Herndon, M; Heuser, J; Hidas, D; Hill, C S; Hirschbuehl, D; Hocker, A; Holloway, A; Hou, S; Houlden, M; Hsu, S-C; Huffman, B T; Hughes, R E; Husemann, U; Huston, J; Incandela, J; Introzzi, G; Iori, M; Ishizawa, Y; Ivanov, A; Iyutin, B; James, E; Jang, D; Jayatilaka, B; Jeans, D; Jensen, H; Jeon, E J; Jindariani, S; Jones, M; Joo, K K; Jun, S Y; Jung, J E; Junk, T R; Kamon, T; Karchin, P E; Kato, Y; Kemp, Y; Kephart, R; Kerzel, U; Khotilovich, V; Kilminster, B; Kim, D H; Kim, H S; Kim, J E; Kim, M J; Kim, S B; Kim, S H; Kim, Y K; Kimura, N; Kirsch, L; Klimenko, S; Klute, M; Knuteson, B; Ko, B R; Kondo, K; Kong, D J; Konigsberg, J; Korytov, A; Kotwal, A V; Kovalev, A; Kraan, A C; Kraus, J; Kravchenko, I; Kreps, M; Kroll, J; Krumnack, N; Kruse, M; Krutelyov, V; Kubo, T; Kuhlmann, S E; Kuhr, T; Kusakabe, Y; Kwang, S; Laasanen, A T; Lai, S; Lami, S; Lammel, S; Lancaster, M; Lander, R L; Lannon, K; Lath, A; Latino, G; Lazzizzera, I; Lecompte, T; Lee, J; Lee, J; Lee, Y J; Lee, S W; Lefèvre, R; Leonardo, N; Leone, S; Levy, S; Lewis, J D; Lin, C; Lin, C S; Lindgren, M; Lipeles, E; Lister, A; Litvintsev, D O; Liu, T; Lockyer, N S; Loginov, A; Loreti, M; Loverre, P; Lu, R-S; Lucchesi, D; Lujan, P; Lukens, P; Lungu, G; Lyons, L; Lys, J; Lysak, R; Lytken, E; Mack, P; MacQueen, D; Madrak, R; Maeshima, K; Makhoul, K; Maki, T; Maksimovic, P; Malde, S; Manca, G; Margaroli, F; Marginean, R; Marino, C; Marino, C P; Martin, A; Martin, M; Martin, V; Martínez, M; Maruyama, T; Mastrandrea, P; Masubuchi, T; Matsunaga, H; Mattson, M E; Mazini, R; Mazzanti, P; McFarland, K S; McIntyre, P; McNulty, R; Mehta, A; Mehtala, P; Menzemer, S; Menzione, A; Merkel, P; Mesropian, C; Messina, A; Miao, T; Miladinovic, N; Miles, J; Miller, R; Mills, C; Milnik, M; Mitra, A; Mitselmakher, G; Miyamoto, A; Moed, S; Moggi, N; Mohr, B; Moore, R; Morello, M; Fernandez, P Movilla; Mülmenstädt, J; Mukherjee, A; Muller, Th; Mumford, R; Murat, P; Nachtman, J; Nagano, A; Naganoma, J; Nakano, I; Napier, A; Necula, V; Neu, C; Neubauer, M S; Nielsen, J; Nigmanov, T; Nodulman, L; Norniella, O; Nurse, E; Oh, S H; Oh, Y D; Oksuzian, I; Okusawa, T; Oldeman, R; Orava, R; Osterberg, K; Pagliarone, C; Palencia, E; Papadimitriou, V; Paramonov, A A; Parks, B; Pashapour, S; Patrick, J; Pauletta, G; Paulini, M; Paus, C; Pellett, D E; Penzo, A; Phillips, T J; Piacentino, G; Piedra, J; Pinera, L; Pitts, K; Plager, C; Pondrom, L; Portell, X; Poukhov, O; Pounder, N; Prakoshyn, F; Pronko, A; Proudfoot, J; Ptohos, F; Punzi, G; Pursley, J; Rademacker, J; Rahaman, A; Ranjan, N; Rappoccio, S; Reisert, B; Rekovic, V; Renton, P; Rescigno, M; Richter, S; Rimondi, F; Ristori, L; Robson, A; Rodrigo, T; Rogers, E; Rolli, S; Roser, R; Rossi, M; Rossin, R; Ruiz, A; Russ, J; Rusu, V; Saarikko, H; Sabik, S; Safonov, A; Sakumoto, W K; Salamanna, G; Saltó, O; Saltzberg, D; Sánchez, C; Santi, L; Sarkar, S; Sartori, L; Sato, K; Savard, P; Savoy-Navarro, A; Scheidle, T; Schlabach, P; Schmidt, E E; Schmidt, M P; Schmitt, M; Schwarz, T; Scodellaro, L; Scott, A L; Scribano, A; Scuri, F; Sedov, A; Seidel, S; Seiya, Y; Semenov, A; Sexton-Kennedy, L; Sfyrla, A; Shapiro, M D; Shears, T; Shepard, P F; Sherman, D; Shimojima, M; Shochet, M; Shon, Y; Shreyber, I; Sidoti, A; Sinervo, P; Sisakyan, A; Sjolin, J; Slaughter, A J; Slaunwhite, J; Sliwa, K; Smith, J R; Snider, F D; Snihur, R; Soderberg, M; Soha, A; Somalwar, S; Sorin, V; Spalding, J; Spinella, F; Spreitzer, T; Squillacioti, P; Stanitzki, M; Staveris-Polykalas, A; St Denis, R; Stelzer, B; Stelzer-Chilton, O; Stentz, D; Strologas, J; Stuart, D; Suh, J S; Sukhanov, A; Sun, H; Suzuki, T; Taffard, A; Takashima, R; Takeuchi, Y; Takikawa, K; Tanaka, M; Tanaka, R; Tecchio, M; Teng, P K; Terashi, K; Thom, J; Thompson, A S; Thomson, E; Tipton, P; Tiwari, V; Tkaczyk, S; Toback, D; Tokar, S; Tollefson, K; Tomura, T; Tonelli, D; Torre, S; Torretta, D; Tourneur, S; Trischuk, W; Tsuchiya, R; Tsuno, S; Turini, N; Ukegawa, F; Unverhau, T; Uozumi, S; Usynin, D; Vallecorsa, S; van Remortel, N; Varganov, A; Vataga, E; Vázquez, F; Velev, G; Veramendi, G; Veszpremi, V; Vidal, R; Vila, I; Vilar, R; Vine, T; Vollrath, I; Volobouev, I; Volpi, G; Würthwein, F; Wagner, P; Wagner, R G; Wagner, R L; Wagner, J; Wagner, W; Wallny, R; Wang, S M; Warburton, A; Waschke, S; Waters, D; Wester, W C; Whitehouse, B; Whiteson, D; Wicklund, A B; Wicklund, E; Williams, G; Williams, H H; Wilson, P; Winer, B L; Wittich, P; Wolbers, S; Wolfe, C; Wright, T; Wu, X; Wynne, S M; Yagil, A; Yamamoto, K; Yamaoka, J; Yamashita, T; Yang, C; Yang, U K; Yang, Y C; Yao, W M; Yeh, G P; Yoh, J; Yorita, K; Yoshida, T; Yu, G B; Yu, I; Yu, S S; Yun, J C; Zanello, L; Zanetti, A; Zaw, I; Zhang, X; Zhou, J; Zucchelli, S

2007-04-06

We present a measurement of the top-quark mass Mtop in the all-hadronic decay channel tt-->W+bW-b-->q1q2bq3q4b. The analysis is performed using 310 pb-1 of sqrt[s]=1.96 TeV pp[over ] collisions collected with the CDF II detector using a multijet trigger. The mass measurement is based on an event-by-event likelihood which depends on both the sample purity and the value of the top-quark mass, using 90 possible jet-to-parton assignments in the six-jet final state. The joint likelihood of 290 selected events yields a value of Mtop=177.1+/-4.9(stat)+/-4.7(syst) GeV/c2.

Paule‐Mandel estimators for network meta‐analysis with random inconsistency effects

PubMed Central

Veroniki, Areti Angeliki; Law, Martin; Tricco, Andrea C.; Baker, Rose

2017-01-01

Network meta‐analysis is used to simultaneously compare multiple treatments in a single analysis. However, network meta‐analyses may exhibit inconsistency, where direct and different forms of indirect evidence are not in agreement with each other, even after allowing for between‐study heterogeneity. Models for network meta‐analysis with random inconsistency effects have the dual aim of allowing for inconsistencies and estimating average treatment effects across the whole network. To date, two classical estimation methods for fitting this type of model have been developed: a method of moments that extends DerSimonian and Laird's univariate method and maximum likelihood estimation. However, the Paule and Mandel estimator is another recommended classical estimation method for univariate meta‐analysis. In this paper, we extend the Paule and Mandel method so that it can be used to fit models for network meta‐analysis with random inconsistency effects. We apply all three estimation methods to a variety of examples that have been used previously and we also examine a challenging new dataset that is highly heterogenous. We perform a simulation study based on this new example. We find that the proposed Paule and Mandel method performs satisfactorily and generally better than the previously proposed method of moments because it provides more accurate inferences. Furthermore, the Paule and Mandel method possesses some advantages over likelihood‐based methods because it is both semiparametric and requires no convergence diagnostics. Although restricted maximum likelihood estimation remains the gold standard, the proposed methodology is a fully viable alternative to this and other estimation methods. PMID:28585257

Dental infections increase the likelihood of hospital admissions among adult patients with sickle cell disease

PubMed Central

Laurence, B.; Haywood, C; Lanzkron, S.

2014-01-01

The objective To determine if dental infections increase the likelihood of hospital admission among adult patients with sickle cell disease (SCD). Basic Research Design Cross-sectional analysis of data from the Nationwide Emergency Department Sample (NEDS) pooled for the years 2006 through 2008. Prevalence ratios (PR) for the effects of interest were estimated using Poisson regression with robust estimates of the variance. Participants Adults, aged 18 and over, diagnosed with SCD using ICD-9-CM codes excluding participants discharged with a code for sickle cell trait. Main outcome measure Emergency department (ED) visit disposition, dichotomised to represent whether or not the ED visit ended in admission versus being treated and released. Results Among patients having a sickle cell crisis, those with dental infections were 72% more likely to be admitted compared to those not having dental infections (PR=1.72, 95%CI 1.58-1.87). No association was observed among adult SCD patients not having a sickle crisis event. Based on preliminary data from this analysis, prevention of dental infection among patients with SCD could result in an estimated cost saving of $2.5 million dollars per year. Conclusions Having a dental infection complicated by a sickle cell crisis significantly increases the likelihood of hospital admission among adult SCD patients presenting to the ED. PMID:24151791

Bayesian Hierarchical Random Effects Models in Forensic Science.

PubMed

Aitken, Colin G G

2018-01-01

Statistical modeling of the evaluation of evidence with the use of the likelihood ratio has a long history. It dates from the Dreyfus case at the end of the nineteenth century through the work at Bletchley Park in the Second World War to the present day. The development received a significant boost in 1977 with a seminal work by Dennis Lindley which introduced a Bayesian hierarchical random effects model for the evaluation of evidence with an example of refractive index measurements on fragments of glass. Many models have been developed since then. The methods have now been sufficiently well-developed and have become so widespread that it is timely to try and provide a software package to assist in their implementation. With that in mind, a project (SAILR: Software for the Analysis and Implementation of Likelihood Ratios) was funded by the European Network of Forensic Science Institutes through their Monopoly programme to develop a software package for use by forensic scientists world-wide that would assist in the statistical analysis and implementation of the approach based on likelihood ratios. It is the purpose of this document to provide a short review of a small part of this history. The review also provides a background, or landscape, for the development of some of the models within the SAILR package and references to SAILR as made as appropriate.

Bias correction in the hierarchical likelihood approach to the analysis of multivariate survival data.

PubMed

Jeon, Jihyoun; Hsu, Li; Gorfine, Malka

2012-07-01

Frailty models are useful for measuring unobserved heterogeneity in risk of failures across clusters, providing cluster-specific risk prediction. In a frailty model, the latent frailties shared by members within a cluster are assumed to act multiplicatively on the hazard function. In order to obtain parameter and frailty variate estimates, we consider the hierarchical likelihood (H-likelihood) approach (Ha, Lee and Song, 2001. Hierarchical-likelihood approach for frailty models. Biometrika 88, 233-243) in which the latent frailties are treated as "parameters" and estimated jointly with other parameters of interest. We find that the H-likelihood estimators perform well when the censoring rate is low, however, they are substantially biased when the censoring rate is moderate to high. In this paper, we propose a simple and easy-to-implement bias correction method for the H-likelihood estimators under a shared frailty model. We also extend the method to a multivariate frailty model, which incorporates complex dependence structure within clusters. We conduct an extensive simulation study and show that the proposed approach performs very well for censoring rates as high as 80%. We also illustrate the method with a breast cancer data set. Since the H-likelihood is the same as the penalized likelihood function, the proposed bias correction method is also applicable to the penalized likelihood estimators.

Link Prediction in Evolving Networks Based on Popularity of Nodes.

PubMed

Wang, Tong; He, Xing-Sheng; Zhou, Ming-Yang; Fu, Zhong-Qian

2017-08-02

Link prediction aims to uncover the underlying relationship behind networks, which could be utilized to predict missing edges or identify the spurious edges. The key issue of link prediction is to estimate the likelihood of potential links in networks. Most classical static-structure based methods ignore the temporal aspects of networks, limited by the time-varying features, such approaches perform poorly in evolving networks. In this paper, we propose a hypothesis that the ability of each node to attract links depends not only on its structural importance, but also on its current popularity (activeness), since active nodes have much more probability to attract future links. Then a novel approach named popularity based structural perturbation method (PBSPM) and its fast algorithm are proposed to characterize the likelihood of an edge from both existing connectivity structure and current popularity of its two endpoints. Experiments on six evolving networks show that the proposed methods outperform state-of-the-art methods in accuracy and robustness. Besides, visual results and statistical analysis reveal that the proposed methods are inclined to predict future edges between active nodes, rather than edges between inactive nodes.

Small area estimation for estimating the number of infant mortality in West Java, Indonesia

NASA Astrophysics Data System (ADS)

Anggreyani, Arie; Indahwati, Kurnia, Anang

2016-02-01

Demographic and Health Survey Indonesia (DHSI) is a national designed survey to provide information regarding birth rate, mortality rate, family planning and health. DHSI was conducted by BPS in cooperation with National Population and Family Planning Institution (BKKBN), Indonesia Ministry of Health (KEMENKES) and USAID. Based on the publication of DHSI 2012, the infant mortality rate for a period of five years before survey conducted is 32 for 1000 birth lives. In this paper, Small Area Estimation (SAE) is used to estimate the number of infant mortality in districts of West Java. SAE is a special model of Generalized Linear Mixed Models (GLMM). In this case, the incidence of infant mortality is a Poisson distribution which has equdispersion assumption. The methods to handle overdispersion are binomial negative and quasi-likelihood model. Based on the results of analysis, quasi-likelihood model is the best model to overcome overdispersion problem. The basic model of the small area estimation used basic area level model. Mean square error (MSE) which based on resampling method is used to measure the accuracy of small area estimates.

A wavelet-based Bayesian framework for 3D object segmentation in microscopy

NASA Astrophysics Data System (ADS)

Pan, Kangyu; Corrigan, David; Hillebrand, Jens; Ramaswami, Mani; Kokaram, Anil

2012-03-01

In confocal microscopy, target objects are labeled with fluorescent markers in the living specimen, and usually appear with irregular brightness in the observed images. Also, due to the existence of out-of-focus objects in the image, the segmentation of 3-D objects in the stack of image slices captured at different depth levels of the specimen is still heavily relied on manual analysis. In this paper, a novel Bayesian model is proposed for segmenting 3-D synaptic objects from given image stack. In order to solve the irregular brightness and out-offocus problems, the segmentation model employs a likelihood using the luminance-invariant 'wavelet features' of image objects in the dual-tree complex wavelet domain as well as a likelihood based on the vertical intensity profile of the image stack in 3-D. Furthermore, a smoothness 'frame' prior based on the a priori knowledge of the connections of the synapses is introduced to the model for enhancing the connectivity of the synapses. As a result, our model can successfully segment the in-focus target synaptic object from a 3D image stack with irregular brightness.

Dynamic Histogram Analysis To Determine Free Energies and Rates from Biased Simulations.

PubMed

Stelzl, Lukas S; Kells, Adam; Rosta, Edina; Hummer, Gerhard

2017-12-12

We present an algorithm to calculate free energies and rates from molecular simulations on biased potential energy surfaces. As input, it uses the accumulated times spent in each state or bin of a histogram and counts of transitions between them. Optimal unbiased equilibrium free energies for each of the states/bins are then obtained by maximizing the likelihood of a master equation (i.e., first-order kinetic rate model). The resulting free energies also determine the optimal rate coefficients for transitions between the states or bins on the biased potentials. Unbiased rates can be estimated, e.g., by imposing a linear free energy condition in the likelihood maximization. The resulting "dynamic histogram analysis method extended to detailed balance" (DHAMed) builds on the DHAM method. It is also closely related to the transition-based reweighting analysis method (TRAM) and the discrete TRAM (dTRAM). However, in the continuous-time formulation of DHAMed, the detailed balance constraints are more easily accounted for, resulting in compact expressions amenable to efficient numerical treatment. DHAMed produces accurate free energies in cases where the common weighted-histogram analysis method (WHAM) for umbrella sampling fails because of slow dynamics within the windows. Even in the limit of completely uncorrelated data, where WHAM is optimal in the maximum-likelihood sense, DHAMed results are nearly indistinguishable. We illustrate DHAMed with applications to ion channel conduction, RNA duplex formation, α-helix folding, and rate calculations from accelerated molecular dynamics. DHAMed can also be used to construct Markov state models from biased or replica-exchange molecular dynamics simulations. By using binless WHAM formulated as a numerical minimization problem, the bias factors for the individual states can be determined efficiently in a preprocessing step and, if needed, optimized globally afterward.

Maximum likelihood decoding analysis of accumulate-repeat-accumulate codes

NASA Technical Reports Server (NTRS)

Abbasfar, A.; Divsalar, D.; Yao, K.

2004-01-01

In this paper, the performance of the repeat-accumulate codes with (ML) decoding are analyzed and compared to random codes by very tight bounds. Some simple codes are shown that perform very close to Shannon limit with maximum likelihood decoding.

Technical Note: Approximate Bayesian parameterization of a complex tropical forest model

NASA Astrophysics Data System (ADS)

Hartig, F.; Dislich, C.; Wiegand, T.; Huth, A.

2013-08-01

Inverse parameter estimation of process-based models is a long-standing problem in ecology and evolution. A key problem of inverse parameter estimation is to define a metric that quantifies how well model predictions fit to the data. Such a metric can be expressed by general cost or objective functions, but statistical inversion approaches are based on a particular metric, the probability of observing the data given the model, known as the likelihood. Deriving likelihoods for dynamic models requires making assumptions about the probability for observations to deviate from mean model predictions. For technical reasons, these assumptions are usually derived without explicit consideration of the processes in the simulation. Only in recent years have new methods become available that allow generating likelihoods directly from stochastic simulations. Previous applications of these approximate Bayesian methods have concentrated on relatively simple models. Here, we report on the application of a simulation-based likelihood approximation for FORMIND, a parameter-rich individual-based model of tropical forest dynamics. We show that approximate Bayesian inference, based on a parametric likelihood approximation placed in a conventional MCMC, performs well in retrieving known parameter values from virtual field data generated by the forest model. We analyze the results of the parameter estimation, examine the sensitivity towards the choice and aggregation of model outputs and observed data (summary statistics), and show results from using this method to fit the FORMIND model to field data from an Ecuadorian tropical forest. Finally, we discuss differences of this approach to Approximate Bayesian Computing (ABC), another commonly used method to generate simulation-based likelihood approximations. Our results demonstrate that simulation-based inference, which offers considerable conceptual advantages over more traditional methods for inverse parameter estimation, can successfully be applied to process-based models of high complexity. The methodology is particularly suited to heterogeneous and complex data structures and can easily be adjusted to other model types, including most stochastic population and individual-based models. Our study therefore provides a blueprint for a fairly general approach to parameter estimation of stochastic process-based models in ecology and evolution.

The weight of networks: the role of social ties and ethnic media in mitigating obesity and hypertension among Latinas.

PubMed

Walter, Nathan; Robbins, Chris; Murphy, Sheila T; Ball-Rokeach, Sandra J

2017-09-01

Latinos have a disproportionately high risk for obesity and hypertension. The current study analyzes survey data from Latin American women to detect differences in rates of obesity and hypertension based on their number of health-related social ties. Additionally, it proposes individuals' health-related media preference (ethnic/ mainstream) as a potential moderator. The dataset includes 364 Latinas (21-50 years old) from the greater Los Angeles metropolitan area, who responded to a series of sociodemographic, physiological, health-related, and media-related questions. Controlling for various sociodemographic and health variables, each additional health-related tie in a Latina's social network significantly decreased her likelihood of being obese OR = .79, p = .041, 95% CI [.66, .95], but did not affect hypertension. Further, the analysis revealed a significant interaction between media preference and health-related social ties, such that exposure to ethnic media tended to compensate for the absence of social ties for the likelihood of obesity OR = .75, p = .041, 95% CI [.52, .97], as well as hypertension OR = .79, p = .045, 95% CI [.55, .98]. In concurrence with the literature, increases in health-related ties reduced the likelihood of obesity in this population. Moreover, ethnic media preference may play an important role in mitigating the likelihood of obesity and hypertension among Latinas.

Disability Among Veterans: Analysis of the National Survey of Veterans (1997-2001).

PubMed

Gerber, Lynn H; Weinstein, Ali A; Frankenfeld, Cara L; Huynh, Minh

2016-03-01

This manuscript assesses whether the Veterans Administration Rating System (VADR) correlates with self-reported activities of daily living (ADL) used in the National Survey of Veterans and likelihood of employment. Veterans' disability benefits are determined based on a single-index standardized rating scheme, measured at time of discharge. The primary aim of this study was to assess how this single-index rating of disability for veterans compares to multidimensional measures of disability (ADL and instrumental activities of daily living [IADL]). The relationship between disability ratings and labor market outcomes such as job search behavior and the likelihood of being employed was assessed. Successful labor market reintegration requires both physical/mental well-being, we examined the extent that VADR can capture the relationship between job market behavior and measures of mental/physical health. Kernel regression estimates were obtained of the likelihood of working/looking for work. Mean numbers of IADL and ADL difficulties and medical conditions were positively associated with VADR (p-trend < 0.001). An inverse relationship was observed with VADR and predicted probability of working (p-trend < 0.001). The combination of >4 ADL/IADL deficits and mental health diagnosis increased the likelihood of not working. The probability of not working correlated with VADR when VADR was greater than 40%. Reprint & Copyright © 2016 Association of Military Surgeons of the U.S.

Probabilistic evaluation of n traces with no putative source: A likelihood ratio based approach in an investigative framework.

PubMed

De March, I; Sironi, E; Taroni, F

2016-09-01

Analysis of marks recovered from different crime scenes can be useful to detect a linkage between criminal cases, even though a putative source for the recovered traces is not available. This particular circumstance is often encountered in the early stage of investigations and thus, the evaluation of evidence association may provide useful information for the investigators. This association is evaluated here from a probabilistic point of view: a likelihood ratio based approach is suggested in order to quantify the strength of the evidence of trace association in the light of two mutually exclusive propositions, namely that the n traces come from a common source or from an unspecified number of sources. To deal with this kind of problem, probabilistic graphical models are used, in form of Bayesian networks and object-oriented Bayesian networks, allowing users to intuitively handle with uncertainty related to the inferential problem. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Is the first seizure epilepsy--and when?

PubMed

Lawn, Nicholas; Chan, Josephine; Lee, Judy; Dunne, John

2015-09-01

Epilepsy has recently been redefined to include a single unprovoked seizure if the probability of recurrence is ≥60% over the following 10 years. This definition is based on the estimated risk of a third seizure after two unprovoked seizures, using the lower-limit 95% confidence interval (CI) at 4 years, and does not account for the initially high recurrence rate after first-ever seizure that rapidly falls with increasing duration of seizure freedom. We analyzed long-term outcomes after the first-ever seizure, and the influence of duration of seizure freedom on the likelihood of seizure recurrence, and their relevance to the new definition of epilepsy. Prospective analysis of 798 adults with a first-ever unprovoked seizure seen at a hospital-based first seizure clinic between 2000 and 2011. The likelihood of seizure recurrence was analyzed according to the duration of seizure freedom, etiology, electroencephalography (EEG), and neuroimaging findings. The likelihood of seizure recurrence at 10 years was ≥60% in patients with epileptiform abnormalities on EEG or neuroimaging abnormalities, therefore, meeting the new definition of epilepsy. However, the risk of recurrence was highly time dependent; after a brief period (≤12 weeks) of seizure freedom, no patient group continued to fulfill the new definition of epilepsy. Of 407 patients who had a second seizure, the likelihood of a third seizure at 4 years was 68% (95% CI 63-73%) and at 10 years was 85% (95% CI 79-91%). The duration of seizure freedom following first-ever seizure substantially influences the risk of recurrence, with none of our patients fulfilling the new definition of epilepsy after a short period of seizure freedom. When a threshold was applied based on the 10-year risk of a third seizure from our data, no first-seizure patient group ever had epilepsy. These data may be utilized in a definition of epilepsy after a first-ever seizure. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

Hospital mergers and market overlap.

PubMed Central

Brooks, G R; Jones, V G

1997-01-01

OBJECTIVE: To address two questions: What are the characteristics of hospitals that affect the likelihood of their being involved in a merger? What characteristics of particular pairs of hospitals affect the likelihood of the pair engaging in a merger? DATA SOURCES/STUDY SETTING: Hospitals in the 12 county region surrounding the San Francisco Bay during the period 1983 to 1992 were the focus of the study. Data were drawn from secondary sources, including the Lexis/Nexis database, the American Hospital Association, and the Office of Statewide Health Planning and Development of the State of California. STUDY DESIGN: Seventeen hospital mergers during the study period were identified. A random sample of pairs of hospitals that did not merge was drawn to establish a statistically efficient control set. Models constructed from hypotheses regarding hospital and market characteristics believed to be related to merger likelihood were tested using logistic regression analysis. DATA COLLECTION: See Data Sources/Study Setting. PRINCIPAL FINDINGS: The analysis shows that the likelihood of a merger between a particular pair of hospitals is positively related to the degree of market overlap that exists between them. Furthermore, market overlap and performance difference interact in their effect on merger likelihood. In an analysis of individual hospitals, conditions of rivalry, hospital market share, and hospital size were not found to influence the likelihood that a hospital will engage in a merger. CONCLUSIONS: Mergers between hospitals are not driven directly by considerations of market power or efficiency as much as by the existence of specific merger opportunities in the hospitals' local markets. Market overlap is a condition that enables a merger to occur, but other factors, such as the relative performance levels of the hospitals in question and their ownership and teaching status, also play a role in influencing the likelihood that a merger will in fact take place. PMID:9018212

Effects of Estimation Bias on Multiple-Category Classification with an IRT-Based Adaptive Classification Procedure

ERIC Educational Resources Information Center

Yang, Xiangdong; Poggio, John C.; Glasnapp, Douglas R.

2006-01-01

The effects of five ability estimators, that is, maximum likelihood estimator, weighted likelihood estimator, maximum a posteriori, expected a posteriori, and Owen's sequential estimator, on the performances of the item response theory-based adaptive classification procedure on multiple categories were studied via simulations. The following…

Robust Gaussian Graphical Modeling via l1 Penalization

PubMed Central

Sun, Hokeun; Li, Hongzhe

2012-01-01

Summary Gaussian graphical models have been widely used as an effective method for studying the conditional independency structure among genes and for constructing genetic networks. However, gene expression data typically have heavier tails or more outlying observations than the standard Gaussian distribution. Such outliers in gene expression data can lead to wrong inference on the dependency structure among the genes. We propose a l1 penalized estimation procedure for the sparse Gaussian graphical models that is robustified against possible outliers. The likelihood function is weighted according to how the observation is deviated, where the deviation of the observation is measured based on its own likelihood. An efficient computational algorithm based on the coordinate gradient descent method is developed to obtain the minimizer of the negative penalized robustified-likelihood, where nonzero elements of the concentration matrix represents the graphical links among the genes. After the graphical structure is obtained, we re-estimate the positive definite concentration matrix using an iterative proportional fitting algorithm. Through simulations, we demonstrate that the proposed robust method performs much better than the graphical Lasso for the Gaussian graphical models in terms of both graph structure selection and estimation when outliers are present. We apply the robust estimation procedure to an analysis of yeast gene expression data and show that the resulting graph has better biological interpretation than that obtained from the graphical Lasso. PMID:23020775

Calibration of two complex ecosystem models with different likelihood functions

NASA Astrophysics Data System (ADS)

Hidy, Dóra; Haszpra, László; Pintér, Krisztina; Nagy, Zoltán; Barcza, Zoltán

2014-05-01

The biosphere is a sensitive carbon reservoir. Terrestrial ecosystems were approximately carbon neutral during the past centuries, but they became net carbon sinks due to climate change induced environmental change and associated CO2 fertilization effect of the atmosphere. Model studies and measurements indicate that the biospheric carbon sink can saturate in the future due to ongoing climate change which can act as a positive feedback. Robustness of carbon cycle models is a key issue when trying to choose the appropriate model for decision support. The input parameters of the process-based models are decisive regarding the model output. At the same time there are several input parameters for which accurate values are hard to obtain directly from experiments or no local measurements are available. Due to the uncertainty associated with the unknown model parameters significant bias can be experienced if the model is used to simulate the carbon and nitrogen cycle components of different ecosystems. In order to improve model performance the unknown model parameters has to be estimated. We developed a multi-objective, two-step calibration method based on Bayesian approach in order to estimate the unknown parameters of PaSim and Biome-BGC models. Biome-BGC and PaSim are a widely used biogeochemical models that simulate the storage and flux of water, carbon, and nitrogen between the ecosystem and the atmosphere, and within the components of the terrestrial ecosystems (in this research the developed version of Biome-BGC is used which is referred as BBGC MuSo). Both models were calibrated regardless the simulated processes and type of model parameters. The calibration procedure is based on the comparison of measured data with simulated results via calculating a likelihood function (degree of goodness-of-fit between simulated and measured data). In our research different likelihood function formulations were used in order to examine the effect of the different model goodness metric on calibration. The different likelihoods are different functions of RMSE (root mean squared error) weighted by measurement uncertainty: exponential / linear / quadratic / linear normalized by correlation. As a first calibration step sensitivity analysis was performed in order to select the influential parameters which have strong effect on the output data. In the second calibration step only the sensitive parameters were calibrated (optimal values and confidence intervals were calculated). In case of PaSim more parameters were found responsible for the 95% of the output data variance than is case of BBGC MuSo. Analysis of the results of the optimized models revealed that the exponential likelihood estimation proved to be the most robust (best model simulation with optimized parameter, highest confidence interval increase). The cross-validation of the model simulations can help in constraining the highly uncertain greenhouse gas budget of grasslands.

Rules or consequences? The role of ethical mind-sets in moral dynamics.

PubMed

Cornelissen, Gert; Bashshur, Michael R; Rode, Julian; Le Menestrel, Marc

2013-04-01

Recent research on the dynamics of moral behavior has documented two contrasting phenomena-moral consistency and moral balancing. Moral balancing refers to the phenomenon whereby behaving ethically or unethically decreases the likelihood of engaging in the same type of behavior again later. Moral consistency describes the opposite pattern-engaging in ethical or unethical behavior increases the likelihood of engaging in the same type of behavior later on. The three studies reported here supported the hypothesis that individuals' ethical mind-set (i.e., outcome-based vs. rule-based) moderates the impact of an initial ethical or unethical act on the likelihood of behaving ethically on a subsequent occasion. More specifically, an outcome-based mind-set facilitated moral balancing, and a rule-based mind-set facilitated moral consistency.

Impacts of rising health care costs on families with employment-based private insurance: a national analysis with state fixed effects.

PubMed

Yu, Hao; Dick, Andrew W

2012-10-01

Given the rapid growth of health care costs, some experts were concerned with erosion of employment-based private insurance (EBPI). This empirical analysis aims to quantify the concern. Using the National Health Account, we generated a cost index to represent state-level annual cost growth. We merged it with the 1996-2003 Medical Expenditure Panel Survey. The unit of analysis is the family. We conducted both bivariate and multivariate logistic analyses. The bivariate analysis found a significant inverse association between the cost index and the proportion of families receiving an offer of EBPI. The multivariate analysis showed that the cost index was significantly negatively associated with the likelihood of receiving an EBPI offer for the entire sample and for families in the first, second, and third quartiles of income distribution. The cost index was also significantly negatively associated with the proportion of families with EBPI for the entire year for each family member (EBPI-EYEM). The multivariate analysis confirmed significance of the relationship for the entire sample, and for families in the second and third quartiles of income distribution. Among the families with EBPI-EYEM, there was a positive relationship between the cost index and this group's likelihood of having out-of-pocket expenditures exceeding 10 percent of family income. The multivariate analysis confirmed significance of the relationship for the entire group and for families in the second and third quartiles of income distribution. Rising health costs reduce EBPI availability and enrollment, and the financial protection provided by it, especially for middle-class families. © Health Research and Educational Trust.

A meta-analysis of confocal laser endomicroscopy for the detection of neoplasia in patients with Barrett's esophagus.

PubMed

Xiong, Yi-Quan; Ma, Shu-Juan; Zhou, Jun-Hua; Zhong, Xue-Shan; Chen, Qing

2016-06-01

Barrett's esophagus (BE) is considered the most important risk factor for development of esophageal adenocarcinoma. Confocal laser endomicroscopy (CLE) is a recently developed technique used to diagnose neoplasia in BE. This meta-analysis was performed to assess the accuracy of CLE for diagnosis of neoplasia in BE. We searched EMBASE, PubMed, Cochrane Library, and Web of Science to identify relevant studies for all articles published up to June 27, 2015 in English. The quality of included studies was assessed using QUADAS-2. Per-patient and per-lesion pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio with 95% confidence intervals (CIs) were calculated. In total, 14 studies were included in the final analysis, covering 789 patients with 4047 lesions. Seven studies were included in the per-patient analysis. Pooled sensitivity and specificity were 89% (95% CI: 0.82-0.94) and 83% (95% CI: 0.78-0.86), respectively. Ten studies were included in the per-lesion analysis. Compared with the PP analysis, the corresponding pooled sensitivity declined to 77% (95% CI: 0.73-0.81) and specificity increased to 89% (95% CI: 0.87-0.90). Subgroup analysis showed that probe-based CLE (pCLE) was superior to endoscope-based CLE (eCLE) in pooled specificity [91.4% (95% CI: 89.7-92.9) vs 86.1% (95% CI: 84.3-87.8)] and AUC for the sROC (0.885 vs 0.762). Confocal laser endomicroscopy is a valid method to accurately differentiate neoplasms from non-neoplasms in BE. It can be applied to BE surveillance and early diagnosis of esophageal adenocarcinoma. © 2015 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Impacts of Rising Health Care Costs on Families with Employment-Based Private Insurance: A National Analysis with State Fixed Effects

PubMed Central

Yu, Hao; Dick, Andrew W

2012-01-01

Background Given the rapid growth of health care costs, some experts were concerned with erosion of employment-based private insurance (EBPI). This empirical analysis aims to quantify the concern. Methods Using the National Health Account, we generated a cost index to represent state-level annual cost growth. We merged it with the 1996–2003 Medical Expenditure Panel Survey. The unit of analysis is the family. We conducted both bivariate and multivariate logistic analyses. Results The bivariate analysis found a significant inverse association between the cost index and the proportion of families receiving an offer of EBPI. The multivariate analysis showed that the cost index was significantly negatively associated with the likelihood of receiving an EBPI offer for the entire sample and for families in the first, second, and third quartiles of income distribution. The cost index was also significantly negatively associated with the proportion of families with EBPI for the entire year for each family member (EBPI-EYEM). The multivariate analysis confirmed significance of the relationship for the entire sample, and for families in the second and third quartiles of income distribution. Among the families with EBPI-EYEM, there was a positive relationship between the cost index and this group's likelihood of having out-of-pocket expenditures exceeding 10 percent of family income. The multivariate analysis confirmed significance of the relationship for the entire group and for families in the second and third quartiles of income distribution. Conclusions Rising health costs reduce EBPI availability and enrollment, and the financial protection provided by it, especially for middle-class families. PMID:22417314

Closed-loop carrier phase synchronization techniques motivated by likelihood functions

NASA Technical Reports Server (NTRS)

Tsou, H.; Hinedi, S.; Simon, M.

1994-01-01

This article reexamines the notion of closed-loop carrier phase synchronization motivated by the theory of maximum a posteriori phase estimation with emphasis on the development of new structures based on both maximum-likelihood and average-likelihood functions. The criterion of performance used for comparison of all the closed-loop structures discussed is the mean-squared phase error for a fixed-loop bandwidth.

Maximum likelihood estimation of signal-to-noise ratio and combiner weight

NASA Technical Reports Server (NTRS)

Kalson, S.; Dolinar, S. J.

1986-01-01

An algorithm for estimating signal to noise ratio and combiner weight parameters for a discrete time series is presented. The algorithm is based upon the joint maximum likelihood estimate of the signal and noise power. The discrete-time series are the sufficient statistics obtained after matched filtering of a biphase modulated signal in additive white Gaussian noise, before maximum likelihood decoding is performed.

Planck 2015 results: XI. CMB power spectra, likelihoods, and robustness of parameters

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

Aghanim, N.; Arnaud, M.; Ashdown, M.

This study presents the Planck 2015 likelihoods, statistical descriptions of the 2-point correlationfunctions of the cosmic microwave background (CMB) temperature and polarization fluctuations that account for relevant uncertainties, both instrumental and astrophysical in nature. They are based on the same hybrid approach used for the previous release, i.e., a pixel-based likelihood at low multipoles (ℓ< 30) and a Gaussian approximation to the distribution of cross-power spectra at higher multipoles. The main improvements are the use of more and better processed data and of Planck polarization information, along with more detailed models of foregrounds and instrumental uncertainties. The increased redundancy broughtmore » by more than doubling the amount of data analysed enables further consistency checks and enhanced immunity to systematic effects. It also improves the constraining power of Planck, in particular with regard to small-scale foreground properties. Progress in the modelling of foreground emission enables the retention of a larger fraction of the sky to determine the properties of the CMB, which also contributes to the enhanced precision of the spectra. Improvements in data processing and instrumental modelling further reduce uncertainties. Extensive tests establish the robustness and accuracy of the likelihood results, from temperature alone, from polarization alone, and from their combination. For temperature, we also perform a full likelihood analysis of realistic end-to-end simulations of the instrumental response to the sky, which were fed into the actual data processing pipeline; this does not reveal biases from residual low-level instrumental systematics. Even with the increase in precision and robustness, the ΛCDM cosmological model continues to offer a very good fit to the Planck data. The slope of the primordial scalar fluctuations, n s, is confirmed smaller than unity at more than 5σ from Planck alone. We further validate the robustness of the likelihood results against specific extensions to the baseline cosmology, which are particularly sensitive to data at high multipoles. For instance, the effective number of neutrino species remains compatible with the canonical value of 3.046. For this first detailed analysis of Planck polarization spectra, we concentrate at high multipoles on the E modes, leaving the analysis of the weaker B modes to future work. At low multipoles we use temperature maps at all Planck frequencies along with a subset of polarization data. These data take advantage of Planck’s wide frequency coverage to improve the separation of CMB and foreground emission. Within the baseline ΛCDM cosmology this requires τ = 0.078 ± 0.019 for the reionization optical depth, which is significantly lower than estimates without the use of high-frequency data for explicit monitoring of dust emission. At high multipoles we detect residual systematic errors in E polarization, typically at the μK 2 level; we therefore choose to retain temperature information alone for high multipoles as the recommended baseline, in particular for testing non-minimal models. Finally and nevertheless, the high-multipole polarization spectra from Planck are already good enough to enable a separate high-precision determination of the parameters of the ΛCDM model, showing consistency with those established independently from temperature information alone.« less

Planck 2015 results. XI. CMB power spectra, likelihoods, and robustness of parameters

NASA Astrophysics Data System (ADS)

Planck Collaboration; Aghanim, N.; Arnaud, M.; Ashdown, M.; Aumont, J.; Baccigalupi, C.; Banday, A. J.; Barreiro, R. B.; Bartlett, J. G.; Bartolo, N.; Battaner, E.; Benabed, K.; Benoît, A.; Benoit-Lévy, A.; Bernard, J.-P.; Bersanelli, M.; Bielewicz, P.; Bock, J. J.; Bonaldi, A.; Bonavera, L.; Bond, J. R.; Borrill, J.; Bouchet, F. R.; Boulanger, F.; Bucher, M.; Burigana, C.; Butler, R. C.; Calabrese, E.; Cardoso, J.-F.; Catalano, A.; Challinor, A.; Chiang, H. C.; Christensen, P. R.; Clements, D. L.; Colombo, L. P. L.; Combet, C.; Coulais, A.; Crill, B. P.; Curto, A.; Cuttaia, F.; Danese, L.; Davies, R. D.; Davis, R. J.; de Bernardis, P.; de Rosa, A.; de Zotti, G.; Delabrouille, J.; Désert, F.-X.; Di Valentino, E.; Dickinson, C.; Diego, J. M.; Dolag, K.; Dole, H.; Donzelli, S.; Doré, O.; Douspis, M.; Ducout, A.; Dunkley, J.; Dupac, X.; Efstathiou, G.; Elsner, F.; Enßlin, T. A.; Eriksen, H. K.; Fergusson, J.; Finelli, F.; Forni, O.; Frailis, M.; Fraisse, A. A.; Franceschi, E.; Frejsel, A.; Galeotta, S.; Galli, S.; Ganga, K.; Gauthier, C.; Gerbino, M.; Giard, M.; Gjerløw, E.; González-Nuevo, J.; Górski, K. M.; Gratton, S.; Gregorio, A.; Gruppuso, A.; Gudmundsson, J. E.; Hamann, J.; Hansen, F. K.; Harrison, D. L.; Helou, G.; Henrot-Versillé, S.; Hernández-Monteagudo, C.; Herranz, D.; Hildebrandt, S. R.; Hivon, E.; Holmes, W. A.; Hornstrup, A.; Huffenberger, K. M.; Hurier, G.; Jaffe, A. H.; Jones, W. C.; Juvela, M.; Keihänen, E.; Keskitalo, R.; Kiiveri, K.; Knoche, J.; Knox, L.; Kunz, M.; Kurki-Suonio, H.; Lagache, G.; Lähteenmäki, A.; Lamarre, J.-M.; Lasenby, A.; Lattanzi, M.; Lawrence, C. R.; Le Jeune, M.; Leonardi, R.; Lesgourgues, J.; Levrier, F.; Lewis, A.; Liguori, M.; Lilje, P. B.; Lilley, M.; Linden-Vørnle, M.; Lindholm, V.; López-Caniego, M.; Macías-Pérez, J. F.; Maffei, B.; Maggio, G.; Maino, D.; Mandolesi, N.; Mangilli, A.; Maris, M.; Martin, P. G.; Martínez-González, E.; Masi, S.; Matarrese, S.; Meinhold, P. R.; Melchiorri, A.; Migliaccio, M.; Millea, M.; Mitra, S.; Miville-Deschênes, M.-A.; Moneti, A.; Montier, L.; Morgante, G.; Mortlock, D.; Mottet, S.; Munshi, D.; Murphy, J. A.; Narimani, A.; Naselsky, P.; Nati, F.; Natoli, P.; Noviello, F.; Novikov, D.; Novikov, I.; Oxborrow, C. A.; Paci, F.; Pagano, L.; Pajot, F.; Paoletti, D.; Partridge, B.; Pasian, F.; Patanchon, G.; Pearson, T. J.; Perdereau, O.; Perotto, L.; Pettorino, V.; Piacentini, F.; Piat, M.; Pierpaoli, E.; Pietrobon, D.; Plaszczynski, S.; Pointecouteau, E.; Polenta, G.; Ponthieu, N.; Pratt, G. W.; Prunet, S.; Puget, J.-L.; Rachen, J. P.; Reinecke, M.; Remazeilles, M.; Renault, C.; Renzi, A.; Ristorcelli, I.; Rocha, G.; Rossetti, M.; Roudier, G.; Rouillé d'Orfeuil, B.; Rubiño-Martín, J. A.; Rusholme, B.; Salvati, L.; Sandri, M.; Santos, D.; Savelainen, M.; Savini, G.; Scott, D.; Serra, P.; Spencer, L. D.; Spinelli, M.; Stolyarov, V.; Stompor, R.; Sunyaev, R.; Sutton, D.; Suur-Uski, A.-S.; Sygnet, J.-F.; Tauber, J. A.; Terenzi, L.; Toffolatti, L.; Tomasi, M.; Tristram, M.; Trombetti, T.; Tucci, M.; Tuovinen, J.; Umana, G.; Valenziano, L.; Valiviita, J.; Van Tent, F.; Vielva, P.; Villa, F.; Wade, L. A.; Wandelt, B. D.; Wehus, I. K.; Yvon, D.; Zacchei, A.; Zonca, A.

2016-09-01

This paper presents the Planck 2015 likelihoods, statistical descriptions of the 2-point correlationfunctions of the cosmic microwave background (CMB) temperature and polarization fluctuations that account for relevant uncertainties, both instrumental and astrophysical in nature. They are based on the same hybrid approach used for the previous release, I.e., a pixel-based likelihood at low multipoles (ℓ< 30) and a Gaussian approximation to the distribution of cross-power spectra at higher multipoles. The main improvements are the use of more and better processed data and of Planck polarization information, along with more detailed models of foregrounds and instrumental uncertainties. The increased redundancy brought by more than doubling the amount of data analysed enables further consistency checks and enhanced immunity to systematic effects. It also improves the constraining power of Planck, in particular with regard to small-scale foreground properties. Progress in the modelling of foreground emission enables the retention of a larger fraction of the sky to determine the properties of the CMB, which also contributes to the enhanced precision of the spectra. Improvements in data processing and instrumental modelling further reduce uncertainties. Extensive tests establish the robustness and accuracy of the likelihood results, from temperature alone, from polarization alone, and from their combination. For temperature, we also perform a full likelihood analysis of realistic end-to-end simulations of the instrumental response to the sky, which were fed into the actual data processing pipeline; this does not reveal biases from residual low-level instrumental systematics. Even with the increase in precision and robustness, the ΛCDM cosmological model continues to offer a very good fit to the Planck data. The slope of the primordial scalar fluctuations, ns, is confirmed smaller than unity at more than 5σ from Planck alone. We further validate the robustness of the likelihood results against specific extensions to the baseline cosmology, which are particularly sensitive to data at high multipoles. For instance, the effective number of neutrino species remains compatible with the canonical value of 3.046. For this first detailed analysis of Planck polarization spectra, we concentrate at high multipoles on the E modes, leaving the analysis of the weaker B modes to future work. At low multipoles we use temperature maps at all Planck frequencies along with a subset of polarization data. These data take advantage of Planck's wide frequency coverage to improve the separation of CMB and foreground emission. Within the baseline ΛCDM cosmology this requires τ = 0.078 ± 0.019 for the reionization optical depth, which is significantly lower than estimates without the use of high-frequency data for explicit monitoring of dust emission. At high multipoles we detect residual systematic errors in E polarization, typically at the μK2 level; we therefore choose to retain temperature information alone for high multipoles as the recommended baseline, in particular for testing non-minimal models. Nevertheless, the high-multipole polarization spectra from Planck are already good enough to enable a separate high-precision determination of the parameters of the ΛCDM model, showing consistency with those established independently from temperature information alone.

Planck 2015 results: XI. CMB power spectra, likelihoods, and robustness of parameters

DOE PAGES

Aghanim, N.; Arnaud, M.; Ashdown, M.; ...

2016-09-20

This study presents the Planck 2015 likelihoods, statistical descriptions of the 2-point correlationfunctions of the cosmic microwave background (CMB) temperature and polarization fluctuations that account for relevant uncertainties, both instrumental and astrophysical in nature. They are based on the same hybrid approach used for the previous release, i.e., a pixel-based likelihood at low multipoles (ℓ< 30) and a Gaussian approximation to the distribution of cross-power spectra at higher multipoles. The main improvements are the use of more and better processed data and of Planck polarization information, along with more detailed models of foregrounds and instrumental uncertainties. The increased redundancy broughtmore » by more than doubling the amount of data analysed enables further consistency checks and enhanced immunity to systematic effects. It also improves the constraining power of Planck, in particular with regard to small-scale foreground properties. Progress in the modelling of foreground emission enables the retention of a larger fraction of the sky to determine the properties of the CMB, which also contributes to the enhanced precision of the spectra. Improvements in data processing and instrumental modelling further reduce uncertainties. Extensive tests establish the robustness and accuracy of the likelihood results, from temperature alone, from polarization alone, and from their combination. For temperature, we also perform a full likelihood analysis of realistic end-to-end simulations of the instrumental response to the sky, which were fed into the actual data processing pipeline; this does not reveal biases from residual low-level instrumental systematics. Even with the increase in precision and robustness, the ΛCDM cosmological model continues to offer a very good fit to the Planck data. The slope of the primordial scalar fluctuations, n s, is confirmed smaller than unity at more than 5σ from Planck alone. We further validate the robustness of the likelihood results against specific extensions to the baseline cosmology, which are particularly sensitive to data at high multipoles. For instance, the effective number of neutrino species remains compatible with the canonical value of 3.046. For this first detailed analysis of Planck polarization spectra, we concentrate at high multipoles on the E modes, leaving the analysis of the weaker B modes to future work. At low multipoles we use temperature maps at all Planck frequencies along with a subset of polarization data. These data take advantage of Planck’s wide frequency coverage to improve the separation of CMB and foreground emission. Within the baseline ΛCDM cosmology this requires τ = 0.078 ± 0.019 for the reionization optical depth, which is significantly lower than estimates without the use of high-frequency data for explicit monitoring of dust emission. At high multipoles we detect residual systematic errors in E polarization, typically at the μK 2 level; we therefore choose to retain temperature information alone for high multipoles as the recommended baseline, in particular for testing non-minimal models. Finally and nevertheless, the high-multipole polarization spectra from Planck are already good enough to enable a separate high-precision determination of the parameters of the ΛCDM model, showing consistency with those established independently from temperature information alone.« less

A Walk on the Wild Side: The Impact of Music on Risk-Taking Likelihood.

PubMed

Enström, Rickard; Schmaltz, Rodney

2017-01-01

From a marketing perspective, there has been substantial interest in on the role of risk-perception on consumer behavior. Specific 'problem music' like rap and heavy metal has long been associated with delinquent behavior, including violence, drug use, and promiscuous sex. Although individuals' risk preferences have been investigated across a range of decision-making situations, there has been little empirical work demonstrating the direct role music may have on the likelihood of engaging in risky activities. In the exploratory study reported here, we assessed the impact of listening to different styles of music while assessing risk-taking likelihood through a psychometric scale. Risk-taking likelihood was measured across ethical, financial, health and safety, recreational and social domains. Through the means of a canonical correlation analysis, the multivariate relationship between different music styles and individual risk-taking likelihood across the different domains is discussed. Our results indicate that listening to different types of music does influence risk-taking likelihood, though not in areas of health and safety.

A Walk on the Wild Side: The Impact of Music on Risk-Taking Likelihood

PubMed Central

Enström, Rickard; Schmaltz, Rodney

2017-01-01

From a marketing perspective, there has been substantial interest in on the role of risk-perception on consumer behavior. Specific ‘problem music’ like rap and heavy metal has long been associated with delinquent behavior, including violence, drug use, and promiscuous sex. Although individuals’ risk preferences have been investigated across a range of decision-making situations, there has been little empirical work demonstrating the direct role music may have on the likelihood of engaging in risky activities. In the exploratory study reported here, we assessed the impact of listening to different styles of music while assessing risk-taking likelihood through a psychometric scale. Risk-taking likelihood was measured across ethical, financial, health and safety, recreational and social domains. Through the means of a canonical correlation analysis, the multivariate relationship between different music styles and individual risk-taking likelihood across the different domains is discussed. Our results indicate that listening to different types of music does influence risk-taking likelihood, though not in areas of health and safety. PMID:28539908

Controlling behavior, power relations within intimate relationships and intimate partner physical and sexual violence against women in Nigeria

PubMed Central

2011-01-01

Background Controlling behavior is more common and can be equally or more threatening than physical or sexual violence. This study sought to determine the role of husband/partner controlling behavior and power relations within intimate relationships in the lifetime risk of physical and sexual violence in Nigeria. Methods This study used secondary data from a cross-sectional nationally-representative survey collected by face-to-face interviews from women aged 15 - 49 years in the 2008 Nigeria Demographic and Health Survey. Utilizing a stratified two-stage cluster sample design, data was collected frrm 19 216 eligible with the DHS domestic violence module, which is based on the Conflict Tactics Scale (CTS). Multivariate logistic regression analysis was used to determine the role of husband/partner controlling behavior in the risk of ever experiencing physical and sexual violence among 2877 women aged 15 - 49 years who were currently or formerly married or cohabiting with a male partner. Results Women who reported controlling behavior by husband/partner had a higher likelihood of experiencing physical violence (RR = 3.04; 95% CI: 2.50 - 3.69), and women resident in rural areas and working in low status occupations had increased likelihood of experiencing physical IPV. Controlling behavior by husband/partner was associated with higher likelihood of experiencing physical violence (RR = 4.01; 95% CI: 2.54 - 6.34). In addition, women who justified wife beating and earned more than their husband/partner were at higher likelihood of experiencing physical and sexual violence. In contrast, women who had decision-making autonomy had lower likelihood of experiencing physical and sexual violence. Conclusion Controlling behavior by husband/partner significantly increases the likelihood of physical and sexual IPV, thus acting as a precursor to violence. Findings emphasize the need to adopt a proactive integrated approach to controlling behavior and intimate partner violence within the society. PMID:21714854

Is Primary Prostate Cancer Treatment Influenced by Likelihood of Extraprostatic Disease? A Surveillance, Epidemiology and End Results Patterns of Care Study

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

Holmes, Jordan A.; Wang, Andrew Z.; University of North Carolina-Lineberger Comprehensive Cancer Center, Chapel Hill, NC

2012-09-01

Purpose: To examine the patterns of primary treatment in a recent population-based cohort of prostate cancer patients, stratified by the likelihood of extraprostatic cancer as predicted by disease characteristics available at diagnosis. Methods and Materials: A total of 157,371 patients diagnosed from 2004 to 2008 with clinically localized and potentially curable (node-negative, nonmetastatic) prostate cancer, who have complete information on prostate-specific antigen, Gleason score, and clinical stage, were included. Patients with clinical T1/T2 disease were grouped into categories of <25%, 25%-50%, and >50% likelihood of having extraprostatic disease using the Partin nomogram. Clinical T3/T4 patients were examined separately as themore » highest-risk group. Logistic regression was used to examine the association between patient group and receipt of each primary treatment, adjusting for age, race, year of diagnosis, marital status, Surveillance, Epidemiology and End Results database region, and county-level education. Separate models were constructed for primary surgery, external-beam radiotherapy (RT), and conservative management. Results: On multivariable analysis, increasing likelihood of extraprostatic disease was significantly associated with increasing use of RT and decreased conservative management. Use of surgery also increased. Patients with >50% likelihood of extraprostatic cancer had almost twice the odds of receiving prostatectomy as those with <25% likelihood, and T3-T4 patients had 18% higher odds. Prostatectomy use increased in recent years. Patients aged 76-80 years were likely to be managed conservatively, even those with a >50% likelihood of extraprostatic cancer (34%) and clinical T3-T4 disease (24%). The proportion of patients who received prostatectomy or conservative management was approximately 50% or slightly higher in all groups. Conclusions: There may be underutilization of RT in older prostate cancer patients and those with likely extraprostatic disease. Because more than half of prostate cancer patients do not consult with a radiation oncologist, a multidisciplinary consultation may affect the treatment decision-making process.« less

A general methodology for maximum likelihood inference from band-recovery data

USGS Publications Warehouse

Conroy, M.J.; Williams, B.K.

1984-01-01

A numerical procedure is described for obtaining maximum likelihood estimates and associated maximum likelihood inference from band- recovery data. The method is used to illustrate previously developed one-age-class band-recovery models, and is extended to new models, including the analysis with a covariate for survival rates and variable-time-period recovery models. Extensions to R-age-class band- recovery, mark-recapture models, and twice-yearly marking are discussed. A FORTRAN program provides computations for these models.

Disparities in Access to Outpatient Rehabilitation Therapy for African Americans with Arthritis.

PubMed

Sandstrom, Robert; Bruns, Alexandria

2017-08-01

Approximately, 10 million Americans have an outpatient physical therapy or occupational therapy visit per year. This population is largely Caucasian, insured, educated and middle or high income. The purpose of this study was to determine the existence of racial and/or ethnic disparities in patients with self-reported arthritis accessing office-based therapy services in the USA. A pooled analytic file of 2008-2010 data from the Medical Expenditure Panel Survey-Household Survey was created. We first conducted a descriptive analysis of the utilization of therapy services for persons reporting arthritis. From the descriptive analysis, we formulated experimental hypotheses that we tested to determine if a racial disparity existed to access therapy services between White and Asian persons with arthritis and Black/Hispanic populations. To test our hypotheses, we determined the odd ratios using a logistic regression analysis. We conducted a similar analysis controlling for education, income, and insurance status. Eight percent of the US adult population with self-reported arthritis has an office-based therapy visit each year. Hispanic and Black Americans with arthritis have a reduced odds of a therapy visit (26.5 % [95 % CI 7-42 %] and 44.8 % [95 % CI 31.9-55.3 %], respectively). We did not find a similar effect on odds of a therapy visit for the Asian American population. The effect of race/ethnicity on the odds of a therapy visit was moderated by socioeconomic variables but persists for Black Americans. The results of this study confirm a reduced likelihood of an office-based therapy visit for Black Americans with arthritis when controlled for income, insurance, and education. An effect of race/ethnicity on the likelihood of a therapy visit for Hispanic Americans with arthritis disappears when controlled for income, insurance, and education.

Haplotype-Based Association Analysis via Variance-Components Score Test

PubMed Central

Tzeng, Jung-Ying ; Zhang, Daowen 

2007-01-01

Haplotypes provide a more informative format of polymorphisms for genetic association analysis than do individual single-nucleotide polymorphisms. However, the practical efficacy of haplotype-based association analysis is challenged by a trade-off between the benefits of modeling abundant variation and the cost of the extra degrees of freedom. To reduce the degrees of freedom, several strategies have been considered in the literature. They include (1) clustering evolutionarily close haplotypes, (2) modeling the level of haplotype sharing, and (3) smoothing haplotype effects by introducing a correlation structure for haplotype effects and studying the variance components (VC) for association. Although the first two strategies enjoy a fair extent of power gain, empirical evidence showed that VC methods may exhibit only similar or less power than the standard haplotype regression method, even in cases of many haplotypes. In this study, we report possible reasons that cause the underpowered phenomenon and show how the power of the VC strategy can be improved. We construct a score test based on the restricted maximum likelihood or the marginal likelihood function of the VC and identify its nontypical limiting distribution. Through simulation, we demonstrate the validity of the test and investigate the power performance of the VC approach and that of the standard haplotype regression approach. With suitable choices for the correlation structure, the proposed method can be directly applied to unphased genotypic data. Our method is applicable to a wide-ranging class of models and is computationally efficient and easy to implement. The broad coverage and the fast and easy implementation of this method make the VC strategy an effective tool for haplotype analysis, even in modern genomewide association studies. PMID:17924336

Bayesian model selection: Evidence estimation based on DREAM simulation and bridge sampling

NASA Astrophysics Data System (ADS)

Volpi, Elena; Schoups, Gerrit; Firmani, Giovanni; Vrugt, Jasper A.

2017-04-01

Bayesian inference has found widespread application in Earth and Environmental Systems Modeling, providing an effective tool for prediction, data assimilation, parameter estimation, uncertainty analysis and hypothesis testing. Under multiple competing hypotheses, the Bayesian approach also provides an attractive alternative to traditional information criteria (e.g. AIC, BIC) for model selection. The key variable for Bayesian model selection is the evidence (or marginal likelihood) that is the normalizing constant in the denominator of Bayes theorem; while it is fundamental for model selection, the evidence is not required for Bayesian inference. It is computed for each hypothesis (model) by averaging the likelihood function over the prior parameter distribution, rather than maximizing it as by information criteria; the larger a model evidence the more support it receives among a collection of hypothesis as the simulated values assign relatively high probability density to the observed data. Hence, the evidence naturally acts as an Occam's razor, preferring simpler and more constrained models against the selection of over-fitted ones by information criteria that incorporate only the likelihood maximum. Since it is not particularly easy to estimate the evidence in practice, Bayesian model selection via the marginal likelihood has not yet found mainstream use. We illustrate here the properties of a new estimator of the Bayesian model evidence, which provides robust and unbiased estimates of the marginal likelihood; the method is coined Gaussian Mixture Importance Sampling (GMIS). GMIS uses multidimensional numerical integration of the posterior parameter distribution via bridge sampling (a generalization of importance sampling) of a mixture distribution fitted to samples of the posterior distribution derived from the DREAM algorithm (Vrugt et al., 2008; 2009). Some illustrative examples are presented to show the robustness and superiority of the GMIS estimator with respect to other commonly used approaches in the literature.

Clinical Paresthesia Atlas Illustrates Likelihood of Coverage Based on Spinal Cord Stimulator Electrode Location.

PubMed

Taghva, Alexander; Karst, Edward; Underwood, Paul

2017-08-01

Concordant paresthesia coverage is an independent predictor of pain relief following spinal cord stimulation (SCS). Using aggregate data, our objective is to produce a map of paresthesia coverage as a function of electrode location in SCS. This retrospective analysis used x-rays, SCS programming data, and paresthesia coverage maps from the EMPOWER registry of SCS implants for chronic neuropathic pain. Spinal level of dorsal column stimulation was determined by x-ray adjudication and active cathodes in patient programs. Likelihood of paresthesia coverage was determined as a function of stimulating electrode location. Segments of paresthesia coverage were grouped anatomically. Fisher's exact test was used to identify significant differences in likelihood of paresthesia coverage as a function of spinal stimulation level. In the 178 patients analyzed, the most prevalent areas of paresthesia coverage were buttocks, anterior and posterior thigh (each 98%), and low back (94%). Unwanted paresthesia at the ribs occurred in 8% of patients. There were significant differences in the likelihood of achieving paresthesia, with higher thoracic levels (T5, T6, and T7) more likely to achieve low back coverage but also more likely to introduce paresthesia felt at the ribs. Higher levels in the thoracic spine were associated with greater coverage of the buttocks, back, and thigh, and with lesser coverage of the leg and foot. This paresthesia atlas uses real-world, aggregate data to determine likelihood of paresthesia coverage as a function of stimulating electrode location. It represents an application of "big data" techniques, and a step toward achieving personalized SCS therapy tailored to the individual's chronic pain. © 2017 International Neuromodulation Society.

Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data.

PubMed

Chen, Feng; Chen, Suren; Ma, Xiaoxiang

2018-06-01

Driving environment, including road surface conditions and traffic states, often changes over time and influences crash probability considerably. It becomes stretched for traditional crash frequency models developed in large temporal scales to capture the time-varying characteristics of these factors, which may cause substantial loss of critical driving environmental information on crash prediction. Crash prediction models with refined temporal data (hourly records) are developed to characterize the time-varying nature of these contributing factors. Unbalanced panel data mixed logit models are developed to analyze hourly crash likelihood of highway segments. The refined temporal driving environmental data, including road surface and traffic condition, obtained from the Road Weather Information System (RWIS), are incorporated into the models. Model estimation results indicate that the traffic speed, traffic volume, curvature and chemically wet road surface indicator are better modeled as random parameters. The estimation results of the mixed logit models based on unbalanced panel data show that there are a number of factors related to crash likelihood on I-25. Specifically, weekend indicator, November indicator, low speed limit and long remaining service life of rutting indicator are found to increase crash likelihood, while 5-am indicator and number of merging ramps per lane per mile are found to decrease crash likelihood. The study underscores and confirms the unique and significant impacts on crash imposed by the real-time weather, road surface, and traffic conditions. With the unbalanced panel data structure, the rich information from real-time driving environmental big data can be well incorporated. Copyright © 2018 National Safety Council and Elsevier Ltd. All rights reserved.

Private or public? An empirical analysis of the importance of work values for work sector choice among Norwegian medical specialists.

PubMed

Midttun, Linda

2007-03-01

In the aftermath of the Norwegian hospital reform of 2002, the private supply of specialized healthcare has increased substantially. This article analyses the likelihood of medical specialists working in the private sector. Sector choice is operationalized in two ways: first, as the likelihood of medical specialists working in the private sector at all (at least 1% of the total work hours), and second, as the likelihood of working full-time (90-100%) privately. The theoretical framework is embedded in work values theory and the results suggest that work values are important predictors of sector choice. All analyses are based on a postal questionnaire survey of medical specialists working in private contract practices and for-profit hospitals and a control group of specialists selected from the Norwegian Medical Association's member register. The analyses revealed that while autonomy values impact positively on the propensity for allocating any time at all to the private sector, professional values have a negative effect. Given that the medical specialist already works in the private sector, a high valuation of professional values and payment and benefit values increases the likelihood of having a dual sector job rather than a full-time private position. However, due to the cross-sectional structure of the data and limitations in the dataset, causality questions cannot be fully settled on the basis of the analyses. The relationship between work values and sector choice should, therefore, be regarded as associations rather than causality links. Finally, the likelihood of working in the private sector varies significantly at the municipality level, suggesting that medical specialist's location is important for sector choice.

Maximum Likelihood Analysis in the PEN Experiment

NASA Astrophysics Data System (ADS)

Lehman, Martin

2013-10-01

The experimental determination of the π+ -->e+ ν (γ) decay branching ratio currently provides the most accurate test of lepton universality. The PEN experiment at PSI, Switzerland, aims to improve the present world average experimental precision of 3 . 3 ×10-3 to 5 ×10-4 using a stopped beam approach. During runs in 2008-10, PEN has acquired over 2 ×107 πe 2 events. The experiment includes active beam detectors (degrader, mini TPC, target), central MWPC tracking with plastic scintillator hodoscopes, and a spherical pure CsI electromagnetic shower calorimeter. The final branching ratio will be calculated using a maximum likelihood analysis. This analysis assigns each event a probability for 5 processes (π+ -->e+ ν , π+ -->μ+ ν , decay-in-flight, pile-up, and hadronic events) using Monte Carlo verified probability distribution functions of our observables (energies, times, etc). A progress report on the PEN maximum likelihood analysis will be presented. Work supported by NSF grant PHY-0970013.

Do-it-yourself statistics: A computer-assisted likelihood approach to analysis of data from genetic crosses.

PubMed Central

Robbins, L G

2000-01-01

Graduate school programs in genetics have become so full that courses in statistics have often been eliminated. In addition, typical introductory statistics courses for the "statistics user" rather than the nascent statistician are laden with methods for analysis of measured variables while genetic data are most often discrete numbers. These courses are often seen by students and genetics professors alike as largely irrelevant cookbook courses. The powerful methods of likelihood analysis, although commonly employed in human genetics, are much less often used in other areas of genetics, even though current computational tools make this approach readily accessible. This article introduces the MLIKELY.PAS computer program and the logic of do-it-yourself maximum-likelihood statistics. The program itself, course materials, and expanded discussions of some examples that are only summarized here are available at http://www.unisi. it/ricerca/dip/bio_evol/sitomlikely/mlikely.h tml. PMID:10628965

Prioritizing conservation investments for mammal species globally

PubMed Central

Wilson, Kerrie A.; Evans, Megan C.; Di Marco, Moreno; Green, David C.; Boitani, Luigi; Possingham, Hugh P.; Chiozza, Federica; Rondinini, Carlo

2011-01-01

We need to set priorities for conservation because we cannot do everything, everywhere, at the same time. We determined priority areas for investment in threat abatement actions, in both a cost-effective and spatially and temporally explicit way, for the threatened mammals of the world. Our analysis presents the first fine-resolution prioritization analysis for mammals at a global scale that accounts for the risk of habitat loss, the actions required to abate this risk, the costs of these actions and the likelihood of investment success. We evaluated the likelihood of success of investments using information on the past frequency and duration of legislative effectiveness at a country scale. The establishment of new protected areas was the action receiving the greatest investment, while restoration was never chosen. The resolution of the analysis and the incorporation of likelihood of success made little difference to this result, but affected the spatial location of these investments. PMID:21844046

Transfer Entropy as a Log-Likelihood Ratio

NASA Astrophysics Data System (ADS)

Barnett, Lionel; Bossomaier, Terry

2012-09-01

Transfer entropy, an information-theoretic measure of time-directed information transfer between joint processes, has steadily gained popularity in the analysis of complex stochastic dynamics in diverse fields, including the neurosciences, ecology, climatology, and econometrics. We show that for a broad class of predictive models, the log-likelihood ratio test statistic for the null hypothesis of zero transfer entropy is a consistent estimator for the transfer entropy itself. For finite Markov chains, furthermore, no explicit model is required. In the general case, an asymptotic χ2 distribution is established for the transfer entropy estimator. The result generalizes the equivalence in the Gaussian case of transfer entropy and Granger causality, a statistical notion of causal influence based on prediction via vector autoregression, and establishes a fundamental connection between directed information transfer and causality in the Wiener-Granger sense.

Transfer entropy as a log-likelihood ratio.

PubMed

Barnett, Lionel; Bossomaier, Terry

2012-09-28

Transfer entropy, an information-theoretic measure of time-directed information transfer between joint processes, has steadily gained popularity in the analysis of complex stochastic dynamics in diverse fields, including the neurosciences, ecology, climatology, and econometrics. We show that for a broad class of predictive models, the log-likelihood ratio test statistic for the null hypothesis of zero transfer entropy is a consistent estimator for the transfer entropy itself. For finite Markov chains, furthermore, no explicit model is required. In the general case, an asymptotic χ2 distribution is established for the transfer entropy estimator. The result generalizes the equivalence in the Gaussian case of transfer entropy and Granger causality, a statistical notion of causal influence based on prediction via vector autoregression, and establishes a fundamental connection between directed information transfer and causality in the Wiener-Granger sense.

Sick leave and the impact of job-to-job mobility on the likelihood of remaining on the labour market--a longitudinal Swedish register study.

PubMed

Nordström, Karin; Ekberg, Kerstin; Hemmingsson, Tomas; Johansson, Gun

2014-04-03

Change of job could be a strategy in vocational rehabilitation when return to the original job is not possible, but research is very limited concerning the effects of job mobility on the future vocational situation. The aim of the study was to investigate whether job-to-job mobility affects the likelihood of remaining on the labour market over time among persons who are employed and have experienced long-term sick leave. In a longitudinal register study, cohorts from three base years (1994, 1999 and 2004) were created, based on the Swedish population who were 20-60 years old, had sickness allowance insurance, and were employed in the base year and the following year (n>3,000,000). The likelihood that individuals on long-term sick leave were employed later depending on whether or not they changed workplace during the present or next year of long-term sick leave was analyzed using logistic regression analysis. Age, sector, industry, children, marital status, education, income, rate of sick leave and earlier sick leave and earlier mobility were taken into consideration. Women with more than 180 days' sick leave who changed workplaces were more likely to have a job later compared with those who did not change jobs. For men, the association was statistically significant with 1994 and 2004 as base years, but not in the cohort from 1999. The present study indicates that for those on long-term sick leave that changed workplaces, the opportunities to stay on the labour market might increase. However, the study has methodological limitations and the results for men are ambiguous. We do not therefore have enough evidence for recommending job change as a strategy for vocational rehabilitation.

Self-assessed mental health problems and work capacity as determinants of return to work: a prospective general population-based study of individuals with all-cause sickness absence

PubMed Central

2013-01-01

Background Mental health problems are common in the work force and influence work capacity and sickness absence. The aim was to examine self-assessed mental health problems and work capacity as determinants of time until return to work (RTW). Methods Employed women and men (n=6140), aged 19–64 years, registered as sick with all-cause sickness absence between February 18 and April 15, 2008 received a self-administered questionnaire covering health and work situation (response rate 54%). Demographic data was collected from official registers. This follow-up study included 2502 individuals. Of these, 1082 were currently off sick when answering the questionnaire. Register data on total number of benefit compensated sick-leave days in the end of 2008 were used to determine the time until RTW. Self-reported persistent mental illness, the WHO (Ten) Mental Well-Being Index and self-assessed work capacity in relation to knowledge, mental, collaborative and physical demands at work were used as determinants. Multinomial and binary logistic regression analyses were used to estimate odds ratios with 95% confidence intervals (CI) for the likelihood of RTW. Results The likelihood of RTW (≥105 days) was higher among those with persistent mental illness OR= 2.97 (95% CI, 2.10-4.20) and those with low mental well-being OR= 2.89 (95% CI, 2.31-3.62) after adjusting for gender, age, SES, hours worked and sick leave 2007. An analysis of employees who were off sick when they answered the questionnaire, the likelihood of RTW (≥105 days) was higher among those who reported low capacity to work in relation to knowledge, mental, collaborative and physical demands at work. In a multivariable analysis, the likelihood of RTW (≥105 days) among those with low mental well-being remained significant OR=1.93 (95% CI 1.46-2.55) even after adjustment for all dimensions of capacity to work. Conclusion Self-assessed persistent mental illness, low mental well-being and low work capacity increased the likelihood of prolonged RTW. This study is unique because it is based on new sick-leave spells and is the first to show that low mental well-being was a strong determinant of RTW even after adjustment for work capacity. Our findings support the importance of identifying individuals with low mental well-being as a way to promote RTW. PMID:24124982

An analysis of crash likelihood : age versus driving experience

DOT National Transportation Integrated Search

1995-05-01

The study was designed to determine the crash likelihood of drivers in Michigan as a function of two independent variables: driver age and driving experience. The age variable had eight levels (18, 19, 20, 21, 22, 23, 24, and 25 years old) and the ex...

Is farm-related job title an adequate surrogate for pesticide exposure in occupational cancer epidemiology?

PubMed

MacFarlane, E; Glass, D; Fritschi, L

2009-08-01

Accurate assessment of exposure is a key factor in occupational epidemiology but can be problematic, particularly where exposures of interest may be many decades removed from relevant health outcomes. Studies have traditionally relied on crude surrogates of exposure based on job title only, for instance farm-related job title as a surrogate for pesticide exposure. This analysis was based on data collected in Western Australia in 2000-2001. Using a multivariate regression model, we compared expert-assessed likelihood of pesticide exposure based on detailed, individual-specific questionnaire and job specific module interview information with reported farm-related job titles as a surrogate for pesticide exposure. Most (68.8%) jobs with likely pesticide exposure were farm jobs, but 78.3% of farm jobs were assessed as having no likelihood of pesticide exposure. Likely pesticide exposure was more frequent among jobs on crop farms than on livestock farms. Likely pesticide exposure was also more frequent among jobs commenced in more recent decades and jobs of longer duration. Our results suggest that very little misclassification would have resulted from the inverse assumption that all non-farming jobs are not pesticide exposed since only a very small fraction of non-agricultural jobs were likely to have had pesticide exposure. Classification of all farm jobs as pesticide exposed is likely to substantially over-estimate the number of individuals exposed. Our results also suggest that researchers should pay special attention to farm type, length of service and historical period of employment when assessing the likelihood of pesticide exposure in farming jobs.

Copula Regression Analysis of Simultaneously Recorded Frontal Eye Field and Inferotemporal Spiking Activity during Object-Based Working Memory

PubMed Central

Hu, Meng; Clark, Kelsey L.; Gong, Xiajing; Noudoost, Behrad; Li, Mingyao; Moore, Tirin

2015-01-01

Inferotemporal (IT) neurons are known to exhibit persistent, stimulus-selective activity during the delay period of object-based working memory tasks. Frontal eye field (FEF) neurons show robust, spatially selective delay period activity during memory-guided saccade tasks. We present a copula regression paradigm to examine neural interaction of these two types of signals between areas IT and FEF of the monkey during a working memory task. This paradigm is based on copula models that can account for both marginal distribution over spiking activity of individual neurons within each area and joint distribution over ensemble activity of neurons between areas. Considering the popular GLMs as marginal models, we developed a general and flexible likelihood framework that uses the copula to integrate separate GLMs into a joint regression analysis. Such joint analysis essentially leads to a multivariate analog of the marginal GLM theory and hence efficient model estimation. In addition, we show that Granger causality between spike trains can be readily assessed via the likelihood ratio statistic. The performance of this method is validated by extensive simulations, and compared favorably to the widely used GLMs. When applied to spiking activity of simultaneously recorded FEF and IT neurons during working memory task, we observed significant Granger causality influence from FEF to IT, but not in the opposite direction, suggesting the role of the FEF in the selection and retention of visual information during working memory. The copula model has the potential to provide unique neurophysiological insights about network properties of the brain. PMID:26063909

A close examination of double filtering with fold change and t test in microarray analysis

PubMed Central

2009-01-01

Background Many researchers use the double filtering procedure with fold change and t test to identify differentially expressed genes, in the hope that the double filtering will provide extra confidence in the results. Due to its simplicity, the double filtering procedure has been popular with applied researchers despite the development of more sophisticated methods. Results This paper, for the first time to our knowledge, provides theoretical insight on the drawback of the double filtering procedure. We show that fold change assumes all genes to have a common variance while t statistic assumes gene-specific variances. The two statistics are based on contradicting assumptions. Under the assumption that gene variances arise from a mixture of a common variance and gene-specific variances, we develop the theoretically most powerful likelihood ratio test statistic. We further demonstrate that the posterior inference based on a Bayesian mixture model and the widely used significance analysis of microarrays (SAM) statistic are better approximations to the likelihood ratio test than the double filtering procedure. Conclusion We demonstrate through hypothesis testing theory, simulation studies and real data examples, that well constructed shrinkage testing methods, which can be united under the mixture gene variance assumption, can considerably outperform the double filtering procedure. PMID:19995439

Spermatogenic and Phylo-molecular Characterizations of Isolated Fasciola Spp. From Cattle, North West Iran.

PubMed

Rouhani, Soheila; Raeghi, Saber; Spotin, Adel

2017-01-01

Fascioliasis is economically important to the livestock industry that caused with Fasciola hepatica and Fasciola gigantica. The objective of this study was to identify these two species F. hepatica and F. gigantica by using nuclear and mitochondrial markers (ITS1, ND1 and CO1) and have been employed to analyze intraspecific phylogenetic relations of Fasciola spp. Approximately 150 Fasciola specimens were collected, then stained with haematoxylin-carmine dye and observed under an optical microscope to examine for the existence of sperm. The ITS1 marker was used to identify different Fasciola and phylogenetic analysis based on ND1 and CO1 sequence data were conducted by maximum likelihood algorithm. Fasciola samples were separated into 2 groups. Almost all specimens had many sperms in the seminal vesicle (spermic fluke) and one fluke did not contain any sperm in the seminal vesicle. The aspermic sample had F. gigantica RFLP pattern with ITS1 gene. Phylogenetic analysis based on NDI and COI sequence data were conducted by maximum likelihood showed a similar topology of the trees obtained particularly for F. hepatica and F. gigantica. This study demonstrated that aspermic Fasciola found in this region of Iran has same genetic structures through the spermic F. gigantica populations in accordance to phylogenetic tree.

Diagnostic significance of microRNAs as novel biomarkers for bladder cancer: a meta-analysis of ten articles.

PubMed

Shi, Hong-Bin; Yu, Jia-Xing; Yu, Jian-Xiu; Feng, Zheng; Zhang, Chao; Li, Guang-Yong; Zhao, Rui-Ning; Yang, Xiao-Bo

2017-08-03

Previous studies have revealed the importance of microRNAs' (miRNAs) function as biomarkers in diagnosing human bladder cancer (BC). However, the results are discordant. Consequently, the possibility of miRNAs to be BC biomarkers was summarized in this meta-analysis. In this study, the relevant articles were systematically searched from CBM, PubMed, EMBASE, and Chinese National Knowledge Infrastructure (CNKI). The bivariate model was used to calculate the pooled diagnostic parameters and summary receiver operator characteristic (SROC) curve in this meta-analysis, thereby estimating the whole predictive performance. STATA software was used during the whole analysis. Thirty-one studies from 10 articles, including 1556 cases and 1347 controls, were explored in this meta-analysis. In short, the pooled sensitivity, area under the SROC curve, specificity, positive likelihood ratio, diagnostic odds ratio, and negative likelihood ratio were 0.72 (95%CI 0.66-0.76), 0.80 (0.77-0.84), 0.76 (0.71-0.81), 3.0 (2.4-3.8), 8 (5.0-12.0), and 0.37 (0.30-0.46) respectively. Additionally, sub-group and meta-regression analyses revealed that there were significant differences between ethnicity, miRNA profiling, and specimen sub-groups. These results suggested that Asian population-based studies, multiple-miRNA profiling, and blood-based assays might yield a higher diagnostic accuracy than their counterparts. This meta-analysis demonstrated that miRNAs, particularly multiple miRNAs in the blood, might be novel, useful biomarkers with relatively high sensitivity and specificity and can be used for the diagnosis of BC. However, further prospective studies with more samples should be performed for further validation.

Neural Networks Involved in Adolescent Reward Processing: An Activation Likelihood Estimation Meta-Analysis of Functional Neuroimaging Studies

PubMed Central

Silverman, Merav H.; Jedd, Kelly; Luciana, Monica

2015-01-01

Behavioral responses to, and the neural processing of, rewards change dramatically during adolescence and may contribute to observed increases in risk-taking during this developmental period. Functional MRI (fMRI) studies suggest differences between adolescents and adults in neural activation during reward processing, but findings are contradictory, and effects have been found in non-predicted directions. The current study uses an activation likelihood estimation (ALE) approach for quantitative meta-analysis of functional neuroimaging studies to: 1) confirm the network of brain regions involved in adolescents’ reward processing, 2) identify regions involved in specific stages (anticipation, outcome) and valence (positive, negative) of reward processing, and 3) identify differences in activation likelihood between adolescent and adult reward-related brain activation. Results reveal a subcortical network of brain regions involved in adolescent reward processing similar to that found in adults with major hubs including the ventral and dorsal striatum, insula, and posterior cingulate cortex (PCC). Contrast analyses find that adolescents exhibit greater likelihood of activation in the insula while processing anticipation relative to outcome and greater likelihood of activation in the putamen and amygdala during outcome relative to anticipation. While processing positive compared to negative valence, adolescents show increased likelihood for activation in the posterior cingulate cortex (PCC) and ventral striatum. Contrasting adolescent reward processing with the existing ALE of adult reward processing (Liu et al., 2011) reveals increased likelihood for activation in limbic, frontolimbic, and striatal regions in adolescents compared with adults. Unlike adolescents, adults also activate executive control regions of the frontal and parietal lobes. These findings support hypothesized elevations in motivated activity during adolescence. PMID:26254587

Handwriting individualization using distance and rarity

NASA Astrophysics Data System (ADS)

Tang, Yi; Srihari, Sargur; Srinivasan, Harish

2012-01-01

Forensic individualization is the task of associating observed evidence with a specific source. The likelihood ratio (LR) is a quantitative measure that expresses the degree of uncertainty in individualization, where the numerator represents the likelihood that the evidence corresponds to the known and the denominator the likelihood that it does not correspond to the known. Since the number of parameters needed to compute the LR is exponential with the number of feature measurements, a commonly used simplification is the use of likelihoods based on distance (or similarity) given the two alternative hypotheses. This paper proposes an intermediate method which decomposes the LR as the product of two factors, one based on distance and the other on rarity. It was evaluated using a data set of handwriting samples, by determining whether two writing samples were written by the same/different writer(s). The accuracy of the distance and rarity method, as measured by error rates, is significantly better than the distance method.

Partially incorrect fossil data augment analyses of discrete trait evolution in living species.

PubMed

Puttick, Mark N

2016-08-01

Ancestral state reconstruction of discrete character traits is often vital when attempting to understand the origins and homology of traits in living species. The addition of fossils has been shown to alter our understanding of trait evolution in extant taxa, but researchers may avoid using fossils alongside extant species if only few are known, or if the designation of the trait of interest is uncertain. Here, I investigate the impacts of fossils and incorrectly coded fossils in the ancestral state reconstruction of discrete morphological characters under a likelihood model. Under simulated phylogenies and data, likelihood-based models are generally accurate when estimating ancestral node values. Analyses with combined fossil and extant data always outperform analyses with extant species alone, even when around one quarter of the fossil information is incorrect. These results are especially pronounced when model assumptions are violated, such as when there is a trend away from the root value. Fossil data are of particular importance when attempting to estimate the root node character state. Attempts should be made to include fossils in analysis of discrete traits under likelihood, even if there is uncertainty in the fossil trait data. © 2016 The Authors.

Fitting distributions to microbial contamination data collected with an unequal probability sampling design.

PubMed

Williams, M S; Ebel, E D; Cao, Y

2013-01-01

The fitting of statistical distributions to microbial sampling data is a common application in quantitative microbiology and risk assessment applications. An underlying assumption of most fitting techniques is that data are collected with simple random sampling, which is often times not the case. This study develops a weighted maximum likelihood estimation framework that is appropriate for microbiological samples that are collected with unequal probabilities of selection. A weighted maximum likelihood estimation framework is proposed for microbiological samples that are collected with unequal probabilities of selection. Two examples, based on the collection of food samples during processing, are provided to demonstrate the method and highlight the magnitude of biases in the maximum likelihood estimator when data are inappropriately treated as a simple random sample. Failure to properly weight samples to account for how data are collected can introduce substantial biases into inferences drawn from the data. The proposed methodology will reduce or eliminate an important source of bias in inferences drawn from the analysis of microbial data. This will also make comparisons between studies and the combination of results from different studies more reliable, which is important for risk assessment applications. © 2012 No claim to US Government works.

8D likelihood effective Higgs couplings extraction framework in h → 4ℓ

DOE PAGES

Chen, Yi; Di Marco, Emanuele; Lykken, Joe; ...

2015-01-23

We present an overview of a comprehensive analysis framework aimed at performing direct extraction of all possible effective Higgs couplings to neutral electroweak gauge bosons in the decay to electrons and muons, the so called ‘golden channel’. Our framework is based primarily on a maximum likelihood method constructed from analytic expressions of the fully differential cross sections for h → 4l and for the dominant irreduciblemore » $$ q\\overline{q} $$ → 4l background, where 4l = 2e2μ, 4e, 4μ. Detector effects are included by an explicit convolution of these analytic expressions with the appropriate transfer function over all center of mass variables. Utilizing the full set of observables, we construct an unbinned detector-level likelihood which is continuous in the effective couplings. We consider possible ZZ, Zγ, and γγ couplings simultaneously, allowing for general CP odd/even admixtures. A broad overview is given of how the convolution is performed and we discuss the principles and theoretical basis of the framework. This framework can be used in a variety of ways to study Higgs couplings in the golden channel using data obtained at the LHC and other future colliders.« less

Semiparametric time-to-event modeling in the presence of a latent progression event.

PubMed

Rice, John D; Tsodikov, Alex

2017-06-01

In cancer research, interest frequently centers on factors influencing a latent event that must precede a terminal event. In practice it is often impossible to observe the latent event precisely, making inference about this process difficult. To address this problem, we propose a joint model for the unobserved time to the latent and terminal events, with the two events linked by the baseline hazard. Covariates enter the model parametrically as linear combinations that multiply, respectively, the hazard for the latent event and the hazard for the terminal event conditional on the latent one. We derive the partial likelihood estimators for this problem assuming the latent event is observed, and propose a profile likelihood-based method for estimation when the latent event is unobserved. The baseline hazard in this case is estimated nonparametrically using the EM algorithm, which allows for closed-form Breslow-type estimators at each iteration, bringing improved computational efficiency and stability compared with maximizing the marginal likelihood directly. We present simulation studies to illustrate the finite-sample properties of the method; its use in practice is demonstrated in the analysis of a prostate cancer data set. © 2016, The International Biometric Society.

Thinking versus feeling: differentiating between cognitive and affective components of perceived cancer risk.

PubMed

Janssen, Eva; van Osch, Liesbeth; Lechner, Lilian; Candel, Math; de Vries, Hein

2012-01-01

Despite the increased recognition of affect in guiding probability estimates, perceived risk has been mainly operationalised in a cognitive way and the differentiation between rational and intuitive judgements is largely unexplored. This study investigated the validity of a measurement instrument differentiating cognitive and affective probability beliefs and examined whether behavioural decision making is mainly guided by cognition or affect. Data were obtained from four surveys focusing on smoking (N=268), fruit consumption (N=989), sunbed use (N=251) and sun protection (N=858). Correlational analyses showed that affective likelihood was more strongly correlated with worry compared to cognitive likelihood and confirmatory factor analysis provided support for a two-factor model of perceived likelihood instead of a one-factor model (i.e. cognition and affect combined). Furthermore, affective likelihood was significantly associated with the various outcome variables, whereas the association for cognitive likelihood was absent in three studies. The findings provide support for the construct validity of the measures used to assess cognitive and affective likelihood. Since affective likelihood might be a better predictor of health behaviour than the commonly used cognitive operationalisation, both dimensions should be considered in future research.

On Muthen's Maximum Likelihood for Two-Level Covariance Structure Models

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Hayashi, Kentaro

2005-01-01

Data in social and behavioral sciences are often hierarchically organized. Special statistical procedures that take into account the dependence of such observations have been developed. Among procedures for 2-level covariance structure analysis, Muthen's maximum likelihood (MUML) has the advantage of easier computation and faster convergence. When…

Mixture Rasch Models with Joint Maximum Likelihood Estimation

ERIC Educational Resources Information Center

Willse, John T.

2011-01-01

This research provides a demonstration of the utility of mixture Rasch models. Specifically, a model capable of estimating a mixture partial credit model using joint maximum likelihood is presented. Like the partial credit model, the mixture partial credit model has the beneficial feature of being appropriate for analysis of assessment data…

An EM Algorithm for Maximum Likelihood Estimation of Process Factor Analysis Models

ERIC Educational Resources Information Center

Lee, Taehun

2010-01-01

In this dissertation, an Expectation-Maximization (EM) algorithm is developed and implemented to obtain maximum likelihood estimates of the parameters and the associated standard error estimates characterizing temporal flows for the latent variable time series following stationary vector ARMA processes, as well as the parameters defining the…

Maximum likelihood solution for inclination-only data in paleomagnetism

NASA Astrophysics Data System (ADS)

Arason, P.; Levi, S.

2010-08-01

We have developed a new robust maximum likelihood method for estimating the unbiased mean inclination from inclination-only data. In paleomagnetic analysis, the arithmetic mean of inclination-only data is known to introduce a shallowing bias. Several methods have been introduced to estimate the unbiased mean inclination of inclination-only data together with measures of the dispersion. Some inclination-only methods were designed to maximize the likelihood function of the marginal Fisher distribution. However, the exact analytical form of the maximum likelihood function is fairly complicated, and all the methods require various assumptions and approximations that are often inappropriate. For some steep and dispersed data sets, these methods provide estimates that are significantly displaced from the peak of the likelihood function to systematically shallower inclination. The problem locating the maximum of the likelihood function is partly due to difficulties in accurately evaluating the function for all values of interest, because some elements of the likelihood function increase exponentially as precision parameters increase, leading to numerical instabilities. In this study, we succeeded in analytically cancelling exponential elements from the log-likelihood function, and we are now able to calculate its value anywhere in the parameter space and for any inclination-only data set. Furthermore, we can now calculate the partial derivatives of the log-likelihood function with desired accuracy, and locate the maximum likelihood without the assumptions required by previous methods. To assess the reliability and accuracy of our method, we generated large numbers of random Fisher-distributed data sets, for which we calculated mean inclinations and precision parameters. The comparisons show that our new robust Arason-Levi maximum likelihood method is the most reliable, and the mean inclination estimates are the least biased towards shallow values.

Short Tree, Long Tree, Right Tree, Wrong Tree: New Acquisition Bias Corrections for Inferring SNP Phylogenies

PubMed Central

Leaché, Adam D.; Banbury, Barbara L.; Felsenstein, Joseph; de Oca, Adrián nieto-Montes; Stamatakis, Alexandros

2015-01-01

Single nucleotide polymorphisms (SNPs) are useful markers for phylogenetic studies owing in part to their ubiquity throughout the genome and ease of collection. Restriction site associated DNA sequencing (RADseq) methods are becoming increasingly popular for SNP data collection, but an assessment of the best practises for using these data in phylogenetics is lacking. We use computer simulations, and new double digest RADseq (ddRADseq) data for the lizard family Phrynosomatidae, to investigate the accuracy of RAD loci for phylogenetic inference. We compare the two primary ways RAD loci are used during phylogenetic analysis, including the analysis of full sequences (i.e., SNPs together with invariant sites), or the analysis of SNPs on their own after excluding invariant sites. We find that using full sequences rather than just SNPs is preferable from the perspectives of branch length and topological accuracy, but not of computational time. We introduce two new acquisition bias corrections for dealing with alignments composed exclusively of SNPs, a conditional likelihood method and a reconstituted DNA approach. The conditional likelihood method conditions on the presence of variable characters only (the number of invariant sites that are unsampled but known to exist is not considered), while the reconstituted DNA approach requires the user to specify the exact number of unsampled invariant sites prior to the analysis. Under simulation, branch length biases increase with the amount of missing data for both acquisition bias correction methods, but branch length accuracy is much improved in the reconstituted DNA approach compared to the conditional likelihood approach. Phylogenetic analyses of the empirical data using concatenation or a coalescent-based species tree approach provide strong support for many of the accepted relationships among phrynosomatid lizards, suggesting that RAD loci contain useful phylogenetic signal across a range of divergence times despite the presence of missing data. Phylogenetic analysis of RAD loci requires careful attention to model assumptions, especially if downstream analyses depend on branch lengths. PMID:26227865

Gastrointestinal malignancies: when does race matter?

PubMed

Fitzgerald, Timothy L; Bradley, Cathy J; Dahman, Bassam; Zervos, Emmanuel E

2009-11-01

African Americans have a poorer survival from gastrointestinal cancers. We hypothesized that socioeconomic status may explain much of this disparity. Four years of population-based Medicare and Medicaid administrative claims files were merged with the Michigan Tumor Registry. Data were identified for 18,260 patients with colorectal (n = 13,001), pancreatic (n = 2,427), gastric (n = 1,739), and esophageal (n = 1,093) cancer. Three outcomes were studied: the likelihood of late stage diagnosis, the likelihood of surgery after diagnosis, and survival. Bivariate analysis was used to compare stage and operation between African-American and Caucasian patients. Cox proportional hazard models were used to evaluate differences in survival. Statistical significance was defined as p < 0.05. In unadjusted analyses, relative to Caucasian patients, African-American patients with colorectal and esophageal cancer were more likely to present with metastatic disease, were less likely to have surgery, and were less likely to survive during the study period (p < 0.05). In a multivariate analysis, African-American patients had a higher likelihood of death from colorectal cancer than Caucasian patients. This difference, however, did not persist when late stage and surgery were taken into account (hazard ratio = 1.15, 95% CI = 1.06 to 1.24). No racial differences in survival were observed among patients with esophagus, gastric, or pancreatic cancer. These data suggest that improvements in screening and rates of operation may reduce differences in colorectal cancer outcomes between African-American and Caucasian patients. But race has little influence on survival of patients with pancreatic, esophageal, or gastric cancer.

A Primer on Risks, Issues and Opportunities

DTIC Science & Technology

2016-08-01

likelihood or consequence. A risk has three main parts: a future root cause, a likelihood and a consequence. The future root cause is determined...through root cause analysis, which is the most important part of any risk management effort. SPECIAL SECTION: RISK MANAGEMENT Defense AT&L: July-August...2016 10 Root cause analysis gets to the heart of the risk. Why does the risk exist? What is its nature? How will the risk occur? What should be

Stochastic capture zone analysis of an arsenic-contaminated well using the generalized likelihood uncertainty estimator (GLUE) methodology

NASA Astrophysics Data System (ADS)

Morse, Brad S.; Pohll, Greg; Huntington, Justin; Rodriguez Castillo, Ramiro

2003-06-01

In 1992, Mexican researchers discovered concentrations of arsenic in excess of World Heath Organization (WHO) standards in several municipal wells in the Zimapan Valley of Mexico. This study describes a method to delineate a capture zone for one of the most highly contaminated wells to aid in future well siting. A stochastic approach was used to model the capture zone because of the high level of uncertainty in several input parameters. Two stochastic techniques were performed and compared: "standard" Monte Carlo analysis and the generalized likelihood uncertainty estimator (GLUE) methodology. The GLUE procedure differs from standard Monte Carlo analysis in that it incorporates a goodness of fit (termed a likelihood measure) in evaluating the model. This allows for more information (in this case, head data) to be used in the uncertainty analysis, resulting in smaller prediction uncertainty. Two likelihood measures are tested in this study to determine which are in better agreement with the observed heads. While the standard Monte Carlo approach does not aid in parameter estimation, the GLUE methodology indicates best fit models when hydraulic conductivity is approximately 10-6.5 m/s, with vertically isotropic conditions and large quantities of interbasin flow entering the basin. Probabilistic isochrones (capture zone boundaries) are then presented, and as predicted, the GLUE-derived capture zones are significantly smaller in area than those from the standard Monte Carlo approach.

A Benefit-Risk Analysis Approach to Capture Regulatory Decision-Making: Non-Small Cell Lung Cancer.

PubMed

Raju, G K; Gurumurthi, K; Domike, R; Kazandjian, D; Blumenthal, G; Pazdur, R; Woodcock, J

2016-12-01

Drug regulators around the world make decisions about drug approvability based on qualitative benefit-risk analyses. There is much interest in quantifying regulatory approaches to benefit and risk. In this work the use of a quantitative benefit-risk analysis was applied to regulatory decision-making about new drugs to treat advanced non-small cell lung cancer (NSCLC). Benefits and risks associated with 20 US Food and Drug Administration (FDA) decisions associated with a set of candidate treatments submitted between 2003 and 2015 were analyzed. For benefit analysis, the median overall survival (OS) was used where available. When not available, OS was estimated based on overall response rate (ORR) or progression-free survival (PFS). Risks were analyzed based on magnitude (or severity) of harm and likelihood of occurrence. Additionally, a sensitivity analysis was explored to demonstrate analysis of systematic uncertainty. FDA approval decision outcomes considered were found to be consistent with the benefit-risk logic. © 2016 American Society for Clinical Pharmacology and Therapeutics.

PEPA test: fast and powerful differential analysis from relative quantitative proteomics data using shared peptides.

PubMed

Jacob, Laurent; Combes, Florence; Burger, Thomas

2018-06-18

We propose a new hypothesis test for the differential abundance of proteins in mass-spectrometry based relative quantification. An important feature of this type of high-throughput analyses is that it involves an enzymatic digestion of the sample proteins into peptides prior to identification and quantification. Due to numerous homology sequences, different proteins can lead to peptides with identical amino acid chains, so that their parent protein is ambiguous. These so-called shared peptides make the protein-level statistical analysis a challenge and are often not accounted for. In this article, we use a linear model describing peptide-protein relationships to build a likelihood ratio test of differential abundance for proteins. We show that the likelihood ratio statistic can be computed in linear time with the number of peptides. We also provide the asymptotic null distribution of a regularized version of our statistic. Experiments on both real and simulated datasets show that our procedures outperforms state-of-the-art methods. The procedures are available via the pepa.test function of the DAPAR Bioconductor R package.

Bayesian experimental design for models with intractable likelihoods.

PubMed

Drovandi, Christopher C; Pettitt, Anthony N

2013-12-01

In this paper we present a methodology for designing experiments for efficiently estimating the parameters of models with computationally intractable likelihoods. The approach combines a commonly used methodology for robust experimental design, based on Markov chain Monte Carlo sampling, with approximate Bayesian computation (ABC) to ensure that no likelihood evaluations are required. The utility function considered for precise parameter estimation is based upon the precision of the ABC posterior distribution, which we form efficiently via the ABC rejection algorithm based on pre-computed model simulations. Our focus is on stochastic models and, in particular, we investigate the methodology for Markov process models of epidemics and macroparasite population evolution. The macroparasite example involves a multivariate process and we assess the loss of information from not observing all variables. © 2013, The International Biometric Society.

Applications of non-standard maximum likelihood techniques in energy and resource economics

NASA Astrophysics Data System (ADS)

Moeltner, Klaus

Two important types of non-standard maximum likelihood techniques, Simulated Maximum Likelihood (SML) and Pseudo-Maximum Likelihood (PML), have only recently found consideration in the applied economic literature. The objective of this thesis is to demonstrate how these methods can be successfully employed in the analysis of energy and resource models. Chapter I focuses on SML. It constitutes the first application of this technique in the field of energy economics. The framework is as follows: Surveys on the cost of power outages to commercial and industrial customers usually capture multiple observations on the dependent variable for a given firm. The resulting pooled data set is censored and exhibits cross-sectional heterogeneity. We propose a model that addresses these issues by allowing regression coefficients to vary randomly across respondents and by using the Geweke-Hajivassiliou-Keane simulator and Halton sequences to estimate high-order cumulative distribution terms. This adjustment requires the use of SML in the estimation process. Our framework allows for a more comprehensive analysis of outage costs than existing models, which rely on the assumptions of parameter constancy and cross-sectional homogeneity. Our results strongly reject both of these restrictions. The central topic of the second Chapter is the use of PML, a robust estimation technique, in count data analysis of visitor demand for a system of recreation sites. PML has been popular with researchers in this context, since it guards against many types of mis-specification errors. We demonstrate, however, that estimation results will generally be biased even if derived through PML if the recreation model is based on aggregate, or zonal data. To countervail this problem, we propose a zonal model of recreation that captures some of the underlying heterogeneity of individual visitors by incorporating distributional information on per-capita income into the aggregate demand function. This adjustment eliminates the unrealistic constraint of constant income across zonal residents, and thus reduces the risk of aggregation bias in estimated macro-parameters. The corrected aggregate specification reinstates the applicability of PML. It also increases model efficiency, and allows-for the generation of welfare estimates for population subgroups.

Meta-analysis of studies with bivariate binary outcomes: a marginal beta-binomial model approach

PubMed Central

Chen, Yong; Hong, Chuan; Ning, Yang; Su, Xiao

2018-01-01

When conducting a meta-analysis of studies with bivariate binary outcomes, challenges arise when the within-study correlation and between-study heterogeneity should be taken into account. In this paper, we propose a marginal beta-binomial model for the meta-analysis of studies with binary outcomes. This model is based on the composite likelihood approach, and has several attractive features compared to the existing models such as bivariate generalized linear mixed model (Chu and Cole, 2006) and Sarmanov beta-binomial model (Chen et al., 2012). The advantages of the proposed marginal model include modeling the probabilities in the original scale, not requiring any transformation of probabilities or any link function, having closed-form expression of likelihood function, and no constraints on the correlation parameter. More importantly, since the marginal beta-binomial model is only based on the marginal distributions, it does not suffer from potential misspecification of the joint distribution of bivariate study-specific probabilities. Such misspecification is difficult to detect and can lead to biased inference using currents methods. We compare the performance of the marginal beta-binomial model with the bivariate generalized linear mixed model and the Sarmanov beta-binomial model by simulation studies. Interestingly, the results show that the marginal beta-binomial model performs better than the Sarmanov beta-binomial model, whether or not the true model is Sarmanov beta-binomial, and the marginal beta-binomial model is more robust than the bivariate generalized linear mixed model under model misspecifications. Two meta-analyses of diagnostic accuracy studies and a meta-analysis of case-control studies are conducted for illustration. PMID:26303591

Estimating hazard ratios in cohort data with missing disease information due to death.

PubMed

Binder, Nadine; Herrnböck, Anne-Sophie; Schumacher, Martin

2017-03-01

In clinical and epidemiological studies information on the primary outcome of interest, that is, the disease status, is usually collected at a limited number of follow-up visits. The disease status can often only be retrieved retrospectively in individuals who are alive at follow-up, but will be missing for those who died before. Right-censoring the death cases at the last visit (ad-hoc analysis) yields biased hazard ratio estimates of a potential risk factor, and the bias can be substantial and occur in either direction. In this work, we investigate three different approaches that use the same likelihood contributions derived from an illness-death multistate model in order to more adequately estimate the hazard ratio by including the death cases into the analysis: a parametric approach, a penalized likelihood approach, and an imputation-based approach. We investigate to which extent these approaches allow for an unbiased regression analysis by evaluating their performance in simulation studies and on a real data example. In doing so, we use the full cohort with complete illness-death data as reference and artificially induce missing information due to death by setting discrete follow-up visits. Compared to an ad-hoc analysis, all considered approaches provide less biased or even unbiased results, depending on the situation studied. In the real data example, the parametric approach is seen to be too restrictive, whereas the imputation-based approach could almost reconstruct the original event history information. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Empirical Likelihood-Based Estimation of the Treatment Effect in a Pretest-Posttest Study.

PubMed

Huang, Chiung-Yu; Qin, Jing; Follmann, Dean A

2008-09-01

The pretest-posttest study design is commonly used in medical and social science research to assess the effect of a treatment or an intervention. Recently, interest has been rising in developing inference procedures that improve efficiency while relaxing assumptions used in the pretest-posttest data analysis, especially when the posttest measurement might be missing. In this article we propose a semiparametric estimation procedure based on empirical likelihood (EL) that incorporates the common baseline covariate information to improve efficiency. The proposed method also yields an asymptotically unbiased estimate of the response distribution. Thus functions of the response distribution, such as the median, can be estimated straightforwardly, and the EL method can provide a more appealing estimate of the treatment effect for skewed data. We show that, compared with existing methods, the proposed EL estimator has appealing theoretical properties, especially when the working model for the underlying relationship between the pretest and posttest measurements is misspecified. A series of simulation studies demonstrates that the EL-based estimator outperforms its competitors when the working model is misspecified and the data are missing at random. We illustrate the methods by analyzing data from an AIDS clinical trial (ACTG 175).

Empirical Likelihood-Based Estimation of the Treatment Effect in a Pretest–Posttest Study

PubMed Central

Huang, Chiung-Yu; Qin, Jing; Follmann, Dean A.

2013-01-01

The pretest–posttest study design is commonly used in medical and social science research to assess the effect of a treatment or an intervention. Recently, interest has been rising in developing inference procedures that improve efficiency while relaxing assumptions used in the pretest–posttest data analysis, especially when the posttest measurement might be missing. In this article we propose a semiparametric estimation procedure based on empirical likelihood (EL) that incorporates the common baseline covariate information to improve efficiency. The proposed method also yields an asymptotically unbiased estimate of the response distribution. Thus functions of the response distribution, such as the median, can be estimated straightforwardly, and the EL method can provide a more appealing estimate of the treatment effect for skewed data. We show that, compared with existing methods, the proposed EL estimator has appealing theoretical properties, especially when the working model for the underlying relationship between the pretest and posttest measurements is misspecified. A series of simulation studies demonstrates that the EL-based estimator outperforms its competitors when the working model is misspecified and the data are missing at random. We illustrate the methods by analyzing data from an AIDS clinical trial (ACTG 175). PMID:23729942

Multiscale analysis of restoration priorities for marine shoreline planning.

PubMed

Diefenderfer, Heida L; Sobocinski, Kathryn L; Thom, Ronald M; May, Christopher W; Borde, Amy B; Southard, Susan L; Vavrinec, John; Sather, Nichole K

2009-10-01

Planners are being called on to prioritize marine shorelines for conservation status and restoration action. This study documents an approach to determining the management strategy most likely to succeed based on current conditions at local and landscape scales. The conceptual framework based in restoration ecology pairs appropriate restoration strategies with sites based on the likelihood of producing long-term resilience given the condition of ecosystem structures and processes at three scales: the shorezone unit (site), the drift cell reach (nearshore marine landscape), and the watershed (terrestrial landscape). The analysis is structured by a conceptual ecosystem model that identifies anthropogenic impacts on targeted ecosystem functions. A scoring system, weighted by geomorphic class, is applied to available spatial data for indicators of stress and function using geographic information systems. This planning tool augments other approaches to prioritizing restoration, including historical conditions and change analysis and ecosystem valuation.

The performance of blood pressure-to-height ratio as a screening measure for identifying children and adolescents with hypertension: a meta-analysis.

PubMed

Ma, Chunming; Liu, Yue; Lu, Qiang; Lu, Na; Liu, Xiaoli; Tian, Yiming; Wang, Rui; Yin, Fuzai

2016-02-01

The blood pressure-to-height ratio (BPHR) has been shown to be an accurate index for screening hypertension in children and adolescents. The aim of the present study was to perform a meta-analysis to assess the performance of BPHR for the assessment of hypertension. Electronic and manual searches were performed to identify studies of the BPHR. After methodological quality assessment and data extraction, pooled estimates of the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, area under the receiver operating characteristic curve and summary receiver operating characteristics were assessed systematically. The extent of heterogeneity for it was assessed. Six studies were identified for analysis. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio values of BPHR, for assessment of hypertension, were 96% [95% confidence interval (CI)=0.95-0.97], 90% (95% CI=0.90-0.91), 10.68 (95% CI=8.03-14.21), 0.04 (95% CI=0.03-0.07) and 247.82 (95% CI=114.50-536.34), respectively. The area under the receiver operating characteristic curve was 0.9472. The BPHR had higher diagnostic accuracies for identifying hypertension in children and adolescents.

Maximum Likelihood Analysis of Nonlinear Structural Equation Models with Dichotomous Variables

ERIC Educational Resources Information Center

Song, Xin-Yuan; Lee, Sik-Yum

2005-01-01

In this article, a maximum likelihood approach is developed to analyze structural equation models with dichotomous variables that are common in behavioral, psychological and social research. To assess nonlinear causal effects among the latent variables, the structural equation in the model is defined by a nonlinear function. The basic idea of the…

Contributions to the Underlying Bivariate Normal Method for Factor Analyzing Ordinal Data

ERIC Educational Resources Information Center

Xi, Nuo; Browne, Michael W.

2014-01-01

A promising "underlying bivariate normal" approach was proposed by Jöreskog and Moustaki for use in the factor analysis of ordinal data. This was a limited information approach that involved the maximization of a composite likelihood function. Its advantage over full-information maximum likelihood was that very much less computation was…

Comparison of standard maximum likelihood classification and polytomous logistic regression used in remote sensing

Treesearch

John Hogland; Nedret Billor; Nathaniel Anderson

2013-01-01

Discriminant analysis, referred to as maximum likelihood classification within popular remote sensing software packages, is a common supervised technique used by analysts. Polytomous logistic regression (PLR), also referred to as multinomial logistic regression, is an alternative classification approach that is less restrictive, more flexible, and easy to interpret. To...

Collinear Latent Variables in Multilevel Confirmatory Factor Analysis: A Comparison of Maximum Likelihood and Bayesian Estimations.

PubMed

Can, Seda; van de Schoot, Rens; Hox, Joop

2015-06-01

Because variables may be correlated in the social and behavioral sciences, multicollinearity might be problematic. This study investigates the effect of collinearity manipulated in within and between levels of a two-level confirmatory factor analysis by Monte Carlo simulation. Furthermore, the influence of the size of the intraclass correlation coefficient (ICC) and estimation method; maximum likelihood estimation with robust chi-squares and standard errors and Bayesian estimation, on the convergence rate are investigated. The other variables of interest were rate of inadmissible solutions and the relative parameter and standard error bias on the between level. The results showed that inadmissible solutions were obtained when there was between level collinearity and the estimation method was maximum likelihood. In the within level multicollinearity condition, all of the solutions were admissible but the bias values were higher compared with the between level collinearity condition. Bayesian estimation appeared to be robust in obtaining admissible parameters but the relative bias was higher than for maximum likelihood estimation. Finally, as expected, high ICC produced less biased results compared to medium ICC conditions.

Augmenting Latent Dirichlet Allocation and Rank Threshold Detection with Ontologies

DTIC Science & Technology

2010-03-01

Probabilistic Latent Semantic Indexing (PLSI) is an automated indexing information retrieval model [20]. It is based on a statistical latent class model which is...uses a statistical foundation that is more accurate in finding hidden semantic relationships [20]. The model uses factor analysis of count data, number...principle of statistical infer- ence which asserts that all of the information in a sample is contained in the likelihood function [20]. The statistical

Likelihood testing of seismicity-based rate forecasts of induced earthquakes in Oklahoma and Kansas

USGS Publications Warehouse

Moschetti, Morgan P.; Hoover, Susan M.; Mueller, Charles

2016-01-01

Likelihood testing of induced earthquakes in Oklahoma and Kansas has identified the parameters that optimize the forecasting ability of smoothed seismicity models and quantified the recent temporal stability of the spatial seismicity patterns. Use of the most recent 1-year period of earthquake data and use of 10–20-km smoothing distances produced the greatest likelihood. The likelihood that the locations of January–June 2015 earthquakes were consistent with optimized forecasts decayed with increasing elapsed time between the catalogs used for model development and testing. Likelihood tests with two additional sets of earthquakes from 2014 exhibit a strong sensitivity of the rate of decay to the smoothing distance. Marked reductions in likelihood are caused by the nonstationarity of the induced earthquake locations. Our results indicate a multiple-fold benefit from smoothed seismicity models in developing short-term earthquake rate forecasts for induced earthquakes in Oklahoma and Kansas, relative to the use of seismic source zones.

The role of self-regulatory efficacy, moral disengagement and guilt on doping likelihood: A social cognitive theory perspective.

PubMed

Ring, Christopher; Kavussanu, Maria

2018-03-01

Given the concern over doping in sport, researchers have begun to explore the role played by self-regulatory processes in the decision whether to use banned performance-enhancing substances. Grounded on Bandura's (1991) theory of moral thought and action, this study examined the role of self-regulatory efficacy, moral disengagement and anticipated guilt on the likelihood to use a banned substance among college athletes. Doping self-regulatory efficacy was associated with doping likelihood both directly (b = -.16, P < .001) and indirectly (b = -.29, P < .001) through doping moral disengagement. Moral disengagement also contributed directly to higher doping likelihood and lower anticipated guilt about doping, which was associated with higher doping likelihood. Overall, the present findings provide evidence to support a model of doping based on Bandura's social cognitive theory of moral thought and action, in which self-regulatory efficacy influences the likelihood to use banned performance-enhancing substances both directly and indirectly via moral disengagement.

Risk analysis of chemical, biological, or radionuclear threats: implications for food security.

PubMed

Mohtadi, Hamid; Murshid, Antu Panini

2009-09-01

If the food sector is attacked, the likely agents will be chemical, biological, or radionuclear (CBRN). We compiled a database of international terrorist/criminal activity involving such agents. Based on these data, we calculate the likelihood of a catastrophic event using extreme value methods. At the present, the probability of an event leading to 5,000 casualties (fatalities and injuries) is between 0.1 and 0.3. However, pronounced, nonstationary patterns within our data suggest that the "reoccurrence period" for such attacks is decreasing every year. Similarly, disturbing trends are evident in a broader data set, which is nonspecific as to the methods or means of attack. While at the present the likelihood of CBRN events is quite low, given an attack, the probability that it involves CBRN agents increases with the number of casualties. This is consistent with evidence of "heavy tails" in the distribution of casualties arising from CBRN events.

Genetic mixed linear models for twin survival data.

PubMed

Ha, Il Do; Lee, Youngjo; Pawitan, Yudi

2007-07-01

Twin studies are useful for assessing the relative importance of genetic or heritable component from the environmental component. In this paper we develop a methodology to study the heritability of age-at-onset or lifespan traits, with application to analysis of twin survival data. Due to limited period of observation, the data can be left truncated and right censored (LTRC). Under the LTRC setting we propose a genetic mixed linear model, which allows general fixed predictors and random components to capture genetic and environmental effects. Inferences are based upon the hierarchical-likelihood (h-likelihood), which provides a statistically efficient and unified framework for various mixed-effect models. We also propose a simple and fast computation method for dealing with large data sets. The method is illustrated by the survival data from the Swedish Twin Registry. Finally, a simulation study is carried out to evaluate its performance.

The Interactive Influences of Friend Deviance and Reward Dominance on the Development of Externalizing Behavior During Middle Adolescence

PubMed Central

Bates, John E.; Newman, Joseph P.; Dodge, Kenneth A.; Pettit, Gregory S.

2009-01-01

This study investigated the interactive effects of friend deviance and reward dominance on the development of externalizing behavior of adolescents in the Child Development Project. Reward dominance was assessed at age 16 by performance on a computer-presented card-playing game in which participants had the choice of either continuing or discontinuing the game as the likelihood of reward decreased and the likelihood of punishment increased. At ages 14 and 16, friend deviance and externalizing behavior were assessed through self-report. As expected, based on motivational balance and response modulation theories, path analysis revealed that age 14 friend deviance predicted age 16 externalizing behavior controlling for age 14 externalizing behavior. Reward dominance was a significant moderator of the relationship between friend deviance and externalizing behavior. The contributions of deviant friends to the development of externalizing behavior were enhanced by adolescents' reward dominance. PMID:16823636

The interactive influences of friend deviance and reward dominance on the development of externalizing behavior during middle adolescence.

PubMed

Goodnight, Jackson A; Bates, John E; Newman, Joseph P; Dodge, Kenneth A; Pettit, Gregory S

2006-10-01

This study investigated the interactive effects of friend deviance and reward dominance on the development of externalizing behavior of adolescents in the Child Development Project. Reward dominance was assessed at age 16 by performance on a computer-presented card-playing game in which participants had the choice of either continuing or discontinuing the game as the likelihood of reward decreased and the likelihood of punishment increased. At ages 14 and 16, friend deviance and externalizing behavior were assessed through self-report. As expected, based on motivational balance and response modulation theories, path analysis revealed that age 14 friend deviance predicted age 16 externalizing behavior controlling for age 14 externalizing behavior. Reward dominance was a significant moderator of the relationship between friend deviance and externalizing behavior. The contributions of deviant friends to the development of externalizing behavior were enhanced by adolescents' reward dominance.

Estimating Orion Heat Shield Failure Due To Ablator Cracking During The EFT-1 Mission

NASA Technical Reports Server (NTRS)

Vander Kam, Jeremy C.; Gage, Peter

2016-01-01

The Orion EFT-1 heatshield suffered from two major certification challenges: First, the mechanical properties used in design were not evident in the flight hardware and second, the flight article itself cracked during fabrication. The combination of these events motivated the Orion Program to pursue an engineering-level Probabilistic Risk Assessment (PRA) as part of heatshield certification rationale. The PRA provided loss of Mission (LOM) likelihoods considering the probability of a crack occurring during the mission and the likelihood of subsequent structure over-temperature. The methods and input data for the PRA are presented along with a discussion of the test data used to anchor the results. The Orion program accepted an EFT-1 Loss of Vehicle (LOV) risk of 1-in-160,000 due to in-mission Avcoat cracking based on the results of this analysis. Conservatisms in the result, along with future considerations for Exploration Missions (EM) are also addressed.

Risk factors for classical hysterotomy by gestational age.

PubMed

Osmundson, Sarah S; Garabedian, Matthew J; Lyell, Deirdre J

2013-10-01

To examine the likelihood of classical hysterotomy across preterm gestational ages and to identify factors that increase its occurrence. This is a secondary analysis of a prospective observational cohort collected by the Maternal-Fetal Medicine Network of all women with singleton gestations who underwent a cesarean delivery with a known hysterotomy. Comparisons were made based on gestational age. Factors thought to influence hysterotomy type were studied, including maternal age, body mass index, parity, birth weight, small for gestational age (SGA) status, fetal presentation, labor preceding delivery, and emergent delivery. Approximately 36,000 women were eligible for analysis, of whom 34,454 (95.7%) underwent low transverse hysterotomy and 1,562 (4.3%) underwent classical hysterotomy. The median gestational age of women undergoing a classical hysterotomy was 32 weeks and the incidence peaked between 24 0/7 weeks and 25 6/7 weeks (53.2%), declining with each additional week of gestation thereafter (P for trend <.001). In multivariable regression, the likelihood of classical hysterotomy was increased with SGA (n=258; odds ratio [OR] 2.71; confidence interval [CI] 1.78-4.13), birth weight 1,000 g or less (n=467; OR 1.51; CI 1.03-2.24), and noncephalic presentation (n=783; OR 2.03; CI 1.52-2.72). The likelihood of classical hysterotomy was decreased between 23 0/7 and 27 6/7 weeks of gestation and after 32 weeks of gestation when labor preceded delivery, and increased between 28 0/7 and 31 6/7 weeks of gestation and after 32 weeks of gestation by multiparity and previous cesarean delivery. Emergent delivery did not predict classical hysterotomy. Fifty percent of women at 23-26 weeks of gestation who undergo cesarean delivery have a classical hysterotomy, and the risk declines steadily thereafter. This likelihood is increased by fetal factors, especially SGA and noncephalic presentation. : II.

Quantifying (dis)agreement between direct detection experiments in a halo-independent way

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

Feldstein, Brian; Kahlhoefer, Felix, E-mail: brian.feldstein@physics.ox.ac.uk, E-mail: felix.kahlhoefer@physics.ox.ac.uk

We propose an improved method to study recent and near-future dark matter direct detection experiments with small numbers of observed events. Our method determines in a quantitative and halo-independent way whether the experiments point towards a consistent dark matter signal and identifies the best-fit dark matter parameters. To achieve true halo independence, we apply a recently developed method based on finding the velocity distribution that best describes a given set of data. For a quantitative global analysis we construct a likelihood function suitable for small numbers of events, which allows us to determine the best-fit particle physics properties of darkmore » matter considering all experiments simultaneously. Based on this likelihood function we propose a new test statistic that quantifies how well the proposed model fits the data and how large the tension between different direct detection experiments is. We perform Monte Carlo simulations in order to determine the probability distribution function of this test statistic and to calculate the p-value for both the dark matter hypothesis and the background-only hypothesis.« less

Modeling, estimation and identification methods for static shape determination of flexible structures. [for large space structure design

NASA Technical Reports Server (NTRS)

Rodriguez, G.; Scheid, R. E., Jr.

1986-01-01

This paper outlines methods for modeling, identification and estimation for static determination of flexible structures. The shape estimation schemes are based on structural models specified by (possibly interconnected) elliptic partial differential equations. The identification techniques provide approximate knowledge of parameters in elliptic systems. The techniques are based on the method of maximum-likelihood that finds parameter values such that the likelihood functional associated with the system model is maximized. The estimation methods are obtained by means of a function-space approach that seeks to obtain the conditional mean of the state given the data and a white noise characterization of model errors. The solutions are obtained in a batch-processing mode in which all the data is processed simultaneously. After methods for computing the optimal estimates are developed, an analysis of the second-order statistics of the estimates and of the related estimation error is conducted. In addition to outlining the above theoretical results, the paper presents typical flexible structure simulations illustrating performance of the shape determination methods.

Compatibility of pedigree-based and marker-based relationship matrices for single-step genetic evaluation.

PubMed

Christensen, Ole F

2012-12-03

Single-step methods provide a coherent and conceptually simple approach to incorporate genomic information into genetic evaluations. An issue with single-step methods is compatibility between the marker-based relationship matrix for genotyped animals and the pedigree-based relationship matrix. Therefore, it is necessary to adjust the marker-based relationship matrix to the pedigree-based relationship matrix. Moreover, with data from routine evaluations, this adjustment should in principle be based on both observed marker genotypes and observed phenotypes, but until now this has been overlooked. In this paper, I propose a new method to address this issue by 1) adjusting the pedigree-based relationship matrix to be compatible with the marker-based relationship matrix instead of the reverse and 2) extending the single-step genetic evaluation using a joint likelihood of observed phenotypes and observed marker genotypes. The performance of this method is then evaluated using two simulated datasets. The method derived here is a single-step method in which the marker-based relationship matrix is constructed assuming all allele frequencies equal to 0.5 and the pedigree-based relationship matrix is constructed using the unusual assumption that animals in the base population are related and inbred with a relationship coefficient γ and an inbreeding coefficient γ / 2. Taken together, this γ parameter and a parameter that scales the marker-based relationship matrix can handle the issue of compatibility between marker-based and pedigree-based relationship matrices. The full log-likelihood function used for parameter inference contains two terms. The first term is the REML-log-likelihood for the phenotypes conditional on the observed marker genotypes, whereas the second term is the log-likelihood for the observed marker genotypes. Analyses of the two simulated datasets with this new method showed that 1) the parameters involved in adjusting marker-based and pedigree-based relationship matrices can depend on both observed phenotypes and observed marker genotypes and 2) a strong association between these two parameters exists. Finally, this method performed at least as well as a method based on adjusting the marker-based relationship matrix. Using the full log-likelihood and adjusting the pedigree-based relationship matrix to be compatible with the marker-based relationship matrix provides a new and interesting approach to handle the issue of compatibility between the two matrices in single-step genetic evaluation.

Quantifying the Establishment Likelihood of Invasive Alien Species Introductions Through Ports with Application to Honeybees in Australia.

PubMed

Heersink, Daniel K; Caley, Peter; Paini, Dean R; Barry, Simon C

2016-05-01

The cost of an uncontrolled incursion of invasive alien species (IAS) arising from undetected entry through ports can be substantial, and knowledge of port-specific risks is needed to help allocate limited surveillance resources. Quantifying the establishment likelihood of such an incursion requires quantifying the ability of a species to enter, establish, and spread. Estimation of the approach rate of IAS into ports provides a measure of likelihood of entry. Data on the approach rate of IAS are typically sparse, and the combinations of risk factors relating to country of origin and port of arrival diverse. This presents challenges to making formal statistical inference on establishment likelihood. Here we demonstrate how these challenges can be overcome with judicious use of mixed-effects models when estimating the incursion likelihood into Australia of the European (Apis mellifera) and Asian (A. cerana) honeybees, along with the invasive parasites of biosecurity concern they host (e.g., Varroa destructor). Our results demonstrate how skewed the establishment likelihood is, with one-tenth of the ports accounting for 80% or more of the likelihood for both species. These results have been utilized by biosecurity agencies in the allocation of resources to the surveillance of maritime ports. © 2015 Society for Risk Analysis.

Analysis of biomolecular solvation sites by 3D-RISM theory.

PubMed

Sindhikara, Daniel J; Hirata, Fumio

2013-06-06

We derive, implement, and apply equilibrium solvation site analysis for biomolecules. Our method utilizes 3D-RISM calculations to quickly obtain equilibrium solvent distributions without either necessity of simulation or limits of solvent sampling. Our analysis of these distributions extracts highest likelihood poses of solvent as well as localized entropies, enthalpies, and solvation free energies. We demonstrate our method on a structure of HIV-1 protease where excellent structural and thermodynamic data are available for comparison. Our results, obtained within minutes, show systematic agreement with available experimental data. Further, our results are in good agreement with established simulation-based solvent analysis methods. This method can be used not only for visual analysis of active site solvation but also for virtual screening methods and experimental refinement.

Patch-based image reconstruction for PET using prior-image derived dictionaries

NASA Astrophysics Data System (ADS)

Tahaei, Marzieh S.; Reader, Andrew J.

2016-09-01

In PET image reconstruction, regularization is often needed to reduce the noise in the resulting images. Patch-based image processing techniques have recently been successfully used for regularization in medical image reconstruction through a penalized likelihood framework. Re-parameterization within reconstruction is another powerful regularization technique in which the object in the scanner is re-parameterized using coefficients for spatially-extensive basis vectors. In this work, a method for extracting patch-based basis vectors from the subject’s MR image is proposed. The coefficients for these basis vectors are then estimated using the conventional MLEM algorithm. Furthermore, using the alternating direction method of multipliers, an algorithm for optimizing the Poisson log-likelihood while imposing sparsity on the parameters is also proposed. This novel method is then utilized to find sparse coefficients for the patch-based basis vectors extracted from the MR image. The results indicate the superiority of the proposed methods to patch-based regularization using the penalized likelihood framework.

Comparative shotgun proteomics using spectral count data and quasi-likelihood modeling.

PubMed

Li, Ming; Gray, William; Zhang, Haixia; Chung, Christine H; Billheimer, Dean; Yarbrough, Wendell G; Liebler, Daniel C; Shyr, Yu; Slebos, Robbert J C

2010-08-06

Shotgun proteomics provides the most powerful analytical platform for global inventory of complex proteomes using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and allows a global analysis of protein changes. Nevertheless, sampling of complex proteomes by current shotgun proteomics platforms is incomplete, and this contributes to variability in assessment of peptide and protein inventories by spectral counting approaches. Thus, shotgun proteomics data pose challenges in comparing proteomes from different biological states. We developed an analysis strategy using quasi-likelihood Generalized Linear Modeling (GLM), included in a graphical interface software package (QuasiTel) that reads standard output from protein assemblies created by IDPicker, an HTML-based user interface to query shotgun proteomic data sets. This approach was compared to four other statistical analysis strategies: Student t test, Wilcoxon rank test, Fisher's Exact test, and Poisson-based GLM. We analyzed the performance of these tests to identify differences in protein levels based on spectral counts in a shotgun data set in which equimolar amounts of 48 human proteins were spiked at different levels into whole yeast lysates. Both GLM approaches and the Fisher Exact test performed adequately, each with their unique limitations. We subsequently compared the proteomes of normal tonsil epithelium and HNSCC using this approach and identified 86 proteins with differential spectral counts between normal tonsil epithelium and HNSCC. We selected 18 proteins from this comparison for verification of protein levels between the individual normal and tumor tissues using liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM-MS). This analysis confirmed the magnitude and direction of the protein expression differences in all 6 proteins for which reliable data could be obtained. Our analysis demonstrates that shotgun proteomic data sets from different tissue phenotypes are sufficiently rich in quantitative information and that statistically significant differences in proteins spectral counts reflect the underlying biology of the samples.

Comparative Shotgun Proteomics Using Spectral Count Data and Quasi-Likelihood Modeling

PubMed Central

2010-01-01

Shotgun proteomics provides the most powerful analytical platform for global inventory of complex proteomes using liquid chromatography−tandem mass spectrometry (LC−MS/MS) and allows a global analysis of protein changes. Nevertheless, sampling of complex proteomes by current shotgun proteomics platforms is incomplete, and this contributes to variability in assessment of peptide and protein inventories by spectral counting approaches. Thus, shotgun proteomics data pose challenges in comparing proteomes from different biological states. We developed an analysis strategy using quasi-likelihood Generalized Linear Modeling (GLM), included in a graphical interface software package (QuasiTel) that reads standard output from protein assemblies created by IDPicker, an HTML-based user interface to query shotgun proteomic data sets. This approach was compared to four other statistical analysis strategies: Student t test, Wilcoxon rank test, Fisher’s Exact test, and Poisson-based GLM. We analyzed the performance of these tests to identify differences in protein levels based on spectral counts in a shotgun data set in which equimolar amounts of 48 human proteins were spiked at different levels into whole yeast lysates. Both GLM approaches and the Fisher Exact test performed adequately, each with their unique limitations. We subsequently compared the proteomes of normal tonsil epithelium and HNSCC using this approach and identified 86 proteins with differential spectral counts between normal tonsil epithelium and HNSCC. We selected 18 proteins from this comparison for verification of protein levels between the individual normal and tumor tissues using liquid chromatography−multiple reaction monitoring mass spectrometry (LC−MRM-MS). This analysis confirmed the magnitude and direction of the protein expression differences in all 6 proteins for which reliable data could be obtained. Our analysis demonstrates that shotgun proteomic data sets from different tissue phenotypes are sufficiently rich in quantitative information and that statistically significant differences in proteins spectral counts reflect the underlying biology of the samples. PMID:20586475

SCI Identification (SCIDNT) program user's guide. [maximum likelihood method for linear rotorcraft models

NASA Technical Reports Server (NTRS)

1979-01-01

The computer program Linear SCIDNT which evaluates rotorcraft stability and control coefficients from flight or wind tunnel test data is described. It implements the maximum likelihood method to maximize the likelihood function of the parameters based on measured input/output time histories. Linear SCIDNT may be applied to systems modeled by linear constant-coefficient differential equations. This restriction in scope allows the application of several analytical results which simplify the computation and improve its efficiency over the general nonlinear case.

On the complex quantification of risk: systems-based perspective on terrorism.

PubMed

Haimes, Yacov Y

2011-08-01

This article highlights the complexity of the quantification of the multidimensional risk function, develops five systems-based premises on quantifying the risk of terrorism to a threatened system, and advocates the quantification of vulnerability and resilience through the states of the system. The five premises are: (i) There exists interdependence between a specific threat to a system by terrorist networks and the states of the targeted system, as represented through the system's vulnerability, resilience, and criticality-impact. (ii) A specific threat, its probability, its timing, the states of the targeted system, and the probability of consequences can be interdependent. (iii) The two questions in the risk assessment process: "What is the likelihood?" and "What are the consequences?" can be interdependent. (iv) Risk management policy options can reduce both the likelihood of a threat to a targeted system and the associated likelihood of consequences by changing the states (including both vulnerability and resilience) of the system. (v) The quantification of risk to a vulnerable system from a specific threat must be built on a systemic and repeatable modeling process, by recognizing that the states of the system constitute an essential step to construct quantitative metrics of the consequences based on intelligence gathering, expert evidence, and other qualitative information. The fact that the states of all systems are functions of time (among other variables) makes the time frame pivotal in each component of the process of risk assessment, management, and communication. Thus, risk to a system, caused by an initiating event (e.g., a threat) is a multidimensional function of the specific threat, its probability and time frame, the states of the system (representing vulnerability and resilience), and the probabilistic multidimensional consequences. © 2011 Society for Risk Analysis.

Modeling and E-M estimation of haplotype-specific relative risks from genotype data for a case-control study of unrelated individuals.

PubMed

Stram, Daniel O; Leigh Pearce, Celeste; Bretsky, Phillip; Freedman, Matthew; Hirschhorn, Joel N; Altshuler, David; Kolonel, Laurence N; Henderson, Brian E; Thomas, Duncan C

2003-01-01

The US National Cancer Institute has recently sponsored the formation of a Cohort Consortium (http://2002.cancer.gov/scpgenes.htm) to facilitate the pooling of data on very large numbers of people, concerning the effects of genes and environment on cancer incidence. One likely goal of these efforts will be generate a large population-based case-control series for which a number of candidate genes will be investigated using SNP haplotype as well as genotype analysis. The goal of this paper is to outline the issues involved in choosing a method of estimating haplotype-specific risk estimates for such data that is technically appropriate and yet attractive to epidemiologists who are already comfortable with odds ratios and logistic regression. Our interest is to develop and evaluate extensions of methods, based on haplotype imputation, that have been recently described (Schaid et al., Am J Hum Genet, 2002, and Zaykin et al., Hum Hered, 2002) as providing score tests of the null hypothesis of no effect of SNP haplotypes upon risk, which may be used for more complex tasks, such as providing confidence intervals, and tests of equivalence of haplotype-specific risks in two or more separate populations. In order to do so we (1) develop a cohort approach towards odds ratio analysis by expanding the E-M algorithm to provide maximum likelihood estimates of haplotype-specific odds ratios as well as genotype frequencies; (2) show how to correct the cohort approach, to give essentially unbiased estimates for population-based or nested case-control studies by incorporating the probability of selection as a case or control into the likelihood, based on a simplified model of case and control selection, and (3) finally, in an example data set (CYP17 and breast cancer, from the Multiethnic Cohort Study) we compare likelihood-based confidence interval estimates from the two methods with each other, and with the use of the single-imputation approach of Zaykin et al. applied under both null and alternative hypotheses. We conclude that so long as haplotypes are well predicted by SNP genotypes (we use the Rh2 criteria of Stram et al. [1]) the differences between the three methods are very small and in particular that the single imputation method may be expected to work extremely well. Copyright 2003 S. Karger AG, Basel

Investigation of reliability indicators of information analysis systems based on Markov’s absorbing chain model

NASA Astrophysics Data System (ADS)

Gilmanshin, I. R.; Kirpichnikov, A. P.

2017-09-01

In the result of study of the algorithm of the functioning of the early detection module of excessive losses, it is proven the ability to model it by using absorbing Markov chains. The particular interest is in the study of probability characteristics of early detection module functioning algorithm of losses in order to identify the relationship of indicators of reliability of individual elements, or the probability of occurrence of certain events and the likelihood of transmission of reliable information. The identified relations during the analysis allow to set thresholds reliability characteristics of the system components.

Characterization of the complete mitochondrial genome of the hybrid Epinephelus moara♀ × Epinephelus lanceolatus♂, and phylogenetic analysis in subfamily epinephelinae

NASA Astrophysics Data System (ADS)

Gao, Fengtao; Wei, Min; Zhu, Ying; Guo, Hua; Chen, Songlin; Yang, Guanpin

2017-06-01

This study presents the complete mitochondrial genome of the hybrid Epinephelus moara♀× Epinephelus lanceolatus♂. The genome is 16886 bp in length, and contains 13 protein-coding genes, 2 rRNA genes, 22 tRNA genes, a light-strand replication origin and a control region. Additionally, phylogenetic analysis based on the nucleotide sequences of 13 conserved protein-coding genes using the maximum likelihood method indicated that the mitochondrial genome is maternally inherited. This study presents genomic data for studying phylogenetic relationships and breeding of hybrid Epinephelinae.

Simultaneous Control of Error Rates in fMRI Data Analysis

PubMed Central

Kang, Hakmook; Blume, Jeffrey; Ombao, Hernando; Badre, David

2015-01-01

The key idea of statistical hypothesis testing is to fix, and thereby control, the Type I error (false positive) rate across samples of any size. Multiple comparisons inflate the global (family-wise) Type I error rate and the traditional solution to maintaining control of the error rate is to increase the local (comparison-wise) Type II error (false negative) rates. However, in the analysis of human brain imaging data, the number of comparisons is so large that this solution breaks down: the local Type II error rate ends up being so large that scientifically meaningful analysis is precluded. Here we propose a novel solution to this problem: allow the Type I error rate to converge to zero along with the Type II error rate. It works because when the Type I error rate per comparison is very small, the accumulation (or global) Type I error rate is also small. This solution is achieved by employing the Likelihood paradigm, which uses likelihood ratios to measure the strength of evidence on a voxel-by-voxel basis. In this paper, we provide theoretical and empirical justification for a likelihood approach to the analysis of human brain imaging data. In addition, we present extensive simulations that show the likelihood approach is viable, leading to ‘cleaner’ looking brain maps and operationally superiority (lower average error rate). Finally, we include a case study on cognitive control related activation in the prefrontal cortex of the human brain. PMID:26272730

Array magnetics modal analysis for the DIII-D tokamak based on localized time-series modelling

DOE PAGES

Olofsson, K. Erik J.; Hanson, Jeremy M.; Shiraki, Daisuke; ...

2014-07-14

Here, time-series analysis of magnetics data in tokamaks is typically done using block-based fast Fourier transform methods. This work presents the development and deployment of a new set of algorithms for magnetic probe array analysis. The method is based on an estimation technique known as stochastic subspace identification (SSI). Compared with the standard coherence approach or the direct singular value decomposition approach, the new technique exhibits several beneficial properties. For example, the SSI method does not require that frequencies are orthogonal with respect to the timeframe used in the analysis. Frequencies are obtained directly as parameters of localized time-series models.more » The parameters are extracted by solving small-scale eigenvalue problems. Applications include maximum-likelihood regularized eigenmode pattern estimation, detection of neoclassical tearing modes, including locked mode precursors, and automatic clustering of modes, and magnetics-pattern characterization of sawtooth pre- and postcursors, edge harmonic oscillations and fishbones.« less

On the Performance of Maximum Likelihood versus Means and Variance Adjusted Weighted Least Squares Estimation in CFA

ERIC Educational Resources Information Center

Beauducel, Andre; Herzberg, Philipp Yorck

2006-01-01

This simulation study compared maximum likelihood (ML) estimation with weighted least squares means and variance adjusted (WLSMV) estimation. The study was based on confirmatory factor analyses with 1, 2, 4, and 8 factors, based on 250, 500, 750, and 1,000 cases, and on 5, 10, 20, and 40 variables with 2, 3, 4, 5, and 6 categories. There was no…

The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction

PubMed Central

Williamson, Ross S.; Sahani, Maneesh; Pillow, Jonathan W.

2015-01-01

Stimulus dimensionality-reduction methods in neuroscience seek to identify a low-dimensional space of stimulus features that affect a neuron’s probability of spiking. One popular method, known as maximally informative dimensions (MID), uses an information-theoretic quantity known as “single-spike information” to identify this space. Here we examine MID from a model-based perspective. We show that MID is a maximum-likelihood estimator for the parameters of a linear-nonlinear-Poisson (LNP) model, and that the empirical single-spike information corresponds to the normalized log-likelihood under a Poisson model. This equivalence implies that MID does not necessarily find maximally informative stimulus dimensions when spiking is not well described as Poisson. We provide several examples to illustrate this shortcoming, and derive a lower bound on the information lost when spiking is Bernoulli in discrete time bins. To overcome this limitation, we introduce model-based dimensionality reduction methods for neurons with non-Poisson firing statistics, and show that they can be framed equivalently in likelihood-based or information-theoretic terms. Finally, we show how to overcome practical limitations on the number of stimulus dimensions that MID can estimate by constraining the form of the non-parametric nonlinearity in an LNP model. We illustrate these methods with simulations and data from primate visual cortex. PMID:25831448

An object-oriented approach to risk and reliability analysis : methodology and aviation safety applications.

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

Dandini, Vincent John; Duran, Felicia Angelica; Wyss, Gregory Dane

2003-09-01

This article describes how features of event tree analysis and Monte Carlo-based discrete event simulation can be combined with concepts from object-oriented analysis to develop a new risk assessment methodology, with some of the best features of each. The resultant object-based event scenario tree (OBEST) methodology enables an analyst to rapidly construct realistic models for scenarios for which an a priori discovery of event ordering is either cumbersome or impossible. Each scenario produced by OBEST is automatically associated with a likelihood estimate because probabilistic branching is integral to the object model definition. The OBEST methodology is then applied to anmore » aviation safety problem that considers mechanisms by which an aircraft might become involved in a runway incursion incident. The resulting OBEST model demonstrates how a close link between human reliability analysis and probabilistic risk assessment methods can provide important insights into aviation safety phenomenology.« less

Age-structured mark-recapture analysis: A virtual-population-analysis-based model for analyzing age-structured capture-recapture data

USGS Publications Warehouse

Coggins, L.G.; Pine, William E.; Walters, C.J.; Martell, S.J.D.

2006-01-01

We present a new model to estimate capture probabilities, survival, abundance, and recruitment using traditional Jolly-Seber capture-recapture methods within a standard fisheries virtual population analysis framework. This approach compares the numbers of marked and unmarked fish at age captured in each year of sampling with predictions based on estimated vulnerabilities and abundance in a likelihood function. Recruitment to the earliest age at which fish can be tagged is estimated by using a virtual population analysis method to back-calculate the expected numbers of unmarked fish at risk of capture. By using information from both marked and unmarked animals in a standard fisheries age structure framework, this approach is well suited to the sparse data situations common in long-term capture-recapture programs with variable sampling effort. ?? Copyright by the American Fisheries Society 2006.

Maximum likelihood estimation for Cox's regression model under nested case-control sampling.

PubMed

Scheike, Thomas H; Juul, Anders

2004-04-01

Nested case-control sampling is designed to reduce the costs of large cohort studies. It is important to estimate the parameters of interest as efficiently as possible. We present a new maximum likelihood estimator (MLE) for nested case-control sampling in the context of Cox's proportional hazards model. The MLE is computed by the EM-algorithm, which is easy to implement in the proportional hazards setting. Standard errors are estimated by a numerical profile likelihood approach based on EM aided differentiation. The work was motivated by a nested case-control study that hypothesized that insulin-like growth factor I was associated with ischemic heart disease. The study was based on a population of 3784 Danes and 231 cases of ischemic heart disease where controls were matched on age and gender. We illustrate the use of the MLE for these data and show how the maximum likelihood framework can be used to obtain information additional to the relative risk estimates of covariates.

Likelihood ratios for glaucoma diagnosis using spectral-domain optical coherence tomography.

PubMed

Lisboa, Renato; Mansouri, Kaweh; Zangwill, Linda M; Weinreb, Robert N; Medeiros, Felipe A

2013-11-01

To present a methodology for calculating likelihood ratios for glaucoma diagnosis for continuous retinal nerve fiber layer (RNFL) thickness measurements from spectral-domain optical coherence tomography (spectral-domain OCT). Observational cohort study. A total of 262 eyes of 187 patients with glaucoma and 190 eyes of 100 control subjects were included in the study. Subjects were recruited from the Diagnostic Innovations Glaucoma Study. Eyes with preperimetric and perimetric glaucomatous damage were included in the glaucoma group. The control group was composed of healthy eyes with normal visual fields from subjects recruited from the general population. All eyes underwent RNFL imaging with Spectralis spectral-domain OCT. Likelihood ratios for glaucoma diagnosis were estimated for specific global RNFL thickness measurements using a methodology based on estimating the tangents to the receiver operating characteristic (ROC) curve. Likelihood ratios could be determined for continuous values of average RNFL thickness. Average RNFL thickness values lower than 86 μm were associated with positive likelihood ratios (ie, likelihood ratios greater than 1), whereas RNFL thickness values higher than 86 μm were associated with negative likelihood ratios (ie, likelihood ratios smaller than 1). A modified Fagan nomogram was provided to assist calculation of posttest probability of disease from the calculated likelihood ratios and pretest probability of disease. The methodology allowed calculation of likelihood ratios for specific RNFL thickness values. By avoiding arbitrary categorization of test results, it potentially allows for an improved integration of test results into diagnostic clinical decision making. Copyright © 2013. Published by Elsevier Inc.

Quantitative analysis of low-density SNP data for parentage assignment and estimation of family contributions to pooled samples.

PubMed

Henshall, John M; Dierens, Leanne; Sellars, Melony J

2014-09-02

While much attention has focused on the development of high-density single nucleotide polymorphism (SNP) assays, the costs of developing and running low-density assays have fallen dramatically. This makes it feasible to develop and apply SNP assays for agricultural species beyond the major livestock species. Although low-cost low-density assays may not have the accuracy of the high-density assays widely used in human and livestock species, we show that when combined with statistical analysis approaches that use quantitative instead of discrete genotypes, their utility may be improved. The data used in this study are from a 63-SNP marker Sequenom® iPLEX Platinum panel for the Black Tiger shrimp, for which high-density SNP assays are not currently available. For quantitative genotypes that could be estimated, in 5% of cases the most likely genotype for an individual at a SNP had a probability of less than 0.99. Matrix formulations of maximum likelihood equations for parentage assignment were developed for the quantitative genotypes and also for discrete genotypes perturbed by an assumed error term. Assignment rates that were based on maximum likelihood with quantitative genotypes were similar to those based on maximum likelihood with perturbed genotypes but, for more than 50% of cases, the two methods resulted in individuals being assigned to different families. Treating genotypes as quantitative values allows the same analysis framework to be used for pooled samples of DNA from multiple individuals. Resulting correlations between allele frequency estimates from pooled DNA and individual samples were consistently greater than 0.90, and as high as 0.97 for some pools. Estimates of family contributions to the pools based on quantitative genotypes in pooled DNA had a correlation of 0.85 with estimates of contributions from DNA-derived pedigree. Even with low numbers of SNPs of variable quality, parentage testing and family assignment from pooled samples are sufficiently accurate to provide useful information for a breeding program. Treating genotypes as quantitative values is an alternative to perturbing genotypes using an assumed error distribution, but can produce very different results. An understanding of the distribution of the error is required for SNP genotyping platforms.

Diagnostic value of 18F-FDG-PET/CT for the evaluation of solitary pulmonary nodules: a systematic review and meta-analysis.

PubMed

Ruilong, Zong; Daohai, Xie; Li, Geng; Xiaohong, Wang; Chunjie, Wang; Lei, Tian

2017-01-01

To carry out a meta-analysis on the performance of fluorine-18-fluorodeoxyglucose (F-FDG) PET/computed tomography (PET/CT) for the evaluation of solitary pulmonary nodules. In the meta-analysis, we performed searches of several electronic databases for relevant studies, including Google Scholar, PubMed, Cochrane Library, and several Chinese databases. The quality of all included studies was assessed by Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Two observers independently extracted data of eligible articles. For the meta-analysis, the total sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratios were pooled. A summary receiver operating characteristic curve was constructed. The I-test was performed to assess the impact of study heterogeneity on the results of the meta-analysis. Meta-regression and subgroup analysis were carried out to investigate the potential covariates that might have considerable impacts on heterogeneity. Overall, 12 studies were included in this meta-analysis, including a total of 1297 patients and 1301 pulmonary nodules. The pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio with corresponding 95% confidence intervals (CIs) were 0.82 (95% CI, 0.76-0.87), 0.81 (95% CI, 0.66-0.90), 4.3 (95% CI, 2.3-7.9), and 0.22 (95% CI, 0.16-0.30), respectively. Significant heterogeneity was observed in sensitivity (I=81.1%) and specificity (I=89.6%). Subgroup analysis showed that the best results for sensitivity (0.90; 95% CI, 0.68-0.86) and accuracy (0.93; 95% CI, 0.90-0.95) were present in a prospective study. The results of our analysis suggest that PET/CT is a useful tool for detecting malignant pulmonary nodules qualitatively. Although current evidence showed moderate accuracy for PET/CT in differentiating malignant from benign solitary pulmonary nodules, further work needs to be carried out to improve its reliability.

Maximum Likelihood Analysis of a Two-Level Nonlinear Structural Equation Model with Fixed Covariates

ERIC Educational Resources Information Center

Lee, Sik-Yum; Song, Xin-Yuan

2005-01-01

In this article, a maximum likelihood (ML) approach for analyzing a rather general two-level structural equation model is developed for hierarchically structured data that are very common in educational and/or behavioral research. The proposed two-level model can accommodate nonlinear causal relations among latent variables as well as effects…

Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes

ERIC Educational Resources Information Center

Leite, Walter L.; Stapleton, Laura M.

2011-01-01

In this study, the authors compared the likelihood ratio test and fit indexes for detection of misspecifications of growth shape in latent growth models through a simulation study and a graphical analysis. They found that the likelihood ratio test, MFI, and root mean square error of approximation performed best for detecting model misspecification…

Constrained Maximum Likelihood Estimation for Two-Level Mean and Covariance Structure Models

ERIC Educational Resources Information Center

Bentler, Peter M.; Liang, Jiajuan; Tang, Man-Lai; Yuan, Ke-Hai

2011-01-01

Maximum likelihood is commonly used for the estimation of model parameters in the analysis of two-level structural equation models. Constraints on model parameters could be encountered in some situations such as equal factor loadings for different factors. Linear constraints are the most common ones and they are relatively easy to handle in…

Computing Maximum Likelihood Estimates of Loglinear Models from Marginal Sums with Special Attention to Loglinear Item Response Theory.

ERIC Educational Resources Information Center

Kelderman, Henk

1992-01-01

Describes algorithms used in the computer program LOGIMO for obtaining maximum likelihood estimates of the parameters in loglinear models. These algorithms are also useful for the analysis of loglinear item-response theory models. Presents modified versions of the iterative proportional fitting and Newton-Raphson algorithms. Simulated data…

Likelihood of Suicidality at Varying Levels of Depression Severity: A Re-Analysis of NESARC Data

ERIC Educational Resources Information Center

Uebelacker, Lisa A.; Strong, David; Weinstock, Lauren M.; Miller, Ivan W.

2010-01-01

Although it is clear that increasing depression severity is associated with more risk for suicidality, less is known about at what levels of depression severity the risk for different suicide symptoms increases. We used item response theory to estimate the likelihood of endorsing suicide symptoms across levels of depression severity in an…

Is Immigrant Status Relevant in School Violence Research? An Analysis with Latino Students

ERIC Educational Resources Information Center

Peguero, Anthony A.

2008-01-01

Background: The role of race and ethnicity is consistently found to be linked to the likelihood of students experiencing school violence-related outcomes; however, the findings are not always consistent. The variation of likelihood, as well as the type, of student-related school violence outcome among the Latino student population may be…

Joint reconstruction of activity and attenuation in Time-of-Flight PET: A Quantitative Analysis.

PubMed

Rezaei, Ahmadreza; Deroose, Christophe M; Vahle, Thomas; Boada, Fernando; Nuyts, Johan

2018-03-01

Joint activity and attenuation reconstruction methods from time of flight (TOF) positron emission tomography (PET) data provide an effective solution to attenuation correction when no (or incomplete/inaccurate) information on the attenuation is available. One of the main barriers limiting their use in clinical practice is the lack of validation of these methods on a relatively large patient database. In this contribution, we aim at validating the activity reconstructions of the maximum likelihood activity reconstruction and attenuation registration (MLRR) algorithm on a whole-body patient data set. Furthermore, a partial validation (since the scale problem of the algorithm is avoided for now) of the maximum likelihood activity and attenuation reconstruction (MLAA) algorithm is also provided. We present a quantitative comparison of the joint reconstructions to the current clinical gold-standard maximum likelihood expectation maximization (MLEM) reconstruction with CT-based attenuation correction. Methods: The whole-body TOF-PET emission data of each patient data set is processed as a whole to reconstruct an activity volume covering all the acquired bed positions, which helps to reduce the problem of a scale per bed position in MLAA to a global scale for the entire activity volume. Three reconstruction algorithms are used: MLEM, MLRR and MLAA. A maximum likelihood (ML) scaling of the single scatter simulation (SSS) estimate to the emission data is used for scatter correction. The reconstruction results are then analyzed in different regions of interest. Results: The joint reconstructions of the whole-body patient data set provide better quantification in case of PET and CT misalignments caused by patient and organ motion. Our quantitative analysis shows a difference of -4.2% (±2.3%) and -7.5% (±4.6%) between the joint reconstructions of MLRR and MLAA compared to MLEM, averaged over all regions of interest, respectively. Conclusion: Joint activity and attenuation estimation methods provide a useful means to estimate the tracer distribution in cases where CT-based attenuation images are subject to misalignments or are not available. With an accurate estimate of the scatter contribution in the emission measurements, the joint TOF-PET reconstructions are within clinical acceptable accuracy. Copyright © 2018 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

TU-FG-201-12: Designing a Risk-Based Quality Assurance Program for a Newly Implemented Y-90 Microspheres Procedure

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

Vile, D; Zhang, L; Cuttino, L

2016-06-15

Purpose: To create a quality assurance program based upon a risk-based assessment of a newly implemented SirSpheres Y-90 procedure. Methods: A process map was created for a newly implemented SirSpheres procedure at a community hospital. The process map documented each step of this collaborative procedure, as well as the roles and responsibilities of each member. From the process map, different potential failure modes were determined as well as any current controls in place. From this list, a full failure mode and effects analysis (FMEA) was performed by grading each failure mode’s likelihood of occurrence, likelihood of detection, and potential severity.more » These numbers were then multiplied to compute the risk priority number (RPN) for each potential failure mode. Failure modes were then ranked based on their RPN. Additional controls were then added, with failure modes corresponding to the highest RPNs taking priority. Results: A process map was created that succinctly outlined each step in the SirSpheres procedure in its current implementation. From this, 72 potential failure modes were identified and ranked according to their associated RPN. Quality assurance controls and safety barriers were then added for failure modes associated with the highest risk being addressed first. Conclusion: A quality assurance program was created from a risk-based assessment of the SirSpheres process. Process mapping and FMEA were effective in identifying potential high-risk failure modes for this new procedure, which were prioritized for new quality assurance controls. TG 100 recommends the fault tree analysis methodology to design a comprehensive and effective QC/QM program, yet we found that by simply introducing additional safety barriers to address high RPN failure modes makes the whole process simpler and safer.« less

Multi-hazard Assessment and Scenario Toolbox (MhAST): A Framework for Analyzing Compounding Effects of Multiple Hazards

NASA Astrophysics Data System (ADS)

Sadegh, M.; Moftakhari, H.; AghaKouchak, A.

2017-12-01

Many natural hazards are driven by multiple forcing variables, and concurrence/consecutive extreme events significantly increases risk of infrastructure/system failure. It is a common practice to use univariate analysis based upon a perceived ruling driver to estimate design quantiles and/or return periods of extreme events. A multivariate analysis, however, permits modeling simultaneous occurrence of multiple forcing variables. In this presentation, we introduce the Multi-hazard Assessment and Scenario Toolbox (MhAST) that comprehensively analyzes marginal and joint probability distributions of natural hazards. MhAST also offers a wide range of scenarios of return period and design levels and their likelihoods. Contribution of this study is four-fold: 1. comprehensive analysis of marginal and joint probability of multiple drivers through 17 continuous distributions and 26 copulas, 2. multiple scenario analysis of concurrent extremes based upon the most likely joint occurrence, one ruling variable, and weighted random sampling of joint occurrences with similar exceedance probabilities, 3. weighted average scenario analysis based on a expected event, and 4. uncertainty analysis of the most likely joint occurrence scenario using a Bayesian framework.

Modem Signature Analysis.

DTIC Science & Technology

1982-10-01

AD-A127 993 MODEM SIGNATURE ANALISIS (U) PAR TECHNOLOGY CORP NEW / HARTFORD NY V EDWARDS ET AL. OCT 82 RADC-TR-82-269 F30602-80-C-0264 NCLASSIFIED F/G...as an indication of the class clustering and separation between different classes in the modem data base. It is apparent from the projection that the...that as the clusters disperse, the likelihood of a sample crossing the boundary into an adjacent region and causing a symbol decision error increases. As

An EM-based semi-parametric mixture model approach to the regression analysis of competing-risks data.

PubMed

Ng, S K; McLachlan, G J

2003-04-15

We consider a mixture model approach to the regression analysis of competing-risks data. Attention is focused on inference concerning the effects of factors on both the probability of occurrence and the hazard rate conditional on each of the failure types. These two quantities are specified in the mixture model using the logistic model and the proportional hazards model, respectively. We propose a semi-parametric mixture method to estimate the logistic and regression coefficients jointly, whereby the component-baseline hazard functions are completely unspecified. Estimation is based on maximum likelihood on the basis of the full likelihood, implemented via an expectation-conditional maximization (ECM) algorithm. Simulation studies are performed to compare the performance of the proposed semi-parametric method with a fully parametric mixture approach. The results show that when the component-baseline hazard is monotonic increasing, the semi-parametric and fully parametric mixture approaches are comparable for mildly and moderately censored samples. When the component-baseline hazard is not monotonic increasing, the semi-parametric method consistently provides less biased estimates than a fully parametric approach and is comparable in efficiency in the estimation of the parameters for all levels of censoring. The methods are illustrated using a real data set of prostate cancer patients treated with different dosages of the drug diethylstilbestrol. Copyright 2003 John Wiley & Sons, Ltd.

Mapping grey matter reductions in schizophrenia: an anatomical likelihood estimation analysis of voxel-based morphometry studies.

PubMed

Fornito, A; Yücel, M; Patti, J; Wood, S J; Pantelis, C

2009-03-01

Voxel-based morphometry (VBM) is a popular tool for mapping neuroanatomical changes in schizophrenia patients. Several recent meta-analyses have identified the brain regions in which patients most consistently show grey matter reductions, although they have not examined whether such changes reflect differences in grey matter concentration (GMC) or grey matter volume (GMV). These measures assess different aspects of grey matter integrity, and may therefore reflect different pathological processes. In this study, we used the Anatomical Likelihood Estimation procedure to analyse significant differences reported in 37 VBM studies of schizophrenia patients, incorporating data from 1646 patients and 1690 controls, and compared the findings of studies using either GMC or GMV to index grey matter differences. Analysis of all studies combined indicated that grey matter reductions in a network of frontal, temporal, thalamic and striatal regions are among the most frequently reported in literature. GMC reductions were generally larger and more consistent than GMV reductions, and were more frequent in the insula, medial prefrontal, medial temporal and striatal regions. GMV reductions were more frequent in dorso-medial frontal cortex, and lateral and orbital frontal areas. These findings support the primacy of frontal, limbic, and subcortical dysfunction in the pathophysiology of schizophrenia, and suggest that the grey matter changes observed with MRI may not necessarily result from a unitary pathological process.

Joint effect of MCP-1 genotype GG and MMP-1 genotype 2G/2G increases the likelihood of developing pulmonary tuberculosis in BCG-vaccinated individuals.

PubMed

Ganachari, Malathesha; Ruiz-Morales, Jorge A; Gomez de la Torre Pretell, Juan C; Dinh, Jeffrey; Granados, Julio; Flores-Villanueva, Pedro O

2010-01-25

We previously reported that the -2518 MCP-1 genotype GG increases the likelihood of developing tuberculosis (TB) in non-BCG-vaccinated Mexicans and Koreans. Here, we tested the hypothesis that this genotype, alone or together with the -1607 MMP-1 functional polymorphism, increases the likelihood of developing TB in BCG-vaccinated individuals. We conducted population-based case-control studies of BCG-vaccinated individuals in Mexico and Peru that included 193 TB cases and 243 healthy tuberculin-positive controls from Mexico and 701 TB cases and 796 controls from Peru. We also performed immunohistochemistry (IHC) analysis of lymph nodes from carriers of relevant two-locus genotypes and in vitro studies to determine how these variants may operate to increase the risk of developing active disease. We report that a joint effect between the -2518 MCP-1 genotype GG and the -1607 MMP-1 genotype 2G/2G consistently increases the odds of developing TB 3.59-fold in Mexicans and 3.9-fold in Peruvians. IHC analysis of lymph nodes indicated that carriers of the two-locus genotype MCP-1 GG MMP-1 2G/2G express the highest levels of both MCP-1 and MMP-1. Carriers of these susceptibility genotypes might be at increased risk of developing TB because they produce high levels of MCP-1, which enhances the induction of MMP-1 production by M. tuberculosis-sonicate antigens to higher levels than in carriers of the other two-locus MCP-1 MMP-1 genotypes studied. This notion was supported by in vitro experiments and luciferase based promoter activity assay. MMP-1 may destabilize granuloma formation and promote tissue damage and disease progression early in the infection. Our findings may foster the development of new and personalized therapeutic approaches targeting MCP-1 and/or MMP-1.

A population-based analysis of use and outcomes of laparoscopic bariatric surgery across socioeconomic groups in Taiwan.

PubMed

Huang, Chun-Che; Huang, Yu-Tung; Chiu, Chong-Chi

2015-11-11

With the growing development of minimally invasive techniques for the treatment of morbid obesity, laparoscopic bariatric surgery (LBS) is increasingly performed. This study aimed to assess the association between patients' socioeconomic status (SES) and the likelihood of undergoing LBS and related outcomes in Taiwan. This nationwide population-based study was conducted by using data from Taiwan's National Health Insurance Research Database. A total of 3678 morbidly obese patients aged 18 years and older who underwent conventional open bariatric surgery or LBS were identified between 2004 and 2011. Regression analyses were performed using generalized estimating equation (GEE) models to account for the nesting of patients within physician to assess patients' SES category associated with the use of LBS and related outcomes. Odds ratios (ORs) and 95 % confidence intervals (CIs) were estimated. Compared with those with medium and low SES (84.6 % and 80.2 %), patients with high SES (88.1 %) had the highest percentage of undergoing LBS (P < 0.001). After adjusting for patient demographics, institution and surgeon characteristics, the multivariate GEE analysis revealed that the highest likelihood of undergoing LBS was noted in morbidly obese patients with high SES (OR = 1.45, 95 % CI 1.10-1.90), followed by those with medium SES (OR = 1.27, 95 % CI 1.04-1.56). In addition, patients with high SES had slightly lower length of hospital stay (LOS; OR = 0.90, 95 % CI 0.82-0.99) and hospital treatment cost (OR = 0.93, 95 % CI 0.87-0.99) than their counterparts after adjustment. The increased likelihood of undergoing LBS and lower LOS and hospital treatment cost were noted among morbidly obese patients with higher SES. This finding suggests there is the need to improve clinical practice and reduce health disparities in the surgical treatment of morbidly obese patients.

Joint Effect of MCP-1 Genotype GG and MMP-1 Genotype 2G/2G Increases the Likelihood of Developing Pulmonary Tuberculosis in BCG-Vaccinated Individuals

PubMed Central

Ganachari, Malathesha; Ruiz-Morales, Jorge A.; Gomez de la Torre Pretell, Juan C.; Dinh, Jeffrey; Granados, Julio; Flores-Villanueva, Pedro O.

2010-01-01

We previously reported that the – 2518 MCP-1 genotype GG increases the likelihood of developing tuberculosis (TB) in non-BCG-vaccinated Mexicans and Koreans. Here, we tested the hypothesis that this genotype, alone or together with the – 1607 MMP-1 functional polymorphism, increases the likelihood of developing TB in BCG-vaccinated individuals. We conducted population-based case-control studies of BCG-vaccinated individuals in Mexico and Peru that included 193 TB cases and 243 healthy tuberculin-positive controls from Mexico and 701 TB cases and 796 controls from Peru. We also performed immunohistochemistry (IHC) analysis of lymph nodes from carriers of relevant two-locus genotypes and in vitro studies to determine how these variants may operate to increase the risk of developing active disease. We report that a joint effect between the – 2518 MCP-1 genotype GG and the – 1607 MMP-1 genotype 2G/2G consistently increases the odds of developing TB 3.59-fold in Mexicans and 3.9-fold in Peruvians. IHC analysis of lymph nodes indicated that carriers of the two-locus genotype MCP-1 GG MMP-1 2G/2G express the highest levels of both MCP-1 and MMP-1. Carriers of these susceptibility genotypes might be at increased risk of developing TB because they produce high levels of MCP-1, which enhances the induction of MMP-1 production by M. tuberculosis-sonicate antigens to higher levels than in carriers of the other two-locus MCP-1 MMP-1 genotypes studied. This notion was supported by in vitro experiments and luciferase based promoter activity assay. MMP-1 may destabilize granuloma formation and promote tissue damage and disease progression early in the infection. Our findings may foster the development of new and personalized therapeutic approaches targeting MCP-1 and/or MMP-1. PMID:20111728

Approximate likelihood approaches for detecting the influence of primordial gravitational waves in cosmic microwave background polarization

NASA Astrophysics Data System (ADS)

Pan, Zhen; Anderes, Ethan; Knox, Lloyd

2018-05-01

One of the major targets for next-generation cosmic microwave background (CMB) experiments is the detection of the primordial B-mode signal. Planning is under way for Stage-IV experiments that are projected to have instrumental noise small enough to make lensing and foregrounds the dominant source of uncertainty for estimating the tensor-to-scalar ratio r from polarization maps. This makes delensing a crucial part of future CMB polarization science. In this paper we present a likelihood method for estimating the tensor-to-scalar ratio r from CMB polarization observations, which combines the benefits of a full-scale likelihood approach with the tractability of the quadratic delensing technique. This method is a pixel space, all order likelihood analysis of the quadratic delensed B modes, and it essentially builds upon the quadratic delenser by taking into account all order lensing and pixel space anomalies. Its tractability relies on a crucial factorization of the pixel space covariance matrix of the polarization observations which allows one to compute the full Gaussian approximate likelihood profile, as a function of r , at the same computational cost of a single likelihood evaluation.

Comparison of statistical sampling methods with ScannerBit, the GAMBIT scanning module

NASA Astrophysics Data System (ADS)

Martinez, Gregory D.; McKay, James; Farmer, Ben; Scott, Pat; Roebber, Elinore; Putze, Antje; Conrad, Jan

2017-11-01

We introduce ScannerBit, the statistics and sampling module of the public, open-source global fitting framework GAMBIT. ScannerBit provides a standardised interface to different sampling algorithms, enabling the use and comparison of multiple computational methods for inferring profile likelihoods, Bayesian posteriors, and other statistical quantities. The current version offers random, grid, raster, nested sampling, differential evolution, Markov Chain Monte Carlo (MCMC) and ensemble Monte Carlo samplers. We also announce the release of a new standalone differential evolution sampler, Diver, and describe its design, usage and interface to ScannerBit. We subject Diver and three other samplers (the nested sampler MultiNest, the MCMC GreAT, and the native ScannerBit implementation of the ensemble Monte Carlo algorithm T-Walk) to a battery of statistical tests. For this we use a realistic physical likelihood function, based on the scalar singlet model of dark matter. We examine the performance of each sampler as a function of its adjustable settings, and the dimensionality of the sampling problem. We evaluate performance on four metrics: optimality of the best fit found, completeness in exploring the best-fit region, number of likelihood evaluations, and total runtime. For Bayesian posterior estimation at high resolution, T-Walk provides the most accurate and timely mapping of the full parameter space. For profile likelihood analysis in less than about ten dimensions, we find that Diver and MultiNest score similarly in terms of best fit and speed, outperforming GreAT and T-Walk; in ten or more dimensions, Diver substantially outperforms the other three samplers on all metrics.

Uncertain call likelihood negatively affects sleep and next-day cognitive performance while on-call in a laboratory environment.

PubMed

Sprajcer, Madeline; Jay, Sarah M; Vincent, Grace E; Vakulin, Andrew; Lack, Leon; Ferguson, Sally A

2018-05-11

On-call working arrangements are employed in a number of industries to manage unpredictable events, and often involve tasks that are safety- or time-critical. This study investigated the effects of call likelihood during an overnight on-call shift on self-reported pre-bed anxiety, sleep and next-day cognitive performance. A four-night laboratory-based protocol was employed, with an adaptation, a control and two counterbalanced on-call nights. On one on-call night, participants were instructed that they would definitely be called during the night, while on the other on-call night they were told they may be called. The State-Trait Anxiety Inventory form x-1 was used to investigate pre-bed anxiety, and sleep was assessed using polysomnography and power spectral analysis of the sleep electroencephalographic analysis. Cognitive performance was assessed four times daily using a 10-min psychomotor vigilance task. Participants felt more anxious before bed when they were definitely going to be called, compared with the control and maybe conditions. Conversely, participants experienced significantly less non-rapid eye movement and stage two sleep and poorer cognitive performance when told they may be called. Further, participants had significantly more rapid eye movement sleep in the maybe condition, which may be an adaptive response to the stress associated with this on-call condition. It appears that self-reported anxiety may not be linked with sleep outcomes while on-call. However, this research indicates that it is important to take call likelihood into consideration when constructing rosters and risk-management systems for on-call workers.

Diagnostic Performance of Electronic Syndromic Surveillance Systems in Acute Care

PubMed Central

Kashiouris, M.; O’Horo, J.C.; Pickering, B.W.; Herasevich, V.

2013-01-01

Context Healthcare Electronic Syndromic Surveillance (ESS) is the systematic collection, analysis and interpretation of ongoing clinical data with subsequent dissemination of results, which aid clinical decision-making. Objective To evaluate, classify and analyze the diagnostic performance, strengths and limitations of existing acute care ESS systems. Data Sources All available to us studies in Ovid MEDLINE, Ovid EMBASE, CINAHL and Scopus databases, from as early as January 1972 through the first week of September 2012. Study Selection: Prospective and retrospective trials, examining the diagnostic performance of inpatient ESS and providing objective diagnostic data including sensitivity, specificity, positive and negative predictive values. Data Extraction Two independent reviewers extracted diagnostic performance data on ESS systems, including clinical area, number of decision points, sensitivity and specificity. Positive and negative likelihood ratios were calculated for each healthcare ESS system. A likelihood matrix summarizing the various ESS systems performance was created. Results The described search strategy yielded 1639 articles. Of these, 1497 were excluded on abstract information. After full text review, abstraction and arbitration with a third reviewer, 33 studies met inclusion criteria, reporting 102,611 ESS decision points. The yielded I2 was high (98.8%), precluding meta-analysis. Performance was variable, with sensitivities ranging from 21% –100% and specificities ranging from 5%-100%. Conclusions There is significant heterogeneity in the diagnostic performance of the available ESS implements in acute care, stemming from the wide spectrum of different clinical entities and ESS systems. Based on the results, we introduce a conceptual framework using a likelihood ratio matrix for evaluation and meaningful application of future, frontline clinical decision support systems. PMID:23874359

PlanetPack: A radial-velocity time-series analysis tool facilitating exoplanets detection, characterization, and dynamical simulations

NASA Astrophysics Data System (ADS)

Baluev, Roman V.

2013-08-01

We present PlanetPack, a new software tool that we developed to facilitate and standardize the advanced analysis of radial velocity (RV) data for the goal of exoplanets detection, characterization, and basic dynamical N-body simulations. PlanetPack is a command-line interpreter, that can run either in an interactive mode or in a batch mode of automatic script interpretation. Its major abilities include: (i) advanced RV curve fitting with the proper maximum-likelihood treatment of unknown RV jitter; (ii) user-friendly multi-Keplerian as well as Newtonian N-body RV fits; (iii) use of more efficient maximum-likelihood periodograms that involve the full multi-planet fitting (sometimes called as “residual” or “recursive” periodograms); (iv) easily calculatable parametric 2D likelihood function level contours, reflecting the asymptotic confidence regions; (v) fitting under some useful functional constraints is user-friendly; (vi) basic tasks of short- and long-term planetary dynamical simulation using a fast Everhart-type integrator based on Gauss-Legendre spacings; (vii) fitting the data with red noise (auto-correlated errors); (viii) various analytical and numerical methods for the tasks of determining the statistical significance. It is planned that further functionality may be added to PlanetPack in the future. During the development of this software, a lot of effort was made to improve the calculational speed, especially for CPU-demanding tasks. PlanetPack was written in pure C++ (standard of 1998/2003), and is expected to be compilable and useable on a wide range of platforms.

Neutron Tomography of a Fuel Cell: Statistical Learning Implementation of a Penalized Likelihood Method

NASA Astrophysics Data System (ADS)

Coakley, Kevin J.; Vecchia, Dominic F.; Hussey, Daniel S.; Jacobson, David L.

2013-10-01

At the NIST Neutron Imaging Facility, we collect neutron projection data for both the dry and wet states of a Proton-Exchange-Membrane (PEM) fuel cell. Transmitted thermal neutrons captured in a scintillator doped with lithium-6 produce scintillation light that is detected by an amorphous silicon detector. Based on joint analysis of the dry and wet state projection data, we reconstruct a residual neutron attenuation image with a Penalized Likelihood method with an edge-preserving Huber penalty function that has two parameters that control how well jumps in the reconstruction are preserved and how well noisy fluctuations are smoothed out. The choice of these parameters greatly influences the resulting reconstruction. We present a data-driven method that objectively selects these parameters, and study its performance for both simulated and experimental data. Before reconstruction, we transform the projection data so that the variance-to-mean ratio is approximately one. For both simulated and measured projection data, the Penalized Likelihood method reconstruction is visually sharper than a reconstruction yielded by a standard Filtered Back Projection method. In an idealized simulation experiment, we demonstrate that the cross validation procedure selects regularization parameters that yield a reconstruction that is nearly optimal according to a root-mean-square prediction error criterion.

Empirical best linear unbiased prediction method for small areas with restricted maximum likelihood and bootstrap procedure to estimate the average of household expenditure per capita in Banjar Regency

NASA Astrophysics Data System (ADS)

Aminah, Agustin Siti; Pawitan, Gandhi; Tantular, Bertho

2017-03-01

So far, most of the data published by Statistics Indonesia (BPS) as data providers for national statistics are still limited to the district level. Less sufficient sample size for smaller area levels to make the measurement of poverty indicators with direct estimation produced high standard error. Therefore, the analysis based on it is unreliable. To solve this problem, the estimation method which can provide a better accuracy by combining survey data and other auxiliary data is required. One method often used for the estimation is the Small Area Estimation (SAE). There are many methods used in SAE, one of them is Empirical Best Linear Unbiased Prediction (EBLUP). EBLUP method of maximum likelihood (ML) procedures does not consider the loss of degrees of freedom due to estimating β with β ^. This drawback motivates the use of the restricted maximum likelihood (REML) procedure. This paper proposed EBLUP with REML procedure for estimating poverty indicators by modeling the average of household expenditures per capita and implemented bootstrap procedure to calculate MSE (Mean Square Error) to compare the accuracy EBLUP method with the direct estimation method. Results show that EBLUP method reduced MSE in small area estimation.

Is it possible to predict office hysteroscopy failure?

PubMed

Cobellis, Luigi; Castaldi, Maria Antonietta; Giordano, Valentino; De Franciscis, Pasquale; Signoriello, Giuseppe; Colacurci, Nicola

2014-10-01

The purpose of this study was to develop a clinical tool, the HFI (Hysteroscopy Failure Index), which gives criteria to predict hysteroscopic examination failure. This was a retrospective diagnostic test study, aimed to validate the HFI, set at the Department of Gynaecology, Obstetric and Reproductive Science of the Second University of Naples, Italy. The HFI was applied to our database of 995 consecutive women, who underwent office based to assess abnormal uterine bleeding (AUB), infertility, cervical polyps, and abnormal sonographic patterns (postmenopausal endometrial thickness of more than 5mm, endometrial hyperechogenic spots, irregular endometrial line, suspect of uterine septa). Demographic characteristics, previous surgery, recurrent infections, sonographic data, Estro-Progestins, IUD and menopausal status were collected. Receiver operating characteristic (ROC) curve analysis was used to assess the ability of the model to identify patients who were correctly identified (true positives) divided by the total number of failed hysteroscopies (true positives+false negatives). Positive and Negative Likelihood Ratios with 95%CI were calculated. The HFI score is able to predict office hysteroscopy failure in 76% of cases. Moreover, the Positive likelihood ratio was 11.37 (95% CI: 8.49-15.21), and the Negative likelihood ratio was 0.33 (95% CI: 0.27-0.41). Hysteroscopy failure index was able to retrospectively predict office hysteroscopy failure. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Pathways to Early Coital Debut for Adolescent Girls: A Recursive Partitioning Analysis

PubMed Central

Pearson, Matthew R.; Kholodkov, Tatyana; Henson, James M.; Impett, Emily A.

2011-01-01

The current study examined pathways to early coital debut among early to middle adolescent girls in the United States. In a two-year longitudinal study of 104 adolescent girls, we conducted Recursive Partitioning (RP) analyses to examine the specific factors that were related to engaging in first intercourse by the 10th grade among adolescent girls who had not yet engaged in sexual intercourse by the 8th grade. RP analyses identified subsamples of girls who had low, medium, and high likelihoods of engaging in early coital debut based on six variables (i.e., school aspirations, early physical intimacy experiences, depression, body objectification, body image, and relationship inauthenticity). For example, girls in the lowest likelihood group (3% had engaged in sex by the 10th grade) reported no prior experiences with being touched under their clothes, low body objectification, high aspirations to complete graduate education, and low depressive symptoms; girls in the highest likelihood group (75% had engaged in sex by the 10th grade) also reported no prior experiences with being touched under their clothes but had high levels of body objectification. The implications of these analyses for the development of female adolescent sexuality as well as for advances in quantitative methods are discussed. PMID:21512947

Radio Frequency Interference Detection for Passive Remote Sensing Using Eigenvalue Analysis

NASA Technical Reports Server (NTRS)

Schoenwald, Adam; Kim, Seung-Jun; Mohammed-Tano, Priscilla

2017-01-01

Radio frequency interference (RFI) can corrupt passive remote sensing measurements taken with microwave radiometers. With the increasingly utilized spectrum and the push for larger bandwidth radiometers, the likelihood of RFI contamination has grown significantly. In this work, an eigenvalue-based algorithm is developed to detect the presence of RFI and provide estimates of RFI-free radiation levels. Simulated tests show that the proposed detector outperforms conventional kurtosis-based RFI detectors in the low-to-medium interferece-to-noise-power-ratio (INR) regime under continuous wave (CW) and quadrature phase shift keying (QPSK) RFIs.

Radio Frequency Interference Detection for Passive Remote Sensing Using Eigenvalue Analysis

NASA Technical Reports Server (NTRS)

Schoenwald, Adam J.; Kim, Seung-Jun; Mohammed, Priscilla N.

2017-01-01

Radio frequency interference (RFI) can corrupt passive remote sensing measurements taken with microwave radiometers. With the increasingly utilized spectrum and the push for larger bandwidth radiometers, the likelihood of RFI contamination has grown significantly. In this work, an eigenvalue-based algorithm is developed to detect the presence of RFI and provide estimates of RFI-free radiation levels. Simulated tests show that the proposed detector outperforms conventional kurtosis-based RFI detectors in the low-to-medium interference-to-noise-power-ratio (INR) regime under continuous wave (CW) and quadrature phase shift keying (QPSK) RFIs.

Among-character rate variation distributions in phylogenetic analysis of discrete morphological characters.

PubMed

Harrison, Luke B; Larsson, Hans C E

2015-03-01

Likelihood-based methods are commonplace in phylogenetic systematics. Although much effort has been directed toward likelihood-based models for molecular data, comparatively less work has addressed models for discrete morphological character (DMC) data. Among-character rate variation (ACRV) may confound phylogenetic analysis, but there have been few analyses of the magnitude and distribution of rate heterogeneity among DMCs. Using 76 data sets covering a range of plants, invertebrate, and vertebrate animals, we used a modified version of MrBayes to test equal, gamma-distributed and lognormally distributed models of ACRV, integrating across phylogenetic uncertainty using Bayesian model selection. We found that in approximately 80% of data sets, unequal-rates models outperformed equal-rates models, especially among larger data sets. Moreover, although most data sets were equivocal, more data sets favored the lognormal rate distribution relative to the gamma rate distribution, lending some support for more complex character correlations than in molecular data. Parsimony estimation of the underlying rate distributions in several data sets suggests that the lognormal distribution is preferred when there are many slowly evolving characters and fewer quickly evolving characters. The commonly adopted four rate category discrete approximation used for molecular data was found to be sufficient to approximate a gamma rate distribution with discrete characters. However, among the two data sets tested that favored a lognormal rate distribution, the continuous distribution was better approximated with at least eight discrete rate categories. Although the effect of rate model on the estimation of topology was difficult to assess across all data sets, it appeared relatively minor between the unequal-rates models for the one data set examined carefully. As in molecular analyses, we argue that researchers should test and adopt the most appropriate model of rate variation for the data set in question. As discrete characters are increasingly used in more sophisticated likelihood-based phylogenetic analyses, it is important that these studies be built on the most appropriate and carefully selected underlying models of evolution. © The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Antidepressant Prescribing by Pediatricians: A Mixed-Methods Analysis.

PubMed

Tulisiak, Anne K; Klein, Jillian A; Harris, Emily; Luft, Marissa J; Schroeder, Heidi K; Mossman, Sarah A; Varney, Sara T; Keeshin, Brooks R; Cotton, Sian; Strawn, Jeffrey R

2017-01-01

Among pediatricians, perceived knowledge of efficacy, tolerability, dosing, and side effects of antidepressants represent significant sources of variability in the use of these medications in youth with depressive and anxiety disorders. Importantly, the qualitative factors that relate to varying levels of comfort with antidepressants and willingness to prescribe are poorly understood. Using a mixed-methods approach, in-depth interviews were conducted with community-based and academic medical center-based pediatricians (N = 14). Interviews were audio recorded and iteratively coded; themes were then generated using inductive thematic analysis. The relationship between demographic factors, knowledge of antidepressants, dosing, and side effects, as well as prescribing likelihood scores for depressive disorders, anxiety disorders or co-morbid anxiety and depressive disorders, were evaluated using mixed models. Pediatricians reported antidepressants to be effective and well-tolerated. However, the likelihood of individual physicians initiating an antidepressant was significantly lower for anxiety disorders relative to depressive disorders with similar functional impairment. Pediatricians considered symptom severity/functional impairment, age and the availability of psychotherapy as they considered prescribing antidepressants to individual patients. Antidepressant choice was related to the physician׳s perceived knowledge and comfort with a particular antidepressant, financial factors, and the disorder-specific evidence base for that particular medication and consultation with mental health practitioners. Pediatricians noted similar efficacy and tolerability profiles for antidepressants in youth with depressive disorders and anxiety disorders, but tended to utilize "therapy first" approaches for anxiety disorders relative to depressive disorders. Parental and family factors that influenced prescribing of antidepressants by pediatricians included parental ambivalence, family-related dysfunction and impairment secondary to the child׳s psychopathology as well as the child׳s psychosocial milieu. Pediatricians consider patient- and family-specific challenges when choosing prescribing antidepressant medications and are, in general, less likely to prescribe antidepressants for youth with anxiety disorders compared to youth with depressive disorders. The lower likelihood of prescribing antidepressants for anxious youth is not related to perception of the efficacy or tolerability, but rather to a perception that anxiety disorders are less impairing and more appropriately managed with psychotherapy. Copyright © 2016 Mosby, Inc. All rights reserved.

Recognizing and labeling sex-based and sexual harassment in the health care workplace.

PubMed

Madison, J; Minichiello, V

2000-01-01

To explore how registered nurses (RNs) recognized and labeled incidents of sex-based and sexual harassment in the Australian health care workplace. Qualitative, using 16 unstructured interviews with registered nurses in Australia. Verbatim transcripts were analyzed largely by inductive analysis. Key categories were identified as themes or concepts for analysis. RNs reported several indicators of sexual harassment, including the invasion of space, confirmation from others, lack of respect, the deliberate nature of the behavior, perceived power or control, overly friendly behavior, and a sexualized workplace. RNs rarely labeled harassing behaviors as sex-based or sexual harassment. Many forces reduce the likelihood that RNs will correctly recognize and label unwelcome sexualized behavior as sexual harassment. Recognition is associated with a variety of workplace behaviors that sometimes precede harassment. Implications for the health care workplace are discussed.

The Impact of Hurricane Katrina on Students’ Behavioral Disorder: A Difference-in-Difference Analysis

PubMed Central

Tian, Xian-Liang; Guan, Xian

2015-01-01

Objective: The objective of this paper is to examine the impact of Hurricane Katrina on displaced students’ behavioral disorder. Methods: First, we determine displaced students’ likelihood of discipline infraction each year relative to non-evacuees using all K12 student records of the U.S. state of Louisiana during the period of 2000–2008. Second, we investigate the impact of hurricane on evacuee students’ in-school behavior in a difference-in-difference framework. The quasi-experimental nature of the hurricane makes this framework appropriate with the advantage that the problem of endogeneity is of least concern and the causal effect of interest can be reasonably identified. Results: Preliminary analysis demonstrates a sharp increase in displaced students’ relative likelihood of discipline infraction around 2005 when the hurricane occurred. Further, formal difference-in-difference analysis confirms the results. To be specific, post Katrina, displaced students’ relative likelihood of any discipline infraction has increased by 7.3% whereas the increase in the relative likelihood for status offense, offense against person, offense against property and serious crime is 4%, 1.5%, 3.8% and 2.1%, respectively. Conclusion: When disasters occur, as was the case with Hurricane Katrina, in addition to assistance for adult evacuees, governments, in cooperation with schools, should also provide aid and assistance to displaced children to support their mental health and in-school behavior. PMID:26006127

The Impact of Hurricane Katrina on Students' Behavioral Disorder: A Difference-in-Difference Analysis.

PubMed

Tian, Xian-Liang; Guan, Xian

2015-05-22

The objective of this paper is to examine the impact of Hurricane Katrina on displaced students' behavioral disorder. First, we determine displaced students' likelihood of discipline infraction each year relative to non-evacuees using all K12 student records of the U.S. state of Louisiana during the period of 2000-2008. Second, we investigate the impact of hurricane on evacuee students' in-school behavior in a difference-in-difference framework. The quasi-experimental nature of the hurricane makes this framework appropriate with the advantage that the problem of endogeneity is of least concern and the causal effect of interest can be reasonably identified. Preliminary analysis demonstrates a sharp increase in displaced students' relative likelihood of discipline infraction around 2005 when the hurricane occurred. Further, formal difference-in-difference analysis confirms the results. To be specific, post Katrina, displaced students' relative likelihood of any discipline infraction has increased by 7.3% whereas the increase in the relative likelihood for status offense, offense against person, offense against property and serious crime is 4%, 1.5%, 3.8% and 2.1%, respectively. When disasters occur, as was the case with Hurricane Katrina, in addition to assistance for adult evacuees, governments, in cooperation with schools, should also provide aid and assistance to displaced children to support their mental health and in-school behavior.

Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis.

PubMed

Wang, Longfei; Lee, Sungyoung; Gim, Jungsoo; Qiao, Dandi; Cho, Michael; Elston, Robert C; Silverman, Edwin K; Won, Sungho

2016-09-01

Family-based designs have been repeatedly shown to be powerful in detecting the significant rare variants associated with human diseases. Furthermore, human diseases are often defined by the outcomes of multiple phenotypes, and thus we expect multivariate family-based analyses may be very efficient in detecting associations with rare variants. However, few statistical methods implementing this strategy have been developed for family-based designs. In this report, we describe one such implementation: the multivariate family-based rare variant association tool (mFARVAT). mFARVAT is a quasi-likelihood-based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at http://healthstat.snu.ac.kr/software/mfarvat/, implemented in C++ and supported on Linux and MS Windows. © 2016 WILEY PERIODICALS, INC.

Which Statistic Should Be Used to Detect Item Preknowledge When the Set of Compromised Items Is Known?

PubMed

Sinharay, Sandip

2017-09-01

Benefiting from item preknowledge is a major type of fraudulent behavior during educational assessments. Belov suggested the posterior shift statistic for detection of item preknowledge and showed its performance to be better on average than that of seven other statistics for detection of item preknowledge for a known set of compromised items. Sinharay suggested a statistic based on the likelihood ratio test for detection of item preknowledge; the advantage of the statistic is that its null distribution is known. Results from simulated and real data and adaptive and nonadaptive tests are used to demonstrate that the Type I error rate and power of the statistic based on the likelihood ratio test are very similar to those of the posterior shift statistic. Thus, the statistic based on the likelihood ratio test appears promising in detecting item preknowledge when the set of compromised items is known.

Preoperative Serum Thyrotropin to Thyroglobulin Ratio Is Effective for Thyroid Nodule Evaluation in Euthyroid Patients.

PubMed

Wang, Lina; Li, Hao; Yang, Zhongyuan; Guo, Zhuming; Zhang, Quan

2015-07-01

This study was designed to assess the efficiency of the serum thyrotropin to thyroglobulin ratio for thyroid nodule evaluation in euthyroid patients. Cross-sectional study. Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China. Retrospective analysis was performed for 400 previously untreated cases presenting with thyroid nodules. Thyroid function was tested with commercially available radioimmunoassays. The receiver operating characteristic curves were constructed to determine cutoff values. The efficacy of the thyrotropin:thyroglobulin ratio and thyroid-stimulating hormone for thyroid nodule evaluation was evaluated in terms of sensitivity, specificity, positive predictive value, positive likelihood ratio, negative likelihood ratio, and odds ratio. In receiver operating characteristic curve analysis, the area under the curve was 0.746 for the thyrotropin:thyroglobulin ratio and 0.659 for thyroid-stimulating hormone. With a cutoff point value of 24.97 IU/g for the thyrotropin:thyroglobulin ratio, the sensitivity, specificity, positive predictive value, positive likelihood ratio, and negative likelihood ratio were 78.9%, 60.8%, 75.5%, 2.01, and 0.35, respectively. The odds ratio for the thyrotropin:thyroglobulin ratio indicating malignancy was 5.80. With a cutoff point value of 1.525 µIU/mL for thyroid-stimulating hormone, the sensitivity, specificity, positive predictive value, positive likelihood ratio, and negative likelihood ratio were 74.0%, 53.2%, 70.8%, 1.58, and 0.49, respectively. The odds ratio indicating malignancy for thyroid-stimulating hormone was 3.23. Increasing preoperative serum thyrotropin:thyroglobulin ratio is a risk factor for thyroid carcinoma, and the correlation of the thyrotropin:thyroglobulin ratio to malignancy is higher than that for serum thyroid-stimulating hormone. © American Academy of Otolaryngology—Head and Neck Surgery Foundation 2015.

Incorporating real-time traffic and weather data to explore road accident likelihood and severity in urban arterials.

PubMed

Theofilatos, Athanasios

2017-06-01

The effective treatment of road accidents and thus the enhancement of road safety is a major concern to societies due to the losses in human lives and the economic and social costs. The investigation of road accident likelihood and severity by utilizing real-time traffic and weather data has recently received significant attention by researchers. However, collected data mainly stem from freeways and expressways. Consequently, the aim of the present paper is to add to the current knowledge by investigating accident likelihood and severity by exploiting real-time traffic and weather data collected from urban arterials in Athens, Greece. Random Forests (RF) are firstly applied for preliminary analysis purposes. More specifically, it is aimed to rank candidate variables according to their relevant importance and provide a first insight on the potential significant variables. Then, Bayesian logistic regression as well finite mixture and mixed effects logit models are applied to further explore factors associated with accident likelihood and severity respectively. Regarding accident likelihood, the Bayesian logistic regression showed that variations in traffic significantly influence accident occurrence. On the other hand, accident severity analysis revealed a generally mixed influence of traffic variations on accident severity, although international literature states that traffic variations increase severity. Lastly, weather parameters did not find to have a direct influence on accident likelihood or severity. The study added to the current knowledge by incorporating real-time traffic and weather data from urban arterials to investigate accident occurrence and accident severity mechanisms. The identification of risk factors can lead to the development of effective traffic management strategies to reduce accident occurrence and severity of injuries in urban arterials. Copyright © 2017 Elsevier Ltd and National Safety Council. All rights reserved.

Updated logistic regression equations for the calculation of post-fire debris-flow likelihood in the western United States

USGS Publications Warehouse

Staley, Dennis M.; Negri, Jacquelyn A.; Kean, Jason W.; Laber, Jayme L.; Tillery, Anne C.; Youberg, Ann M.

2016-06-30

Wildfire can significantly alter the hydrologic response of a watershed to the extent that even modest rainstorms can generate dangerous flash floods and debris flows. To reduce public exposure to hazard, the U.S. Geological Survey produces post-fire debris-flow hazard assessments for select fires in the western United States. We use publicly available geospatial data describing basin morphology, burn severity, soil properties, and rainfall characteristics to estimate the statistical likelihood that debris flows will occur in response to a storm of a given rainfall intensity. Using an empirical database and refined geospatial analysis methods, we defined new equations for the prediction of debris-flow likelihood using logistic regression methods. We showed that the new logistic regression model outperformed previous models used to predict debris-flow likelihood.

The effects of gender, family status, and race on sentencing decisions.

PubMed

Freiburger, Tina L

2010-01-01

This study sought to determine the effects of family role, gender, and race on judges' sentencing decisions. To assess these effects, factorial surveys were sent to 360 Court of Common Plea judges who presided over criminal court cases in the state. Survey administration resulted in a 51% response rate. The findings indicate that defendants who were depicted as performing caretaker roles had a significantly decreased likelihood of incarceration. Further analysis found that the reduction in likelihood of incarceration for being a caretaker was larger for males than for females. Examination of the interaction of familial role with race found that familial role equally reduced the likelihood of incarceration for black and white females. Familial responsibility, however, resulted in a significantly greater decrease in likelihood of incarceration for black men than for white men. 2009 John Wiley & Sons, Ltd.

Likelihood-Ratio DIF Testing: Effects of Nonnormality

ERIC Educational Resources Information Center

Woods, Carol M.

2008-01-01

Differential item functioning (DIF) occurs when an item has different measurement properties for members of one group versus another. Likelihood-ratio (LR) tests for DIF based on item response theory (IRT) involve statistically comparing IRT models that vary with respect to their constraints. A simulation study evaluated how violation of the…

Three methods to construct predictive models using logistic regression and likelihood ratios to facilitate adjustment for pretest probability give similar results.

PubMed

Chan, Siew Foong; Deeks, Jonathan J; Macaskill, Petra; Irwig, Les

2008-01-01

To compare three predictive models based on logistic regression to estimate adjusted likelihood ratios allowing for interdependency between diagnostic variables (tests). This study was a review of the theoretical basis, assumptions, and limitations of published models; and a statistical extension of methods and application to a case study of the diagnosis of obstructive airways disease based on history and clinical examination. Albert's method includes an offset term to estimate an adjusted likelihood ratio for combinations of tests. Spiegelhalter and Knill-Jones method uses the unadjusted likelihood ratio for each test as a predictor and computes shrinkage factors to allow for interdependence. Knottnerus' method differs from the other methods because it requires sequencing of tests, which limits its application to situations where there are few tests and substantial data. Although parameter estimates differed between the models, predicted "posttest" probabilities were generally similar. Construction of predictive models using logistic regression is preferred to the independence Bayes' approach when it is important to adjust for dependency of tests errors. Methods to estimate adjusted likelihood ratios from predictive models should be considered in preference to a standard logistic regression model to facilitate ease of interpretation and application. Albert's method provides the most straightforward approach.

Bayesian structural equation modeling in sport and exercise psychology.

PubMed

Stenling, Andreas; Ivarsson, Andreas; Johnson, Urban; Lindwall, Magnus

2015-08-01

Bayesian statistics is on the rise in mainstream psychology, but applications in sport and exercise psychology research are scarce. In this article, the foundations of Bayesian analysis are introduced, and we will illustrate how to apply Bayesian structural equation modeling in a sport and exercise psychology setting. More specifically, we contrasted a confirmatory factor analysis on the Sport Motivation Scale II estimated with the most commonly used estimator, maximum likelihood, and a Bayesian approach with weakly informative priors for cross-loadings and correlated residuals. The results indicated that the model with Bayesian estimation and weakly informative priors provided a good fit to the data, whereas the model estimated with a maximum likelihood estimator did not produce a well-fitting model. The reasons for this discrepancy between maximum likelihood and Bayesian estimation are discussed as well as potential advantages and caveats with the Bayesian approach.

Seniors, health information, and the Internet: motivation, ability, and Internet knowledge.

PubMed

Sheng, Xiaojing; Simpson, Penny M

2013-10-01

Providing health information to older adults is crucial to empowering them to better control their health, and the information is readily available on the Internet. Yet, little is known about the factors that are important in affecting seniors' Internet search for health information behavior. This work addresses this research deficit by examining the role of health information orientation (HIO), eHealth literacy, and Internet knowledge (IK) in affecting the likelihood of using the Internet as a source for health information. The analysis reveals that each variable in the study is significant in affecting Internet search likelihood. Results from the analysis also demonstrate the partial mediating role of eHealth literacy and the interaction between eHealth literacy and HIO. The findings suggest that improving seniors' IK and eHealth literacy would increase their likelihood of searching for and finding health information on the Internet that might encourage better health behaviors.

A Gateway for Phylogenetic Analysis Powered by Grid Computing Featuring GARLI 2.0

PubMed Central

Bazinet, Adam L.; Zwickl, Derrick J.; Cummings, Michael P.

2014-01-01

We introduce molecularevolution.org, a publicly available gateway for high-throughput, maximum-likelihood phylogenetic analysis powered by grid computing. The gateway features a garli 2.0 web service that enables a user to quickly and easily submit thousands of maximum likelihood tree searches or bootstrap searches that are executed in parallel on distributed computing resources. The garli web service allows one to easily specify partitioned substitution models using a graphical interface, and it performs sophisticated post-processing of phylogenetic results. Although the garli web service has been used by the research community for over three years, here we formally announce the availability of the service, describe its capabilities, highlight new features and recent improvements, and provide details about how the grid system efficiently delivers high-quality phylogenetic results. [garli, gateway, grid computing, maximum likelihood, molecular evolution portal, phylogenetics, web service.] PMID:24789072

Noncentral Chi-Square versus Normal Distributions in Describing the Likelihood Ratio Statistic: The Univariate Case and Its Multivariate Implication

ERIC Educational Resources Information Center

Yuan, Ke-Hai

2008-01-01

In the literature of mean and covariance structure analysis, noncentral chi-square distribution is commonly used to describe the behavior of the likelihood ratio (LR) statistic under alternative hypothesis. Due to the inaccessibility of the rather technical literature for the distribution of the LR statistic, it is widely believed that the…

An Improved Nested Sampling Algorithm for Model Selection and Assessment

NASA Astrophysics Data System (ADS)

Zeng, X.; Ye, M.; Wu, J.; WANG, D.

2017-12-01

Multimodel strategy is a general approach for treating model structure uncertainty in recent researches. The unknown groundwater system is represented by several plausible conceptual models. Each alternative conceptual model is attached with a weight which represents the possibility of this model. In Bayesian framework, the posterior model weight is computed as the product of model prior weight and marginal likelihood (or termed as model evidence). As a result, estimating marginal likelihoods is crucial for reliable model selection and assessment in multimodel analysis. Nested sampling estimator (NSE) is a new proposed algorithm for marginal likelihood estimation. The implementation of NSE comprises searching the parameters' space from low likelihood area to high likelihood area gradually, and this evolution is finished iteratively via local sampling procedure. Thus, the efficiency of NSE is dominated by the strength of local sampling procedure. Currently, Metropolis-Hasting (M-H) algorithm and its variants are often used for local sampling in NSE. However, M-H is not an efficient sampling algorithm for high-dimensional or complex likelihood function. For improving the performance of NSE, it could be feasible to integrate more efficient and elaborated sampling algorithm - DREAMzs into the local sampling. In addition, in order to overcome the computation burden problem of large quantity of repeating model executions in marginal likelihood estimation, an adaptive sparse grid stochastic collocation method is used to build the surrogates for original groundwater model.

Separate modal analysis for tumor detection with a digital image elasto tomography (DIET) breast cancer screening system.

PubMed

Kashif, Amer S; Lotz, Thomas F; Heeren, Adrianus M W; Chase, James G

2013-11-01

It is estimated that every year, 1 × 10(6) women are diagnosed with breast cancer, and more than 410,000 die annually worldwide. Digital Image Elasto Tomography (DIET) is a new noninvasive breast cancer screening modality that induces mechanical vibrations in the breast and images its surface motion with digital cameras to detect changes in stiffness. This research develops a new automated approach for diagnosing breast cancer using DIET based on a modal analysis model. The first and second natural frequency of silicone phantom breasts is analyzed. Separate modal analysis is performed for each region of the phantom to estimate the modal parameters using imaged motion data over several input frequencies. Statistical methods are used to assess the likelihood of a frequency shift, which can indicate tumor location. Phantoms with 5, 10, and 20 mm stiff inclusions are tested, as well as a homogeneous (healthy) phantom. Inclusions are located at four locations with different depth. The second natural frequency proves to be a reliable metric with the potential to clearly distinguish lesion like inclusions of different stiffness, as well as providing an approximate location for the tumor like inclusions. The 10 and 20 mm inclusions are always detected regardless of depth. The 5 mm inclusions are only detected near the surface. The homogeneous phantom always yields a negative result, as expected. Detection is based on a statistical likelihood analysis to determine the presence of significantly different frequency response over the phantom, which is a novel approach to this problem. The overall results show promise and justify proof of concept trials with human subjects.

Meta-analysis of studies with bivariate binary outcomes: a marginal beta-binomial model approach.

PubMed

Chen, Yong; Hong, Chuan; Ning, Yang; Su, Xiao

2016-01-15

When conducting a meta-analysis of studies with bivariate binary outcomes, challenges arise when the within-study correlation and between-study heterogeneity should be taken into account. In this paper, we propose a marginal beta-binomial model for the meta-analysis of studies with binary outcomes. This model is based on the composite likelihood approach and has several attractive features compared with the existing models such as bivariate generalized linear mixed model (Chu and Cole, 2006) and Sarmanov beta-binomial model (Chen et al., 2012). The advantages of the proposed marginal model include modeling the probabilities in the original scale, not requiring any transformation of probabilities or any link function, having closed-form expression of likelihood function, and no constraints on the correlation parameter. More importantly, because the marginal beta-binomial model is only based on the marginal distributions, it does not suffer from potential misspecification of the joint distribution of bivariate study-specific probabilities. Such misspecification is difficult to detect and can lead to biased inference using currents methods. We compare the performance of the marginal beta-binomial model with the bivariate generalized linear mixed model and the Sarmanov beta-binomial model by simulation studies. Interestingly, the results show that the marginal beta-binomial model performs better than the Sarmanov beta-binomial model, whether or not the true model is Sarmanov beta-binomial, and the marginal beta-binomial model is more robust than the bivariate generalized linear mixed model under model misspecifications. Two meta-analyses of diagnostic accuracy studies and a meta-analysis of case-control studies are conducted for illustration. Copyright © 2015 John Wiley & Sons, Ltd.

pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree

PubMed Central

2010-01-01

Background Likelihood-based phylogenetic inference is generally considered to be the most reliable classification method for unknown sequences. However, traditional likelihood-based phylogenetic methods cannot be applied to large volumes of short reads from next-generation sequencing due to computational complexity issues and lack of phylogenetic signal. "Phylogenetic placement," where a reference tree is fixed and the unknown query sequences are placed onto the tree via a reference alignment, is a way to bring the inferential power offered by likelihood-based approaches to large data sets. Results This paper introduces pplacer, a software package for phylogenetic placement and subsequent visualization. The algorithm can place twenty thousand short reads on a reference tree of one thousand taxa per hour per processor, has essentially linear time and memory complexity in the number of reference taxa, and is easy to run in parallel. Pplacer features calculation of the posterior probability of a placement on an edge, which is a statistically rigorous way of quantifying uncertainty on an edge-by-edge basis. It also can inform the user of the positional uncertainty for query sequences by calculating expected distance between placement locations, which is crucial in the estimation of uncertainty with a well-sampled reference tree. The software provides visualizations using branch thickness and color to represent number of placements and their uncertainty. A simulation study using reads generated from 631 COG alignments shows a high level of accuracy for phylogenetic placement over a wide range of alignment diversity, and the power of edge uncertainty estimates to measure placement confidence. Conclusions Pplacer enables efficient phylogenetic placement and subsequent visualization, making likelihood-based phylogenetics methodology practical for large collections of reads; it is freely available as source code, binaries, and a web service. PMID:21034504

Evaluating Fast Maximum Likelihood-Based Phylogenetic Programs Using Empirical Phylogenomic Data Sets

PubMed Central

Zhou, Xiaofan; Shen, Xing-Xing; Hittinger, Chris Todd

2018-01-01

Abstract The sizes of the data matrices assembled to resolve branches of the tree of life have increased dramatically, motivating the development of programs for fast, yet accurate, inference. For example, several different fast programs have been developed in the very popular maximum likelihood framework, including RAxML/ExaML, PhyML, IQ-TREE, and FastTree. Although these programs are widely used, a systematic evaluation and comparison of their performance using empirical genome-scale data matrices has so far been lacking. To address this question, we evaluated these four programs on 19 empirical phylogenomic data sets with hundreds to thousands of genes and up to 200 taxa with respect to likelihood maximization, tree topology, and computational speed. For single-gene tree inference, we found that the more exhaustive and slower strategies (ten searches per alignment) outperformed faster strategies (one tree search per alignment) using RAxML, PhyML, or IQ-TREE. Interestingly, single-gene trees inferred by the three programs yielded comparable coalescent-based species tree estimations. For concatenation-based species tree inference, IQ-TREE consistently achieved the best-observed likelihoods for all data sets, and RAxML/ExaML was a close second. In contrast, PhyML often failed to complete concatenation-based analyses, whereas FastTree was the fastest but generated lower likelihood values and more dissimilar tree topologies in both types of analyses. Finally, data matrix properties, such as the number of taxa and the strength of phylogenetic signal, sometimes substantially influenced the programs’ relative performance. Our results provide real-world gene and species tree phylogenetic inference benchmarks to inform the design and execution of large-scale phylogenomic data analyses. PMID:29177474

The diagnostic performance of perfusion MRI for differentiating glioma recurrence from pseudoprogression: A meta-analysis.

PubMed

Wan, Bing; Wang, Siqi; Tu, Mengqi; Wu, Bo; Han, Ping; Xu, Haibo

2017-03-01

The purpose of this meta-analysis was to evaluate the diagnostic accuracy of perfusion magnetic resonance imaging (MRI) as a method for differentiating glioma recurrence from pseudoprogression. The PubMed, Embase, Cochrane Library, and Chinese Biomedical databases were searched comprehensively for relevant studies up to August 3, 2016 according to specific inclusion and exclusion criteria. The quality of the included studies was assessed according to the quality assessment of diagnostic accuracy studies (QUADAS-2). After performing heterogeneity and threshold effect tests, pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated. Publication bias was evaluated visually by a funnel plot and quantitatively using Deek funnel plot asymmetry test. The area under the summary receiver operating characteristic curve was calculated to demonstrate the diagnostic performance of perfusion MRI. Eleven studies covering 416 patients and 418 lesions were included in this meta-analysis. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.88 (95% confidence interval [CI] 0.84-0.92), 0.77 (95% CI 0.69-0.84), 3.93 (95% CI 2.83-5.46), 0.16 (95% CI 0.11-0.22), and 27.17 (95% CI 14.96-49.35), respectively. The area under the summary receiver operating characteristic curve was 0.8899. There was no notable publication bias. Sensitivity analysis showed that the meta-analysis results were stable and credible. While perfusion MRI is not the ideal diagnostic method for differentiating glioma recurrence from pseudoprogression, it could improve diagnostic accuracy. Therefore, further research on combining perfusion MRI with other imaging modalities is warranted.

Empirical likelihood-based confidence intervals for mean medical cost with censored data.

PubMed

Jeyarajah, Jenny; Qin, Gengsheng

2017-11-10

In this paper, we propose empirical likelihood methods based on influence function and jackknife techniques for constructing confidence intervals for mean medical cost with censored data. We conduct a simulation study to compare the coverage probabilities and interval lengths of our proposed confidence intervals with that of the existing normal approximation-based confidence intervals and bootstrap confidence intervals. The proposed methods have better finite-sample performances than existing methods. Finally, we illustrate our proposed methods with a relevant example. Copyright © 2017 John Wiley & Sons, Ltd.

Approximated maximum likelihood estimation in multifractal random walks

NASA Astrophysics Data System (ADS)

Løvsletten, O.; Rypdal, M.

2012-04-01

We present an approximated maximum likelihood method for the multifractal random walk processes of [E. Bacry , Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.64.026103 64, 026103 (2001)]. The likelihood is computed using a Laplace approximation and a truncation in the dependency structure for the latent volatility. The procedure is implemented as a package in the r computer language. Its performance is tested on synthetic data and compared to an inference approach based on the generalized method of moments. The method is applied to estimate parameters for various financial stock indices.

An information-based approach to change-point analysis with applications to biophysics and cell biology.

PubMed

Wiggins, Paul A

2015-07-21

This article describes the application of a change-point algorithm to the analysis of stochastic signals in biological systems whose underlying state dynamics consist of transitions between discrete states. Applications of this analysis include molecular-motor stepping, fluorophore bleaching, electrophysiology, particle and cell tracking, detection of copy number variation by sequencing, tethered-particle motion, etc. We present a unified approach to the analysis of processes whose noise can be modeled by Gaussian, Wiener, or Ornstein-Uhlenbeck processes. To fit the model, we exploit explicit, closed-form algebraic expressions for maximum-likelihood estimators of model parameters and estimated information loss of the generalized noise model, which can be computed extremely efficiently. We implement change-point detection using the frequentist information criterion (which, to our knowledge, is a new information criterion). The frequentist information criterion specifies a single, information-based statistical test that is free from ad hoc parameters and requires no prior probability distribution. We demonstrate this information-based approach in the analysis of simulated and experimental tethered-particle-motion data. Copyright © 2015 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Improving estimates of genetic maps: a meta-analysis-based approach.

PubMed

Stewart, William C L

2007-07-01

Inaccurate genetic (or linkage) maps can reduce the power to detect linkage, increase type I error, and distort haplotype and relationship inference. To improve the accuracy of existing maps, I propose a meta-analysis-based method that combines independent map estimates into a single estimate of the linkage map. The method uses the variance of each independent map estimate to combine them efficiently, whether the map estimates use the same set of markers or not. As compared with a joint analysis of the pooled genotype data, the proposed method is attractive for three reasons: (1) it has comparable efficiency to the maximum likelihood map estimate when the pooled data are homogeneous; (2) relative to existing map estimation methods, it can have increased efficiency when the pooled data are heterogeneous; and (3) it avoids the practical difficulties of pooling human subjects data. On the basis of simulated data modeled after two real data sets, the proposed method can reduce the sampling variation of linkage maps commonly used in whole-genome linkage scans. Furthermore, when the independent map estimates are also maximum likelihood estimates, the proposed method performs as well as or better than when they are estimated by the program CRIMAP. Since variance estimates of maps may not always be available, I demonstrate the feasibility of three different variance estimators. Overall, the method should prove useful to investigators who need map positions for markers not contained in publicly available maps, and to those who wish to minimize the negative effects of inaccurate maps. Copyright 2007 Wiley-Liss, Inc.

Diagnostic accuracy of droplet digital PCR for detection of EGFR T790M mutation in circulating tumor DNA

PubMed Central

Tong, Xiang; Wang, Ye; Wang, Chengdi; Jin, Jing; Tian, Panwen; Li, Weimin

2018-01-01

Objectives Although different methods have been established to detect epidermal growth factor receptor (EGFR) T790M mutation in circulating tumor DNA (ctDNA), a wide range of diagnostic accuracy values were reported in previous studies. The aim of this meta-analysis was to provide pooled diagnostic accuracy measures for droplet digital PCR (ddPCR) in the diagnosis of EGFR T790M mutation based on ctDNA. Materials and methods A systematic review and meta-analysis were carried out based on resources from Pubmed, Web of Science, Embase and Cochrane Library up to October 11, 2017. Data were extracted to assess the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio (NLR), diagnostic OR (DOR), and areas under the summary receiver-operating characteristic curve (SROC). Results Eleven of 311 studies identified have met the including criteria. The sensitivity and specificity of ddPCR for the detection of T790M mutation in ctDNA ranged from 0.0% to 100.0% and 63.2% to 100.0%, respectively. For the pooled analysis, ddPCR had a performance of 70.1% (95% CI, 62.7%–76.7%) sensitivity, 86.9 % (95% CI, 80.6%–91.7%) specificity, 3.67 (95% CI, 2.33–5.79) PLR, 0.41 (95% CI, 0.32–0.55) NLR, and 10.83 (95% CI, 5.86–20.03) DOR, with the area under the SROC curve being 0.82. Conclusion The ddPCR harbored a good performance for detection of EGFR T790M mutation in ctDNA. PMID:29844700

[Determinants of sterilization among married couples in Korea].

PubMed

Kim, Ju Hee; Chung, Woojin; Lee, Sunmi; Suh, Moonhee; Kang, Dae Ryong

2007-11-01

The purpose of this study was to examine the determinants of sterilization in South Korea. This study was based on the data from the Korea National Fertility Survey carried out in the year 2000 by the Korea Institute of Health and Social Affairs. The subjects of the analysis were 4,604 women and their husbands who were in their first marriage, in the age group of 15-49 years. The data were analyzed by multiple logistic regression analysis. Consistent with the findings of previous studies, the woman's age and the number of total children increased the likelihood of sterilization. In addition, the year of marriage had a strong positive association with sterilization. Interestingly, the number of surviving sons tended to increase the likelihood of sterilization, whereas the woman's education level and age at the time of marriage showed a negative association with sterilization. Religion, place of residence, son preference, and the husband's education level, age and type of occupation were not significant determinants of sterilization. The sex of previous children and lower level of education are distinct determinants of sterilization among women in South Korea. More studies are needed in order to determine the associations between sterilization rate and decreased fertility.

Relationships between mastitis and functional longevity in Danish Black and White dairy cattle estimated using survival analysis.

PubMed

Neerhof, H J; Madsen, P; Ducrocq, V P; Vollema, A R; Jensen, J; Korsgaard, I R

2000-05-01

The relationship between mastitis and functional longevity was assessed with survival analysis on data of Danish Black and White dairy cows. Different methods of including the effect of mastitis treatment on the culling decision by a farmer in the model were compared. The model in which mastitis treatment was assumed to have an effect on functional longevity until the end of the lactation had the highest likelihood, and the model in which mastitis treatment had an effect for only a short period had the lowest likelihood. A cow with mastitis had 1.69 times greater risk of being culled than did a healthy herdmate with all other effects being the same. A model without mastitis treatment was used to predict transmitting abilities of bulls for risk of being culled, based on longevity records of their daughters, and was expressed in terms of risk of being culled. The correlation between the risk of being culled and the national evaluations of the bulls for mastitis resistance was approximately -0.4, indicating that resistance against mastitis was genetically correlated with a lower risk of being culled and, thus, a longer functional length of productive life.

Accounting for correlation in network meta-analysis with multi-arm trials.

PubMed

Franchini, A J; Dias, S; Ades, A E; Jansen, J P; Welton, N J

2012-06-01

Multi-arm trials (trials with more than two arms) are particularly valuable forms of evidence for network meta-analysis (NMA). Trial results are available either as arm-level summaries, where effect measures are reported for each arm, or as contrast-level summaries, where the differences in effect between arms compare with the control arm chosen for the trial. We show that likelihood-based inference in both contrast-level and arm-level formats is identical if there are only two-arm trials, but that if there are multi-arm trials, results from the contrast-level format will be incorrect unless correlations are accounted for in the likelihood. We review Bayesian and frequentist software for NMA with multi-arm trials that can account for this correlation and give an illustrative example of the difference in estimates that can be introduced if the correlations are not incorporated. We discuss methods of imputing correlations when they cannot be derived from the reported results and urge trialists to report the standard error for the control arm even if only contrast-level summaries are reported. Copyright © 2012 John Wiley & Sons, Ltd. Copyright © 2012 John Wiley & Sons, Ltd.

Organizational and environmental factors associated with nursing home participation in managed care.

PubMed Central

Zinn, J S; Mor, V; Castle, N; Intrator, O; Brannon, D

1999-01-01

OBJECTIVE: To develop and test a model, based on resource dependence theory, that identifies the organizational and environmental characteristics associated with nursing home participation in managed care. DATA SOURCES AND STUDY SETTING: Data for statistical analysis derived from a survey of Directors of Nursing in a sample of nursing homes in eight states (n = 308). These data were merged with data from the On-line Survey Certification and Reporting System, the Medicare Managed Care State/County Data File, and the 1995 Area Resource File. STUDY DESIGN: Since the dependent variable is dichotomous, the logistic procedure was used to fit the regression. The analysis was weighted using SUDAAN. FINDINGS: Participation in a provider network, higher proportions of resident care covered by Medicare, providing IV therapy, greater availability of RNs and physical therapists, and Medicare HMO market penetration are associated with a greater likelihood of having a managed care contract. CONCLUSION: As more Medicare recipients enroll in HMOs, nursing home involvement in managed care is likely to increase. Interorganizational linkages enhance the likelihood of managed care participation. Nursing homes interested in managed care should consider upgrading staffing and providing at least some subacute services. PMID:10029508

Sick leave and the impact of job-to-job mobility on the likelihood of remaining on the labour market - a longitudinal Swedish register study

PubMed Central

2014-01-01

Background Change of job could be a strategy in vocational rehabilitation when return to the original job is not possible, but research is very limited concerning the effects of job mobility on the future vocational situation. The aim of the study was to investigate whether job-to-job mobility affects the likelihood of remaining on the labour market over time among persons who are employed and have experienced long-term sick leave. Methods In a longitudinal register study, cohorts from three base years (1994, 1999 and 2004) were created, based on the Swedish population who were 20–60 years old, had sickness allowance insurance, and were employed in the base year and the following year (n > 3,000,000). The likelihood that individuals on long-term sick leave were employed later depending on whether or not they changed workplace during the present or next year of long-term sick leave was analyzed using logistic regression analysis. Age, sector, industry, children, marital status, education, income, rate of sick leave and earlier sick leave and earlier mobility were taken into consideration. Results Women with more than 180 days’ sick leave who changed workplaces were more likely to have a job later compared with those who did not change jobs. For men, the association was statistically significant with 1994 and 2004 as base years, but not in the cohort from 1999. Conclusions The present study indicates that for those on long-term sick leave that changed workplaces, the opportunities to stay on the labour market might increase. However, the study has methodological limitations and the results for men are ambiguous. We do not therefore have enough evidence for recommending job change as a strategy for vocational rehabilitation. PMID:24694029

Competing Forces of Socioeconomic Development and Environmental Degradation on Health and Happiness for Different Income Groups in China.

PubMed

Gu, Lijuan; Rosenberg, Mark W; Zeng, Juxin

2017-10-01

China's rapid socioeconomic growth in recent years and the simultaneous increase in many forms of pollution are generating contradictory pictures of residents' well-being. This paper applies multilevel analysis to the 2013 China General Social Survey data on social development and health to understand this twofold phenomenon. Multilevel models are developed to investigate the impact of socioeconomic development and environmental degradation on self-reported health (SRH) and self-reported happiness (SRHP), differentiating among lower, middle, and higher income groups. The results of the logit multilevel analysis demonstrate that income, jobs, and education increased the likelihood of rating SRH and SRHP positively for the lower and middle groups but had little or no effect on the higher income group. Having basic health insurance had an insignificant effect on health but increased the likelihood of happiness among the lower income group. Provincial-level pollutants were associated with a higher likelihood of good health for all income groups, and community-level industrial pollutants increased the likelihood of good health for the lower and middle income groups. Measures of community-level pollution were robust predictors of the likelihood of unhappiness among the lower and middle income groups. Environmental hazards had a mediating effect on the relationship between socioeconomic development and health, and socioeconomic development strengthened the association between environmental hazards and happiness. These outcomes indicate that the complex interconnections among socioeconomic development and environmental degradation have differential effects on well-being among different income groups in China.

What influences the choice of assessment methods in health technology assessments? Statistical analysis of international health technology assessments from 1989 to 2002.

PubMed

Draborg, Eva; Andersen, Christian Kronborg

2006-01-01

Health technology assessment (HTA) has been used as input in decision making worldwide for more than 25 years. However, no uniform definition of HTA or agreement on assessment methods exists, leaving open the question of what influences the choice of assessment methods in HTAs. The objective of this study is to analyze statistically a possible relationship between methods of assessment used in practical HTAs, type of assessed technology, type of assessors, and year of publication. A sample of 433 HTAs published by eleven leading institutions or agencies in nine countries was reviewed and analyzed by multiple logistic regression. The study shows that outsourcing of HTA reports to external partners is associated with a higher likelihood of using assessment methods, such as meta-analysis, surveys, economic evaluations, and randomized controlled trials; and with a lower likelihood of using assessment methods, such as literature reviews and "other methods". The year of publication was statistically related to the inclusion of economic evaluations and shows a decreasing likelihood during the year span. The type of assessed technology was related to economic evaluations with a decreasing likelihood, to surveys, and to "other methods" with a decreasing likelihood when pharmaceuticals were the assessed type of technology. During the period from 1989 to 2002, no major developments in assessment methods used in practical HTAs were shown statistically in a sample of 433 HTAs worldwide. Outsourcing to external assessors has a statistically significant influence on choice of assessment methods.

Laser-Based Slam with Efficient Occupancy Likelihood Map Learning for Dynamic Indoor Scenes

NASA Astrophysics Data System (ADS)

Li, Li; Yao, Jian; Xie, Renping; Tu, Jinge; Feng, Chen

2016-06-01

Location-Based Services (LBS) have attracted growing attention in recent years, especially in indoor environments. The fundamental technique of LBS is the map building for unknown environments, this technique also named as simultaneous localization and mapping (SLAM) in robotic society. In this paper, we propose a novel approach for SLAMin dynamic indoor scenes based on a 2D laser scanner mounted on a mobile Unmanned Ground Vehicle (UGV) with the help of the grid-based occupancy likelihood map. Instead of applying scan matching in two adjacent scans, we propose to match current scan with the occupancy likelihood map learned from all previous scans in multiple scales to avoid the accumulation of matching errors. Due to that the acquisition of the points in a scan is sequential but not simultaneous, there unavoidably exists the scan distortion at different extents. To compensate the scan distortion caused by the motion of the UGV, we propose to integrate a velocity of a laser range finder (LRF) into the scan matching optimization framework. Besides, to reduce the effect of dynamic objects such as walking pedestrians often existed in indoor scenes as much as possible, we propose a new occupancy likelihood map learning strategy by increasing or decreasing the probability of each occupancy grid after each scan matching. Experimental results in several challenged indoor scenes demonstrate that our proposed approach is capable of providing high-precision SLAM results.

Model averaging techniques for quantifying conceptual model uncertainty.

PubMed

Singh, Abhishek; Mishra, Srikanta; Ruskauff, Greg

2010-01-01

In recent years a growing understanding has emerged regarding the need to expand the modeling paradigm to include conceptual model uncertainty for groundwater models. Conceptual model uncertainty is typically addressed by formulating alternative model conceptualizations and assessing their relative likelihoods using statistical model averaging approaches. Several model averaging techniques and likelihood measures have been proposed in the recent literature for this purpose with two broad categories--Monte Carlo-based techniques such as Generalized Likelihood Uncertainty Estimation or GLUE (Beven and Binley 1992) and criterion-based techniques that use metrics such as the Bayesian and Kashyap Information Criteria (e.g., the Maximum Likelihood Bayesian Model Averaging or MLBMA approach proposed by Neuman 2003) and Akaike Information Criterion-based model averaging (AICMA) (Poeter and Anderson 2005). These different techniques can often lead to significantly different relative model weights and ranks because of differences in the underlying statistical assumptions about the nature of model uncertainty. This paper provides a comparative assessment of the four model averaging techniques (GLUE, MLBMA with KIC, MLBMA with BIC, and AIC-based model averaging) mentioned above for the purpose of quantifying the impacts of model uncertainty on groundwater model predictions. Pros and cons of each model averaging technique are examined from a practitioner's perspective using two groundwater modeling case studies. Recommendations are provided regarding the use of these techniques in groundwater modeling practice.

Empirical likelihood-based tests for stochastic ordering

PubMed Central

BARMI, HAMMOU EL; MCKEAGUE, IAN W.

2013-01-01

This paper develops an empirical likelihood approach to testing for the presence of stochastic ordering among univariate distributions based on independent random samples from each distribution. The proposed test statistic is formed by integrating a localized empirical likelihood statistic with respect to the empirical distribution of the pooled sample. The asymptotic null distribution of this test statistic is found to have a simple distribution-free representation in terms of standard Brownian bridge processes. The approach is used to compare the lengths of rule of Roman Emperors over various historical periods, including the “decline and fall” phase of the empire. In a simulation study, the power of the proposed test is found to improve substantially upon that of a competing test due to El Barmi and Mukerjee. PMID:23874142

Accurate recapture identification for genetic mark–recapture studies with error-tolerant likelihood-based match calling and sample clustering

USGS Publications Warehouse

Sethi, Suresh; Linden, Daniel; Wenburg, John; Lewis, Cara; Lemons, Patrick R.; Fuller, Angela K.; Hare, Matthew P.

2016-01-01

Error-tolerant likelihood-based match calling presents a promising technique to accurately identify recapture events in genetic mark–recapture studies by combining probabilities of latent genotypes and probabilities of observed genotypes, which may contain genotyping errors. Combined with clustering algorithms to group samples into sets of recaptures based upon pairwise match calls, these tools can be used to reconstruct accurate capture histories for mark–recapture modelling. Here, we assess the performance of a recently introduced error-tolerant likelihood-based match-calling model and sample clustering algorithm for genetic mark–recapture studies. We assessed both biallelic (i.e. single nucleotide polymorphisms; SNP) and multiallelic (i.e. microsatellite; MSAT) markers using a combination of simulation analyses and case study data on Pacific walrus (Odobenus rosmarus divergens) and fishers (Pekania pennanti). A novel two-stage clustering approach is demonstrated for genetic mark–recapture applications. First, repeat captures within a sampling occasion are identified. Subsequently, recaptures across sampling occasions are identified. The likelihood-based matching protocol performed well in simulation trials, demonstrating utility for use in a wide range of genetic mark–recapture studies. Moderately sized SNP (64+) and MSAT (10–15) panels produced accurate match calls for recaptures and accurate non-match calls for samples from closely related individuals in the face of low to moderate genotyping error. Furthermore, matching performance remained stable or increased as the number of genetic markers increased, genotyping error notwithstanding.

Software for Statistical Analysis of Weibull Distributions with Application to Gear Fatigue Data: User Manual with Verification

NASA Technical Reports Server (NTRS)

Krantz, Timothy L.

2002-01-01

The Weibull distribution has been widely adopted for the statistical description and inference of fatigue data. This document provides user instructions, examples, and verification for software to analyze gear fatigue test data. The software was developed presuming the data are adequately modeled using a two-parameter Weibull distribution. The calculations are based on likelihood methods, and the approach taken is valid for data that include type 1 censoring. The software was verified by reproducing results published by others.

The l z ( p ) * Person-Fit Statistic in an Unfolding Model Context.

PubMed

Tendeiro, Jorge N

2017-01-01

Although person-fit analysis has a long-standing tradition within item response theory, it has been applied in combination with dominance response models almost exclusively. In this article, a popular log likelihood-based parametric person-fit statistic under the framework of the generalized graded unfolding model is used. Results from a simulation study indicate that the person-fit statistic performed relatively well in detecting midpoint response style patterns and not so well in detecting extreme response style patterns.

Software for Statistical Analysis of Weibull Distributions with Application to Gear Fatigue Data: User Manual with Verification

NASA Technical Reports Server (NTRS)

Kranz, Timothy L.

2002-01-01

The Weibull distribution has been widely adopted for the statistical description and inference of fatigue data. This document provides user instructions, examples, and verification for software to analyze gear fatigue test data. The software was developed presuming the data are adequately modeled using a two-parameter Weibull distribution. The calculations are based on likelihood methods, and the approach taken is valid for data that include type I censoring. The software was verified by reproducing results published by others.

Testing students' e-learning via Facebook through Bayesian structural equation modeling.

PubMed

Salarzadeh Jenatabadi, Hashem; Moghavvemi, Sedigheh; Wan Mohamed Radzi, Che Wan Jasimah Bt; Babashamsi, Parastoo; Arashi, Mohammad

2017-01-01

Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Evidence of seasonal variation in longitudinal growth of height in a sample of boys from Stuttgart Carlsschule, 1771-1793, using combined principal component analysis and maximum likelihood principle.

PubMed

Lehmann, A; Scheffler, Ch; Hermanussen, M

2010-02-01

Recent progress in modelling individual growth has been achieved by combining the principal component analysis and the maximum likelihood principle. This combination models growth even in incomplete sets of data and in data obtained at irregular intervals. We re-analysed late 18th century longitudinal growth of German boys from the boarding school Carlsschule in Stuttgart. The boys, aged 6-23 years, were measured at irregular 3-12 monthly intervals during the period 1771-1793. At the age of 18 years, mean height was 1652 mm, but height variation was large. The shortest boy reached 1474 mm, the tallest 1826 mm. Measured height closely paralleled modelled height, with mean difference of 4 mm, SD 7 mm. Seasonal height variation was found. Low growth rates occurred in spring and high growth rates in summer and autumn. The present study demonstrates that combining the principal component analysis and the maximum likelihood principle enables growth modelling in historic height data also. Copyright (c) 2009 Elsevier GmbH. All rights reserved.

Testing students’ e-learning via Facebook through Bayesian structural equation modeling

PubMed Central

Moghavvemi, Sedigheh; Wan Mohamed Radzi, Che Wan Jasimah Bt; Babashamsi, Parastoo; Arashi, Mohammad

2017-01-01

Learning is an intentional activity, with several factors affecting students’ intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods’ results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated. PMID:28886019

Power and Sample Size Calculations for Logistic Regression Tests for Differential Item Functioning

ERIC Educational Resources Information Center

Li, Zhushan

2014-01-01

Logistic regression is a popular method for detecting uniform and nonuniform differential item functioning (DIF) effects. Theoretical formulas for the power and sample size calculations are derived for likelihood ratio tests and Wald tests based on the asymptotic distribution of the maximum likelihood estimators for the logistic regression model.…

Expected versus Observed Information in SEM with Incomplete Normal and Nonnormal Data

ERIC Educational Resources Information Center

Savalei, Victoria

2010-01-01

Maximum likelihood is the most common estimation method in structural equation modeling. Standard errors for maximum likelihood estimates are obtained from the associated information matrix, which can be estimated from the sample using either expected or observed information. It is known that, with complete data, estimates based on observed or…

Bias and Efficiency in Structural Equation Modeling: Maximum Likelihood versus Robust Methods

ERIC Educational Resources Information Center

Zhong, Xiaoling; Yuan, Ke-Hai

2011-01-01

In the structural equation modeling literature, the normal-distribution-based maximum likelihood (ML) method is most widely used, partly because the resulting estimator is claimed to be asymptotically unbiased and most efficient. However, this may not hold when data deviate from normal distribution. Outlying cases or nonnormally distributed data,…

Five Methods for Estimating Angoff Cut Scores with IRT

ERIC Educational Resources Information Center

Wyse, Adam E.

2017-01-01

This article illustrates five different methods for estimating Angoff cut scores using item response theory (IRT) models. These include maximum likelihood (ML), expected a priori (EAP), modal a priori (MAP), and weighted maximum likelihood (WML) estimators, as well as the most commonly used approach based on translating ratings through the test…

Not Even if You Were the Last Person on Earth! How Marital Search Constraints Affect the Likelihood of Marriage

ERIC Educational Resources Information Center

Raley, R. Kelly; Bratter, Jenifer

2004-01-01

Using the 1987-1988 and 1992-1994 waves of the National Survey of Families and Households, the authors measure the association between Wave 1 responses to 12 questions on whom respondents would be "most willing to marry" and the likelihood of marriage by Wave 2. Preliminary analysis indicated that some questions about partner preferences…

Uncertainty analysis of signal deconvolution using a measured instrument response function

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

Hartouni, E. P.; Beeman, B.; Caggiano, J. A.

2016-10-05

A common analysis procedure minimizes the ln-likelihood that a set of experimental observables matches a parameterized model of the observation. The model includes a description of the underlying physical process as well as the instrument response function (IRF). Here, we investigate the National Ignition Facility (NIF) neutron time-of-flight (nTOF) spectrometers, the IRF is constructed from measurements and models. IRF measurements have a finite precision that can make significant contributions to the uncertainty estimate of the physical model’s parameters. Finally, we apply a Bayesian analysis to properly account for IRF uncertainties in calculating the ln-likelihood function used to find the optimummore » physical parameters.« less

Learning Time-Varying Coverage Functions

PubMed Central

Du, Nan; Liang, Yingyu; Balcan, Maria-Florina; Song, Le

2015-01-01

Coverage functions are an important class of discrete functions that capture the law of diminishing returns arising naturally from applications in social network analysis, machine learning, and algorithmic game theory. In this paper, we propose a new problem of learning time-varying coverage functions, and develop a novel parametrization of these functions using random features. Based on the connection between time-varying coverage functions and counting processes, we also propose an efficient parameter learning algorithm based on likelihood maximization, and provide a sample complexity analysis. We applied our algorithm to the influence function estimation problem in information diffusion in social networks, and show that with few assumptions about the diffusion processes, our algorithm is able to estimate influence significantly more accurately than existing approaches on both synthetic and real world data. PMID:25960624

Learning Time-Varying Coverage Functions.

PubMed

Du, Nan; Liang, Yingyu; Balcan, Maria-Florina; Song, Le

2014-12-08

Coverage functions are an important class of discrete functions that capture the law of diminishing returns arising naturally from applications in social network analysis, machine learning, and algorithmic game theory. In this paper, we propose a new problem of learning time-varying coverage functions, and develop a novel parametrization of these functions using random features. Based on the connection between time-varying coverage functions and counting processes, we also propose an efficient parameter learning algorithm based on likelihood maximization, and provide a sample complexity analysis. We applied our algorithm to the influence function estimation problem in information diffusion in social networks, and show that with few assumptions about the diffusion processes, our algorithm is able to estimate influence significantly more accurately than existing approaches on both synthetic and real world data.

Intimate partner violence among women in Spain: the impact of regional-level male unemployment and income inequality.

PubMed

Sanz-Barbero, Belén; Vives-Cases, Carmen; Otero-García, Laura; Muntaner, Carles; Torrubiano-Domínguez, Jordi; O'Campo, Patricia

2015-12-01

Intimate partner violence (IPV) against women is a complex worldwide public health problem. There is scarce research on the independent effect on IPV exerted by structural factors such as labour and economic policies, economic inequalities and gender inequality. To analyse the association, in Spain, between contextual variables of regional unemployment and income inequality and individual women's likelihood of IPV, independently of the women's characteristics. We conducted multilevel logistic regression to analyse cross-sectional data from the 2011 Spanish Macrosurvey of Gender-based Violence which included 7898 adult women. The first level of analyses was the individual women' characteristics and the second level was the region of residence. Of the survey participants, 12.2% reported lifetime IPV. The region of residence accounted for 3.5% of the total variability in IPV prevalence. We determined a direct association between regional male long-term unemployment and IPV likelihood (P = 0.007) and between the Gini Index for the regional income inequality and IPV likelihood (P < 0.001). Women residing in a region with higher gender-based income discrimination are at a lower likelihood of IPV than those residing in a region with low gender-based income discrimination (odds ratio = 0.64, 95% confidence intervals: 0.55-0.75). Growing regional unemployment rates and income inequalities increase women's likelihood of IPV. In times of economic downturn, like the current one in Spain, this association may translate into an increase in women's vulnerability to IPV. © The Author 2015. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.

Statistical inference of static analysis rules

NASA Technical Reports Server (NTRS)

Engler, Dawson Richards (Inventor)

2009-01-01

Various apparatus and methods are disclosed for identifying errors in program code. Respective numbers of observances of at least one correctness rule by different code instances that relate to the at least one correctness rule are counted in the program code. Each code instance has an associated counted number of observances of the correctness rule by the code instance. Also counted are respective numbers of violations of the correctness rule by different code instances that relate to the correctness rule. Each code instance has an associated counted number of violations of the correctness rule by the code instance. A respective likelihood of the validity is determined for each code instance as a function of the counted number of observances and counted number of violations. The likelihood of validity indicates a relative likelihood that a related code instance is required to observe the correctness rule. The violations may be output in order of the likelihood of validity of a violated correctness rule.

dPIRPLE: a joint estimation framework for deformable registration and penalized-likelihood CT image reconstruction using prior images

NASA Astrophysics Data System (ADS)

Dang, H.; Wang, A. S.; Sussman, Marc S.; Siewerdsen, J. H.; Stayman, J. W.

2014-09-01

Sequential imaging studies are conducted in many clinical scenarios. Prior images from previous studies contain a great deal of patient-specific anatomical information and can be used in conjunction with subsequent imaging acquisitions to maintain image quality while enabling radiation dose reduction (e.g., through sparse angular sampling, reduction in fluence, etc). However, patient motion between images in such sequences results in misregistration between the prior image and current anatomy. Existing prior-image-based approaches often include only a simple rigid registration step that can be insufficient for capturing complex anatomical motion, introducing detrimental effects in subsequent image reconstruction. In this work, we propose a joint framework that estimates the 3D deformation between an unregistered prior image and the current anatomy (based on a subsequent data acquisition) and reconstructs the current anatomical image using a model-based reconstruction approach that includes regularization based on the deformed prior image. This framework is referred to as deformable prior image registration, penalized-likelihood estimation (dPIRPLE). Central to this framework is the inclusion of a 3D B-spline-based free-form-deformation model into the joint registration-reconstruction objective function. The proposed framework is solved using a maximization strategy whereby alternating updates to the registration parameters and image estimates are applied allowing for improvements in both the registration and reconstruction throughout the optimization process. Cadaver experiments were conducted on a cone-beam CT testbench emulating a lung nodule surveillance scenario. Superior reconstruction accuracy and image quality were demonstrated using the dPIRPLE algorithm as compared to more traditional reconstruction methods including filtered backprojection, penalized-likelihood estimation (PLE), prior image penalized-likelihood estimation (PIPLE) without registration, and prior image penalized-likelihood estimation with rigid registration of a prior image (PIRPLE) over a wide range of sampling sparsity and exposure levels.

Reliability Analysis of Systems Subject to First-Passage Failure

NASA Technical Reports Server (NTRS)

Lutes, Loren D.; Sarkani, Shahram

2009-01-01

An obvious goal of reliability analysis is the avoidance of system failure. However, it is generally recognized that it is often not feasible to design a practical or useful system for which failure is impossible. Thus it is necessary to use techniques that estimate the likelihood of failure based on modeling the uncertainty about such items as the demands on and capacities of various elements in the system. This usually involves the use of probability theory, and a design is considered acceptable if it has a sufficiently small probability of failure. This report contains findings of analyses of systems subject to first-passage failure.

A business model analysis of telecardiology service.

PubMed

Lin, Shu-Hsia; Liu, Jorn-Hon; Wei, Jen; Yin, Wei-Hsian; Chen, Hung-Hsin; Chiu, Wen-Ta

2010-12-01

Telecare has become an increasingly common medical service in recent years. However, new service must be close to the market and be market-driven to have a high likelihood of success. This article analyzes the business model of a telecardiology service managed by a general hospital. The methodology of the article is as follows: (1) initially it describes the elements of the service based on the ontology of the business model, (2) then it transfers these elements into the choices for business model dynamic loops and examines their validity, and (3) finally provides an empirical financial analysis of the service to assess the profit-making possibilities.

Likelihoods for fixed rank nomination networks

PubMed Central

HOFF, PETER; FOSDICK, BAILEY; VOLFOVSKY, ALEX; STOVEL, KATHERINE

2014-01-01

Many studies that gather social network data use survey methods that lead to censored, missing, or otherwise incomplete information. For example, the popular fixed rank nomination (FRN) scheme, often used in studies of schools and businesses, asks study participants to nominate and rank at most a small number of contacts or friends, leaving the existence of other relations uncertain. However, most statistical models are formulated in terms of completely observed binary networks. Statistical analyses of FRN data with such models ignore the censored and ranked nature of the data and could potentially result in misleading statistical inference. To investigate this possibility, we compare Bayesian parameter estimates obtained from a likelihood for complete binary networks with those obtained from likelihoods that are derived from the FRN scheme, and therefore accommodate the ranked and censored nature of the data. We show analytically and via simulation that the binary likelihood can provide misleading inference, particularly for certain model parameters that relate network ties to characteristics of individuals and pairs of individuals. We also compare these different likelihoods in a data analysis of several adolescent social networks. For some of these networks, the parameter estimates from the binary and FRN likelihoods lead to different conclusions, indicating the importance of analyzing FRN data with a method that accounts for the FRN survey design. PMID:25110586

Using a probabilistic approach in an ecological risk assessment simulation tool: test case for depleted uranium (DU).

PubMed

Fan, Ming; Thongsri, Tepwitoon; Axe, Lisa; Tyson, Trevor A

2005-06-01

A probabilistic approach was applied in an ecological risk assessment (ERA) to characterize risk and address uncertainty employing Monte Carlo simulations for assessing parameter and risk probabilistic distributions. This simulation tool (ERA) includes a Window's based interface, an interactive and modifiable database management system (DBMS) that addresses a food web at trophic levels, and a comprehensive evaluation of exposure pathways. To illustrate this model, ecological risks from depleted uranium (DU) exposure at the US Army Yuma Proving Ground (YPG) and Aberdeen Proving Ground (APG) were assessed and characterized. Probabilistic distributions showed that at YPG, a reduction in plant root weight is considered likely to occur (98% likelihood) from exposure to DU; for most terrestrial animals, likelihood for adverse reproduction effects ranges from 0.1% to 44%. However, for the lesser long-nosed bat, the effects are expected to occur (>99% likelihood) through the reduction in size and weight of offspring. Based on available DU data for the firing range at APG, DU uptake will not likely affect survival of aquatic plants and animals (<0.1% likelihood). Based on field and laboratory studies conducted at APG and YPG on pocket mice, kangaroo rat, white-throated woodrat, deer, and milfoil, body burden concentrations observed fall into the distributions simulated at both sites.

Likelihood-based methods for evaluating principal surrogacy in augmented vaccine trials.

PubMed

Liu, Wei; Zhang, Bo; Zhang, Hui; Zhang, Zhiwei

2017-04-01

There is growing interest in assessing immune biomarkers, which are quick to measure and potentially predictive of long-term efficacy, as surrogate endpoints in randomized, placebo-controlled vaccine trials. This can be done under a principal stratification approach, with principal strata defined using a subject's potential immune responses to vaccine and placebo (the latter may be assumed to be zero). In this context, principal surrogacy refers to the extent to which vaccine efficacy varies across principal strata. Because a placebo recipient's potential immune response to vaccine is unobserved in a standard vaccine trial, augmented vaccine trials have been proposed to produce the information needed to evaluate principal surrogacy. This article reviews existing methods based on an estimated likelihood and a pseudo-score (PS) and proposes two new methods based on a semiparametric likelihood (SL) and a pseudo-likelihood (PL), for analyzing augmented vaccine trials. Unlike the PS method, the SL method does not require a model for missingness, which can be advantageous when immune response data are missing by happenstance. The SL method is shown to be asymptotically efficient, and it performs similarly to the PS and PL methods in simulation experiments. The PL method appears to have a computational advantage over the PS and SL methods.

Influence analysis in quantitative trait loci detection.

PubMed

Dou, Xiaoling; Kuriki, Satoshi; Maeno, Akiteru; Takada, Toyoyuki; Shiroishi, Toshihiko

2014-07-01

This paper presents systematic methods for the detection of influential individuals that affect the log odds (LOD) score curve. We derive general formulas of influence functions for profile likelihoods and introduce them into two standard quantitative trait locus detection methods-the interval mapping method and single marker analysis. Besides influence analysis on specific LOD scores, we also develop influence analysis methods on the shape of the LOD score curves. A simulation-based method is proposed to assess the significance of the influence of the individuals. These methods are shown useful in the influence analysis of a real dataset of an experimental population from an F2 mouse cross. By receiver operating characteristic analysis, we confirm that the proposed methods show better performance than existing diagnostics. © 2014 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

An iterative procedure for obtaining maximum-likelihood estimates of the parameters for a mixture of normal distributions

NASA Technical Reports Server (NTRS)

Peters, B. C., Jr.; Walker, H. F.

1978-01-01

This paper addresses the problem of obtaining numerically maximum-likelihood estimates of the parameters for a mixture of normal distributions. In recent literature, a certain successive-approximations procedure, based on the likelihood equations, was shown empirically to be effective in numerically approximating such maximum-likelihood estimates; however, the reliability of this procedure was not established theoretically. Here, we introduce a general iterative procedure, of the generalized steepest-ascent (deflected-gradient) type, which is just the procedure known in the literature when the step-size is taken to be 1. We show that, with probability 1 as the sample size grows large, this procedure converges locally to the strongly consistent maximum-likelihood estimate whenever the step-size lies between 0 and 2. We also show that the step-size which yields optimal local convergence rates for large samples is determined in a sense by the 'separation' of the component normal densities and is bounded below by a number between 1 and 2.

An iterative procedure for obtaining maximum-likelihood estimates of the parameters for a mixture of normal distributions, 2

NASA Technical Reports Server (NTRS)

Peters, B. C., Jr.; Walker, H. F.

1976-01-01

The problem of obtaining numerically maximum likelihood estimates of the parameters for a mixture of normal distributions is addressed. In recent literature, a certain successive approximations procedure, based on the likelihood equations, is shown empirically to be effective in numerically approximating such maximum-likelihood estimates; however, the reliability of this procedure was not established theoretically. Here, a general iterative procedure is introduced, of the generalized steepest-ascent (deflected-gradient) type, which is just the procedure known in the literature when the step-size is taken to be 1. With probability 1 as the sample size grows large, it is shown that this procedure converges locally to the strongly consistent maximum-likelihood estimate whenever the step-size lies between 0 and 2. The step-size which yields optimal local convergence rates for large samples is determined in a sense by the separation of the component normal densities and is bounded below by a number between 1 and 2.

Algorithms of maximum likelihood data clustering with applications

NASA Astrophysics Data System (ADS)

Giada, Lorenzo; Marsili, Matteo

2002-12-01

We address the problem of data clustering by introducing an unsupervised, parameter-free approach based on maximum likelihood principle. Starting from the observation that data sets belonging to the same cluster share a common information, we construct an expression for the likelihood of any possible cluster structure. The likelihood in turn depends only on the Pearson's coefficient of the data. We discuss clustering algorithms that provide a fast and reliable approximation to maximum likelihood configurations. Compared to standard clustering methods, our approach has the advantages that (i) it is parameter free, (ii) the number of clusters need not be fixed in advance and (iii) the interpretation of the results is transparent. In order to test our approach and compare it with standard clustering algorithms, we analyze two very different data sets: time series of financial market returns and gene expression data. We find that different maximization algorithms produce similar cluster structures whereas the outcome of standard algorithms has a much wider variability.

A Solution to Separation and Multicollinearity in Multiple Logistic Regression

PubMed Central

Shen, Jianzhao; Gao, Sujuan

2010-01-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27–38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth’s penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study. PMID:20376286

A Solution to Separation and Multicollinearity in Multiple Logistic Regression.

PubMed

Shen, Jianzhao; Gao, Sujuan

2008-10-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.

Use of empirical likelihood to calibrate auxiliary information in partly linear monotone regression models.

PubMed

Chen, Baojiang; Qin, Jing

2014-05-10

In statistical analysis, a regression model is needed if one is interested in finding the relationship between a response variable and covariates. When the response depends on the covariate, then it may also depend on the function of this covariate. If one has no knowledge of this functional form but expect for monotonic increasing or decreasing, then the isotonic regression model is preferable. Estimation of parameters for isotonic regression models is based on the pool-adjacent-violators algorithm (PAVA), where the monotonicity constraints are built in. With missing data, people often employ the augmented estimating method to improve estimation efficiency by incorporating auxiliary information through a working regression model. However, under the framework of the isotonic regression model, the PAVA does not work as the monotonicity constraints are violated. In this paper, we develop an empirical likelihood-based method for isotonic regression model to incorporate the auxiliary information. Because the monotonicity constraints still hold, the PAVA can be used for parameter estimation. Simulation studies demonstrate that the proposed method can yield more efficient estimates, and in some situations, the efficiency improvement is substantial. We apply this method to a dementia study. Copyright © 2013 John Wiley & Sons, Ltd.

Parent-Child Communication and Marijuana Initiation: Evidence Using Discrete-Time Survival Analysis

PubMed Central

Nonnemaker, James M.; Silber-Ashley, Olivia; Farrelly, Matthew C.; Dench, Daniel

2012-01-01

This study supplements existing literature on the relationship between parent-child communication and adolescent drug use by exploring whether parental and/or adolescent recall of specific drug-related conversations differentially impact youth's likelihood of initiating marijuana use. Using discrete-time survival analysis, we estimated the hazard of marijuana initiation using a logit model to obtain an estimate of the relative risk of initiation. Our results suggest that parent-child communication about drug use is either not protective (no effect) or—in the case of youth reports of communication—potentially harmful (leading to increased likelihood of marijuana initiation). PMID:22958867

The likelihood ratio as a random variable for linked markers in kinship analysis.

PubMed

Egeland, Thore; Slooten, Klaas

2016-11-01

The likelihood ratio is the fundamental quantity that summarizes the evidence in forensic cases. Therefore, it is important to understand the theoretical properties of this statistic. This paper is the last in a series of three, and the first to study linked markers. We show that for all non-inbred pairwise kinship comparisons, the expected likelihood ratio in favor of a type of relatedness depends on the allele frequencies only via the number of alleles, also for linked markers, and also if the true relationship is another one than is tested for by the likelihood ratio. Exact expressions for the expectation and variance are derived for all these cases. Furthermore, we show that the expected likelihood ratio is a non-increasing function if the recombination rate increases between 0 and 0.5 when the actual relationship is the one investigated by the LR. Besides being of theoretical interest, exact expressions such as obtained here can be used for software validation as they allow to verify the correctness up to arbitrary precision. The paper also presents results and advice of practical importance. For example, we argue that the logarithm of the likelihood ratio behaves in a fundamentally different way than the likelihood ratio itself in terms of expectation and variance, in agreement with its interpretation as weight of evidence. Equipped with the results presented and freely available software, one may check calculations and software and also do power calculations.

Quantifying uncertainties in streamflow predictions through signature based inference of hydrological model parameters

NASA Astrophysics Data System (ADS)

Fenicia, Fabrizio; Reichert, Peter; Kavetski, Dmitri; Albert, Calro

2016-04-01

The calibration of hydrological models based on signatures (e.g. Flow Duration Curves - FDCs) is often advocated as an alternative to model calibration based on the full time series of system responses (e.g. hydrographs). Signature based calibration is motivated by various arguments. From a conceptual perspective, calibration on signatures is a way to filter out errors that are difficult to represent when calibrating on the full time series. Such errors may for example occur when observed and simulated hydrographs are shifted, either on the "time" axis (i.e. left or right), or on the "streamflow" axis (i.e. above or below). These shifts may be due to errors in the precipitation input (time or amount), and if not properly accounted in the likelihood function, may cause biased parameter estimates (e.g. estimated model parameters that do not reproduce the recession characteristics of a hydrograph). From a practical perspective, signature based calibration is seen as a possible solution for making predictions in ungauged basins. Where streamflow data are not available, it may in fact be possible to reliably estimate streamflow signatures. Previous research has for example shown how FDCs can be reliably estimated at ungauged locations based on climatic and physiographic influence factors. Typically, the goal of signature based calibration is not the prediction of the signatures themselves, but the prediction of the system responses. Ideally, the prediction of system responses should be accompanied by a reliable quantification of the associated uncertainties. Previous approaches for signature based calibration, however, do not allow reliable estimates of streamflow predictive distributions. Here, we illustrate how the Bayesian approach can be employed to obtain reliable streamflow predictive distributions based on signatures. A case study is presented, where a hydrological model is calibrated on FDCs and additional signatures. We propose an approach where the likelihood function for the signatures is derived from the likelihood for streamflow (rather than using an "ad-hoc" likelihood for the signatures as done in previous approaches). This likelihood is not easily tractable analytically and we therefore cannot apply "simple" MCMC methods. This numerical problem is solved using Approximate Bayesian Computation (ABC). Our result indicate that the proposed approach is suitable for producing reliable streamflow predictive distributions based on calibration to signature data. Moreover, our results provide indications on which signatures are more appropriate to represent the information content of the hydrograph.

A Novel Method for Block Size Forensics Based on Morphological Operations

NASA Astrophysics Data System (ADS)

Luo, Weiqi; Huang, Jiwu; Qiu, Guoping

Passive forensics analysis aims to find out how multimedia data is acquired and processed without relying on pre-embedded or pre-registered information. Since most existing compression schemes for digital images are based on block processing, one of the fundamental steps for subsequent forensics analysis is to detect the presence of block artifacts and estimate the block size for a given image. In this paper, we propose a novel method for blind block size estimation. A 2×2 cross-differential filter is first applied to detect all possible block artifact boundaries, morphological operations are then used to remove the boundary effects caused by the edges of the actual image contents, and finally maximum-likelihood estimation (MLE) is employed to estimate the block size. The experimental results evaluated on over 1300 nature images show the effectiveness of our proposed method. Compared with existing gradient-based detection method, our method achieves over 39% accuracy improvement on average.

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE)

PubMed Central

Boker, Steven M.; Brick, Timothy R.; Pritikin, Joshua N.; Wang, Yang; von Oertzen, Timo; Brown, Donald; Lach, John; Estabrook, Ryne; Hunter, Michael D.; Maes, Hermine H.; Neale, Michael C.

2015-01-01

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE) is a novel paradigm for research in the behavioral, social, and health sciences. The MIDDLE approach is based on the seemingly-impossible idea that data can be privately maintained by participants and never revealed to researchers, while still enabling statistical models to be fit and scientific hypotheses tested. MIDDLE rests on the assumption that participant data should belong to, be controlled by, and remain in the possession of the participants themselves. Distributed likelihood estimation refers to fitting statistical models by sending an objective function and vector of parameters to each participants’ personal device (e.g., smartphone, tablet, computer), where the likelihood of that individual’s data is calculated locally. Only the likelihood value is returned to the central optimizer. The optimizer aggregates likelihood values from responding participants and chooses new vectors of parameters until the model converges. A MIDDLE study provides significantly greater privacy for participants, automatic management of opt-in and opt-out consent, lower cost for the researcher and funding institute, and faster determination of results. Furthermore, if a participant opts into several studies simultaneously and opts into data sharing, these studies automatically have access to individual-level longitudinal data linked across all studies. PMID:26717128

Computational tools for exact conditional logistic regression.

PubMed

Corcoran, C; Mehta, C; Patel, N; Senchaudhuri, P

Logistic regression analyses are often challenged by the inability of unconditional likelihood-based approximations to yield consistent, valid estimates and p-values for model parameters. This can be due to sparseness or separability in the data. Conditional logistic regression, though useful in such situations, can also be computationally unfeasible when the sample size or number of explanatory covariates is large. We review recent developments that allow efficient approximate conditional inference, including Monte Carlo sampling and saddlepoint approximations. We demonstrate through real examples that these methods enable the analysis of significantly larger and more complex data sets. We find in this investigation that for these moderately large data sets Monte Carlo seems a better alternative, as it provides unbiased estimates of the exact results and can be executed in less CPU time than can the single saddlepoint approximation. Moreover, the double saddlepoint approximation, while computationally the easiest to obtain, offers little practical advantage. It produces unreliable results and cannot be computed when a maximum likelihood solution does not exist. Copyright 2001 John Wiley & Sons, Ltd.

A computational framework to characterize and compare the geometry of coronary networks.

PubMed

Bulant, C A; Blanco, P J; Lima, T P; Assunção, A N; Liberato, G; Parga, J R; Ávila, L F R; Pereira, A C; Feijóo, R A; Lemos, P A

2017-03-01

This work presents a computational framework to perform a systematic and comprehensive assessment of the morphometry of coronary arteries from in vivo medical images. The methodology embraces image segmentation, arterial vessel representation, characterization and comparison, data storage, and finally analysis. Validation is performed using a sample of 48 patients. Data mining of morphometric information of several coronary arteries is presented. Results agree to medical reports in terms of basic geometric and anatomical variables. Concerning geometric descriptors, inter-artery and intra-artery correlations are studied. Data reported here can be useful for the construction and setup of blood flow models of the coronary circulation. Finally, as an application example, similarity criterion to assess vasculature likelihood based on geometric features is presented and used to test geometric similarity among sibling patients. Results indicate that likelihood, measured through geometric descriptors, is stronger between siblings compared with non-relative patients. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Nested case-control studies: should one break the matching?

PubMed

Borgan, Ørnulf; Keogh, Ruth

2015-10-01

In a nested case-control study, controls are selected for each case from the individuals who are at risk at the time at which the case occurs. We say that the controls are matched on study time. To adjust for possible confounding, it is common to match on other variables as well. The standard analysis of nested case-control data is based on a partial likelihood which compares the covariates of each case to those of its matched controls. It has been suggested that one may break the matching of nested case-control data and analyse them as case-cohort data using an inverse probability weighted (IPW) pseudo likelihood. Further, when some covariates are available for all individuals in the cohort, multiple imputation (MI) makes it possible to use all available data in the cohort. In the paper we review the standard method and the IPW and MI approaches, and compare their performance using simulations that cover a range of scenarios, including one and two endpoints.

The Use of Dynamic Stochastic Social Behavior Models to Produce Likelihood Functions for Risk Modeling of Proliferation and Terrorist Attacks

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

Young, Jonathan; Thompson, Sandra E.; Brothers, Alan J.

The ability to estimate the likelihood of future events based on current and historical data is essential to the decision making process of many government agencies. Successful predictions related to terror events and characterizing the risks will support development of options for countering these events. The predictive tasks involve both technical and social component models. The social components have presented a particularly difficult challenge. This paper outlines some technical considerations of this modeling activity. Both data and predictions associated with the technical and social models will likely be known with differing certainties or accuracies – a critical challenge is linkingmore » across these model domains while respecting this fundamental difference in certainty level. This paper will describe the technical approach being taken to develop the social model and identification of the significant interfaces between the technical and social modeling in the context of analysis of diversion of nuclear material.« less

Variable selection in semiparametric cure models based on penalized likelihood, with application to breast cancer clinical trials.

PubMed

Liu, Xiang; Peng, Yingwei; Tu, Dongsheng; Liang, Hua

2012-10-30

Survival data with a sizable cure fraction are commonly encountered in cancer research. The semiparametric proportional hazards cure model has been recently used to analyze such data. As seen in the analysis of data from a breast cancer study, a variable selection approach is needed to identify important factors in predicting the cure status and risk of breast cancer recurrence. However, no specific variable selection method for the cure model is available. In this paper, we present a variable selection approach with penalized likelihood for the cure model. The estimation can be implemented easily by combining the computational methods for penalized logistic regression and the penalized Cox proportional hazards models with the expectation-maximization algorithm. We illustrate the proposed approach on data from a breast cancer study. We conducted Monte Carlo simulations to evaluate the performance of the proposed method. We used and compared different penalty functions in the simulation studies. Copyright © 2012 John Wiley & Sons, Ltd.

[Sexual behavior and emergency contraception among adolescents from public schools in Pernambuco State, Brazil].

PubMed

Araújo, Maria Suely Peixoto de; Costa, Laura Olinda Bregieiro Fernandes

2009-03-01

This study focused on knowledge and use of emergency contraception among 4,210 adolescents (14-19 years) enrolled in public schools in Pernambuco State, Brazil. Information was collected using the Global School-Based Student Health Survey, previously validated. Knowledge, frequency, and form of use of emergency contraception were investigated. Independent variables were classified as socio-demographic and those related to sexual behavior. Most adolescents reported knowing and having received information about the method, but among those who had already used it, only 22.1% had done so correctly. Adjusted regression analysis showed greater likelihood of knowledge about the method among girls (OR = 5.03; 95%CI: 1.72-14.69) and the sexually initiated (OR = 1.52; 95%CI: 1.34-1.75), while rural residents were 68% less knowledgeable. Rural residents showed 1.68 times higher odds (CI95%: 1.09-2.25) of incorrect use, while girls showed 71% lower likelihood of incorrect use. Sexual and reproductive education is necessary, especially among male and rural adolescents.

A score to estimate the likelihood of detecting advanced colorectal neoplasia at colonoscopy

PubMed Central

Kaminski, Michal F; Polkowski, Marcin; Kraszewska, Ewa; Rupinski, Maciej; Butruk, Eugeniusz; Regula, Jaroslaw

2014-01-01

Objective This study aimed to develop and validate a model to estimate the likelihood of detecting advanced colorectal neoplasia in Caucasian patients. Design We performed a cross-sectional analysis of database records for 40-year-old to 66-year-old patients who entered a national primary colonoscopy-based screening programme for colorectal cancer in 73 centres in Poland in the year 2007. We used multivariate logistic regression to investigate the associations between clinical variables and the presence of advanced neoplasia in a randomly selected test set, and confirmed the associations in a validation set. We used model coefficients to develop a risk score for detection of advanced colorectal neoplasia. Results Advanced colorectal neoplasia was detected in 2544 of the 35 918 included participants (7.1%). In the test set, a logistic-regression model showed that independent risk factors for advanced colorectal neoplasia were: age, sex, family history of colorectal cancer, cigarette smoking (p<0.001 for these four factors), and Body Mass Index (p=0.033). In the validation set, the model was well calibrated (ratio of expected to observed risk of advanced neoplasia: 1.00 (95% CI 0.95 to 1.06)) and had moderate discriminatory power (c-statistic 0.62). We developed a score that estimated the likelihood of detecting advanced neoplasia in the validation set, from 1.32% for patients scoring 0, to 19.12% for patients scoring 7–8. Conclusions Developed and internally validated score consisting of simple clinical factors successfully estimates the likelihood of detecting advanced colorectal neoplasia in asymptomatic Caucasian patients. Once externally validated, it may be useful for counselling or designing primary prevention studies. PMID:24385598

A score to estimate the likelihood of detecting advanced colorectal neoplasia at colonoscopy.

PubMed

Kaminski, Michal F; Polkowski, Marcin; Kraszewska, Ewa; Rupinski, Maciej; Butruk, Eugeniusz; Regula, Jaroslaw

2014-07-01

This study aimed to develop and validate a model to estimate the likelihood of detecting advanced colorectal neoplasia in Caucasian patients. We performed a cross-sectional analysis of database records for 40-year-old to 66-year-old patients who entered a national primary colonoscopy-based screening programme for colorectal cancer in 73 centres in Poland in the year 2007. We used multivariate logistic regression to investigate the associations between clinical variables and the presence of advanced neoplasia in a randomly selected test set, and confirmed the associations in a validation set. We used model coefficients to develop a risk score for detection of advanced colorectal neoplasia. Advanced colorectal neoplasia was detected in 2544 of the 35,918 included participants (7.1%). In the test set, a logistic-regression model showed that independent risk factors for advanced colorectal neoplasia were: age, sex, family history of colorectal cancer, cigarette smoking (p<0.001 for these four factors), and Body Mass Index (p=0.033). In the validation set, the model was well calibrated (ratio of expected to observed risk of advanced neoplasia: 1.00 (95% CI 0.95 to 1.06)) and had moderate discriminatory power (c-statistic 0.62). We developed a score that estimated the likelihood of detecting advanced neoplasia in the validation set, from 1.32% for patients scoring 0, to 19.12% for patients scoring 7-8. Developed and internally validated score consisting of simple clinical factors successfully estimates the likelihood of detecting advanced colorectal neoplasia in asymptomatic Caucasian patients. Once externally validated, it may be useful for counselling or designing primary prevention studies. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Evidence and Clinical Trials.

NASA Astrophysics Data System (ADS)

Goodman, Steven N.

1989-11-01

This dissertation explores the use of a mathematical measure of statistical evidence, the log likelihood ratio, in clinical trials. The methods and thinking behind the use of an evidential measure are contrasted with traditional methods of analyzing data, which depend primarily on a p-value as an estimate of the statistical strength of an observed data pattern. It is contended that neither the behavioral dictates of Neyman-Pearson hypothesis testing methods, nor the coherency dictates of Bayesian methods are realistic models on which to base inference. The use of the likelihood alone is applied to four aspects of trial design or conduct: the calculation of sample size, the monitoring of data, testing for the equivalence of two treatments, and meta-analysis--the combining of results from different trials. Finally, a more general model of statistical inference, using belief functions, is used to see if it is possible to separate the assessment of evidence from our background knowledge. It is shown that traditional and Bayesian methods can be modeled as two ends of a continuum of structured background knowledge, methods which summarize evidence at the point of maximum likelihood assuming no structure, and Bayesian methods assuming complete knowledge. Both schools are seen to be missing a concept of ignorance- -uncommitted belief. This concept provides the key to understanding the problem of sampling to a foregone conclusion and the role of frequency properties in statistical inference. The conclusion is that statistical evidence cannot be defined independently of background knowledge, and that frequency properties of an estimator are an indirect measure of uncommitted belief. Several likelihood summaries need to be used in clinical trials, with the quantitative disparity between summaries being an indirect measure of our ignorance. This conclusion is linked with parallel ideas in the philosophy of science and cognitive psychology.

MCMC multilocus lod scores: application of a new approach.

PubMed

George, Andrew W; Wijsman, Ellen M; Thompson, Elizabeth A

2005-01-01

On extended pedigrees with extensive missing data, the calculation of multilocus likelihoods for linkage analysis is often beyond the computational bounds of exact methods. Growing interest therefore surrounds the implementation of Monte Carlo estimation methods. In this paper, we demonstrate the speed and accuracy of a new Markov chain Monte Carlo method for the estimation of linkage likelihoods through an analysis of real data from a study of early-onset Alzheimer's disease. For those data sets where comparison with exact analysis is possible, we achieved up to a 100-fold increase in speed. Our approach is implemented in the program lm_bayes within the framework of the freely available MORGAN 2.6 package for Monte Carlo genetic analysis (http://www.stat.washington.edu/thompson/Genepi/MORGAN/Morgan.shtml).

Factors associated with survival of epiploic foramen entrapment colic: a multicentre, international study.

PubMed

Archer, D C; Pinchbeck, G L; Proudman, C J

2011-08-01

Epiploic foramen entrapment (EFE) has been associated with reduced post operative survival compared to other types of colic but specific factors associated with reduced long-term survival of these cases have not been evaluated in a large number of horses using survival analysis. To describe post operative survival of EFE cases and to identify factors associated with long-term survival. A prospective, multicentre, international study was conducted using clinical data and long-term follow-up information for 126 horses diagnosed with EFE during exploratory laparotomy at 15 clinics in the UK, Ireland and USA. Descriptive data were generated and survival analysis performed to identify factors associated with reduced post operative survival. For the EFE cohort that recovered following anaesthesia, survival to hospital discharge was 78.5%. Survival to 1 and 2 years post operatively was 50.6 and 34.3%, respectively. The median survival time of EFE cases undergoing surgery was 397 days. Increased packed cell volume (PCV) and increased length of small intestine (SI) resected were significantly associated with increased likelihood of mortality when multivariable analysis of pre- and intraoperative variables were analysed. When all pre-, intra- and post operative variables were analysed separately, only horses that developed post operative ileus (POI) were shown to be at increased likelihood of mortality. Increased PCV, increased length of SI resected and POI are all associated with increased likelihood of mortality of EFE cases. This emphasises the importance of early diagnosis and treatment and the need for improved strategies in the management of POI in order to reduce post operative mortality in these cases. The present study provides evidence-based information to clinicians and owners of horses undergoing surgery for EFE about long-term survival. These results are applicable to university and large private clinics over a wide geographical area. © 2011 EVJ Ltd.

Statistical inference methods for sparse biological time series data.

PubMed

Ndukum, Juliet; Fonseca, Luís L; Santos, Helena; Voit, Eberhard O; Datta, Susmita

2011-04-25

Comparing metabolic profiles under different biological perturbations has become a powerful approach to investigating the functioning of cells. The profiles can be taken as single snapshots of a system, but more information is gained if they are measured longitudinally over time. The results are short time series consisting of relatively sparse data that cannot be analyzed effectively with standard time series techniques, such as autocorrelation and frequency domain methods. In this work, we study longitudinal time series profiles of glucose consumption in the yeast Saccharomyces cerevisiae under different temperatures and preconditioning regimens, which we obtained with methods of in vivo nuclear magnetic resonance (NMR) spectroscopy. For the statistical analysis we first fit several nonlinear mixed effect regression models to the longitudinal profiles and then used an ANOVA likelihood ratio method in order to test for significant differences between the profiles. The proposed methods are capable of distinguishing metabolic time trends resulting from different treatments and associate significance levels to these differences. Among several nonlinear mixed-effects regression models tested, a three-parameter logistic function represents the data with highest accuracy. ANOVA and likelihood ratio tests suggest that there are significant differences between the glucose consumption rate profiles for cells that had been--or had not been--preconditioned by heat during growth. Furthermore, pair-wise t-tests reveal significant differences in the longitudinal profiles for glucose consumption rates between optimal conditions and heat stress, optimal and recovery conditions, and heat stress and recovery conditions (p-values <0.0001). We have developed a nonlinear mixed effects model that is appropriate for the analysis of sparse metabolic and physiological time profiles. The model permits sound statistical inference procedures, based on ANOVA likelihood ratio tests, for testing the significance of differences between short time course data under different biological perturbations.

The association between the patient and the physician genders and the likelihood of intensive care unit admission in hospital with restricted ICU bed capacity.

PubMed

Sagy, I; Fuchs, L; Mizrakli, Y; Codish, S; Politi, L; Fink, L; Novack, V

2018-05-01

Despite the evidence that the patient gender is an important component in the intensive care unit (ICU) admission decision, the role of physician gender and the interaction between the two remain unclear. To investigate the association of both the patient and the physician gender with ICU admission rate of critically ill emergency department (ED) medical patients in a hospital with restricted ICU bed capacity operates with 'closed door' policy. A retrospective population-based cohort analysis. We included patients above 18 admitted to an ED resuscitation room (RR) of a tertiary hospital during 2011-12. Data on medical, laboratory and clinical characteristics were obtained. We used an adjusted multivariable logistic regression to analyze the association between both the patient and the physician gender to the ICU admission decision. We included 831 RR admissions, 388 (46.7%) were female patients and 188 (22.6%) were treated by a female physicians. In adjusted multivariable analysis (adjusted for age, diabetes, mode of hospital transportation, first pH and patients who were treated with definitive airway and vasso-pressors in the RR), female-female combination (patient-physician, respectively) showed the lowest likelihood to be admitted to ICU (adjusted OR: 0.21; 95% CI: 0.09-0.51) compared to male-male combination, in addition to a smaller decrease among female-male (adjusted OR: 0.53; 95% CI: 0.32-0.86) and male-female (adjusted OR: 0.43; 95% CI: 0.21-0.89) combinations. We demonstrated the existence of the possible gender bias where female gender of the patient and treating physician diminish the likelihood of the restricted health resource use.

Combining Mean and Standard Deviation of Hounsfield Unit Measurements from Preoperative CT Allows More Accurate Prediction of Urinary Stone Composition Than Mean Hounsfield Units Alone.

PubMed

Tailly, Thomas; Larish, Yaniv; Nadeau, Brandon; Violette, Philippe; Glickman, Leonard; Olvera-Posada, Daniel; Alenezi, Husain; Amann, Justin; Denstedt, John; Razvi, Hassan

2016-04-01

The mineral composition of a urinary stone may influence its surgical and medical treatment. Previous attempts at identifying stone composition based on mean Hounsfield Units (HUm) have had varied success. We aimed to evaluate the additional use of standard deviation of HU (HUsd) to more accurately predict stone composition. We identified patients from two centers who had undergone urinary stone treatment between 2006 and 2013 and had mineral stone analysis and a computed tomography (CT) available. HUm and HUsd of the stones were compared with ANOVA. Receiver operative characteristic analysis with area under the curve (AUC), Youden index, and likelihood ratio calculations were performed. Data were available for 466 patients. The major components were calcium oxalate monohydrate (COM), uric acid, hydroxyapatite, struvite, brushite, cystine, and CO dihydrate (COD) in 41.4%, 19.3%, 12.4%, 7.5%, 5.8%, 5.4%, and 4.7% of patients, respectively. The HUm of UA and Br was significantly lower and higher than the HUm of any other stone type, respectively. HUm and HUsd were most accurate in predicting uric acid with an AUC of 0.969 and 0.851, respectively. The combined use of HUm and HUsd resulted in increased positive predictive value and higher likelihood ratios for identifying a stone's mineral composition for all stone types but COM. To the best of our knowledge, this is the first report of CT data aiding in the prediction of brushite stone composition. Both HUm and HUsd can help predict stone composition and their combined use results in higher likelihood ratios influencing probability.

[Formula: see text]Parental ratings of daily behavior and child cognitive test performance after pediatric mild traumatic brain injury.

PubMed

Donders, Jacobus; DeWit, Christin

2017-07-01

This study aimed to evaluate the degree to which the Behavior Rating Inventory of Executive Function (BRIEF) and Child Behavior Checklist (CBCL) measure overlapping vs. distinct constructs in pediatric patients with mild traumatic brain injury (TBI), and to examine the demographic and injury correlates of such constructs as well as those of cognitive test performance. A total of 100 parents completed the BRIEF and the CBCL within 1 to 12 months after the injury of their child. Groups were contrasted based on the presence vs. absence of impairment on, respectively, the BRIEF and the CBCL. Exploratory maximum likelihood factor analysis was used to evaluate latent constructs. Correlates of the various factor scores were evaluated through regression analysis and contrasted with those of a test of verbal learning and memory.The results revealed that the BRIEF and the CBCL disagree about the presence vs. absence of impairment in about one quarter of cases. A prior history of attention deficit/hyperactivity disorder (ADHD) was associated with an increased likelihood of impairment on both the BRIEF and the CBCL, whereas prior outpatient psychiatric treatment was associated with the increased likelihood of selective impairment on the CBCL. Latent constructs manifested themselves along cognitive regulation, emotional adjustment and behavioral regulation factors. Whereas premorbid characteristics were the exclusive correlates of these factors, performance on a test of verbal learning and memory was negatively affected by intracranial lesions on neuroimaging.It is concluded that the BRIEF and the CBCL offer complementary and non-redundant information about daily functioning after pediatric mild TBI. The correlates of cognitive test performance and parental behavior ratings after such injuries are different and reflect a divergence between premorbid and injury-related influences.

Prevalence and factors associated with non-utilization of healthcare facility for childbirth in rural and urban Nigeria: Analysis of a national population-based survey.

PubMed

Adewuyi, Emmanuel O; Zhao, Yun; Auta, Asa; Lamichhane, Reeta

2017-08-01

The aim of this study was to assess the rural-urban differences in the prevalence and factors associated with non-utilization of healthcare facility for childbirth (home delivery) in Nigeria. Dataset from the Nigeria demographic and health survey, 2013, disaggregated by rural-urban residence were analyzed with appropriate adjustment for the cluster sampling design of the survey. Factors associated with home delivery were identified using multivariable logistic regression analysis. In rural and urban residence, the prevalence of home delivery were 78.3% and 38.1%, respectively ( p < 0.001). The lowest prevalence of home delivery occurred in the South-East region for rural residence (18.6%) and the South-West region for urban residence (17.9%). The North-West region had the highest prevalence of home delivery, 93.6% and 70.5% in rural and urban residence, respectively. Low maternal as well as paternal education, low antenatal attendance, being less wealthy, the practice of Islam, and living in the North-East, North-West and the South-South regions increased the likelihood of home delivery in both rural and urban residences. Whether in rural or urban residence, birth order of one decreased the likelihood of home delivery. In rural residence only, living in the North-Central region increased the chances of home delivery. In urban residence only, maternal age ⩾ 36 years decreased the likelihood of home delivery, while 'Traditionalist/other' religion and maternal age < 20 years increased it. The prevalence of home delivery was much higher in rural than urban Nigeria and the associated factors differ to varying degrees in the two residences. Future intervention efforts would need to prioritize findings in this study.

Analysis of case-only studies accounting for genotyping error.

PubMed

Cheng, K F

2007-03-01

The case-only design provides one approach to assess possible interactions between genetic and environmental factors. It has been shown that if these factors are conditionally independent, then a case-only analysis is not only valid but also very efficient. However, a drawback of the case-only approach is that its conclusions may be biased by genotyping errors. In this paper, our main aim is to propose a method for analysis of case-only studies when these errors occur. We show that the bias can be adjusted through the use of internal validation data, which are obtained by genotyping some sampled individuals twice. Our analysis is based on a simple and yet highly efficient conditional likelihood approach. Simulation studies considered in this paper confirm that the new method has acceptable performance under genotyping errors.

The Relation between Factor Score Estimates, Image Scores, and Principal Component Scores

ERIC Educational Resources Information Center

Velicer, Wayne F.

1976-01-01

Investigates the relation between factor score estimates, principal component scores, and image scores. The three methods compared are maximum likelihood factor analysis, principal component analysis, and a variant of rescaled image analysis. (RC)

Empirical likelihood-based confidence intervals for the sensitivity of a continuous-scale diagnostic test at a fixed level of specificity.

PubMed

Gengsheng Qin; Davis, Angela E; Jing, Bing-Yi

2011-06-01

For a continuous-scale diagnostic test, it is often of interest to find the range of the sensitivity of the test at the cut-off that yields a desired specificity. In this article, we first define a profile empirical likelihood ratio for the sensitivity of a continuous-scale diagnostic test and show that its limiting distribution is a scaled chi-square distribution. We then propose two new empirical likelihood-based confidence intervals for the sensitivity of the test at a fixed level of specificity by using the scaled chi-square distribution. Simulation studies are conducted to compare the finite sample performance of the newly proposed intervals with the existing intervals for the sensitivity in terms of coverage probability. A real example is used to illustrate the application of the recommended methods.

Two simple clinical tests for predicting onset of medial tibial stress syndrome: shin palpation test and shin oedema test.

PubMed

Newman, Phil; Adams, Roger; Waddington, Gordon

2012-09-01

To examine the relationship between two clinical test results and future diagnosis of (Medial Tibial Stress Syndrome) MTSS in personnel at a military trainee establishment. Data from a preparticipation musculoskeletal screening test performed on 384 Australian Defence Force Academy Officer Cadets were compared against 693 injuries reported by 326 of the Officer Cadets in the following 16 months. Data were held in an Injury Surveillance database and analysed using χ² and Fisher's Exact tests, and Receiver Operating Characteristic Curve analysis. Diagnosis of MTSS, confirmed by an independent blinded health practitioner. Both the palpation and oedema clinical tests were each found to be significant predictors for later onset of MTSS. Specifically: Shin palpation test OR 4.63, 95% CI 2.5 to 8.5, Positive Likelihood Ratio 3.38, Negative Likelihood Ratio 0.732, Pearson χ² p<0.001; Shin oedema test OR 76.1 95% CI 9.6 to 602.7, Positive Likelihood Ratio 7.26, Negative Likelihood Ratio 0.095, Fisher's Exact p<0.001; Combined Shin Palpation Test and Shin Oedema Test Positive Likelihood Ratio 7.94, Negative Likelihood Ratio <0.001, Fisher's Exact p<0.001. Female gender was found to be an independent risk factor (OR 2.97, 95% CI 1.66 to 5.31, Positive Likelihood Ratio 2.09, Negative Likelihood Ratio 0.703, Pearson χ² p<0.001) for developing MTSS. The tests for MTSS employed here are components of a normal clinical examination used to diagnose MTSS. This paper confirms that these tests and female gender can also be confidently applied in predicting those in an asymptomatic population who are at greater risk of developing MTSS symptoms with activity at some point in the future.

A Comparison of Pseudo-Maximum Likelihood and Asymptotically Distribution-Free Dynamic Factor Analysis Parameter Estimation in Fitting Covariance Structure Models to Block-Toeplitz Matrices Representing Single-Subject Multivariate Time-Series.

ERIC Educational Resources Information Center

Molenaar, Peter C. M.; Nesselroade, John R.

1998-01-01

Pseudo-Maximum Likelihood (p-ML) and Asymptotically Distribution Free (ADF) estimation methods for estimating dynamic factor model parameters within a covariance structure framework were compared through a Monte Carlo simulation. Both methods appear to give consistent model parameter estimates, but only ADF gives standard errors and chi-square…

Mean cerebral blood volume is an effective diagnostic index of recurrent and radiation injury in glioma patients: A meta-analysis of diagnostic test.

PubMed

Li, Zhanzhan; Zhou, Qin; Li, Yanyan; Yan, Shipeng; Fu, Jun; Huang, Xinqiong; Shen, Liangfang

2017-02-28

We conducted a meta-analysis to evaluate the diagnostic values of mean cerebral blood volume for recurrent and radiation injury in glioma patients. We performed systematic electronic searches for eligible study up to August 8, 2016. Bivariate mixed effects models were used to estimate the combined sensitivity, specificity, positive likelihood ratios, negative likelihood ratios, diagnostic odds ratios and their 95% confidence intervals (CIs). Fifteen studies with a total number of 576 participants were enrolled. The pooled sensitivity and specificity of diagnostic were 0.88 (95%CI: 0.82-0.92) and 0.85 (95%CI: 0.68-0.93). The pooled positive likelihood ratio is 5.73 (95%CI: 2.56-12.81), negative likelihood ratio is 0.15 (95%CI: 0.10-0.22), and the diagnostic odds ratio is 39.34 (95%CI:13.96-110.84). The summary receiver operator characteristic is 0.91 (95%CI: 0.88-0.93). However, the Deek's plot suggested publication bias may exist (t=2.30, P=0.039). Mean cerebral blood volume measurement methods seems to be very sensitive and highly specific to differentiate recurrent and radiation injury in glioma patients. The results should be interpreted with caution because of the potential bias.

Screening for postnatal depression in Chinese-speaking women using the Hong Kong translated version of the Edinburgh Postnatal Depression Scale.

PubMed

Chen, Helen; Bautista, Dianne; Ch'ng, Ying Chia; Li, Wenyun; Chan, Edwin; Rush, A John

2013-06-01

The Edinburgh Postnatal Depression Scale (EPDS) may not be a uniformly valid postnatal depression (PND) screen across populations. We evaluated the performance of a Chinese translation of 10-item (HK-EPDS) and six-item (HK-EPDS-6) versions in post-partum women in Singapore. Chinese-speaking post-partum obstetric clinic patients were recruited for this study. They completed the HK-EPDS, from which we derived the six-item HK-EPDS-6. All women were clinically assessed for PND based on Diagnostic and Statistical Manual, Fourth Edition-Text Revision criteria. Receiver-operator curve (ROC) analyses and likelihood ratio computations informed scale cutoff choices. Clinical fitness was judged by thresholds for internal consistency [α ≥ 0.70] and for diagnostic performance by true-positive rate (>85%), false-positive rate (≤10%), positive likelihood ratio (>1), negative likelihood ratio (<0.2), area under the ROC curve (AUC, ≥90%) and effect size (≥0.80). Based on clinical interview, prevalence of PND was 6.2% in 487 post-partum women. HK-EPDS internal consistency was 0.84. At 13 or more cutoff, the true-positive rate was 86.7%, false-positive rate 3.3%, positive likelihood ratio 26.4, negative likelihood ratio 0.14, AUC 94.4% and effect size 0.81. For the HK-EPDS-6, internal consistency was 0.76. At 8 or more cutoff, we found a true-positive rate of 86.7%, false-positive rate 6.6%, positive likelihood ratio 13.2, negative likelihood ration 0.14, AUC 92.9% and effect size 0.98. The HK-EPDS (cutoff ≥13) and HK-EPDS6 (cutoff ≥8) are fit for PND screening for general population post-partum women. The brief six-item version appears to be clinically suitable for quick screening in Chinese speaking women. Copyright © 2013 Wiley Publishing Asia Pty Ltd.

Bayesian imperfect information analysis for clinical recurrent data

PubMed Central

Chang, Chih-Kuang; Chang, Chi-Chang

2015-01-01

In medical research, clinical practice must often be undertaken with imperfect information from limited resources. This study applied Bayesian imperfect information-value analysis to realistic situations to produce likelihood functions and posterior distributions, to a clinical decision-making problem for recurrent events. In this study, three kinds of failure models are considered, and our methods illustrated with an analysis of imperfect information from a trial of immunotherapy in the treatment of chronic granulomatous disease. In addition, we present evidence toward a better understanding of the differing behaviors along with concomitant variables. Based on the results of simulations, the imperfect information value of the concomitant variables was evaluated and different realistic situations were compared to see which could yield more accurate results for medical decision-making. PMID:25565853

The Extended-Image Tracking Technique Based on the Maximum Likelihood Estimation

NASA Technical Reports Server (NTRS)

Tsou, Haiping; Yan, Tsun-Yee

2000-01-01

This paper describes an extended-image tracking technique based on the maximum likelihood estimation. The target image is assume to have a known profile covering more than one element of a focal plane detector array. It is assumed that the relative position between the imager and the target is changing with time and the received target image has each of its pixels disturbed by an independent additive white Gaussian noise. When a rotation-invariant movement between imager and target is considered, the maximum likelihood based image tracking technique described in this paper is a closed-loop structure capable of providing iterative update of the movement estimate by calculating the loop feedback signals from a weighted correlation between the currently received target image and the previously estimated reference image in the transform domain. The movement estimate is then used to direct the imager to closely follow the moving target. This image tracking technique has many potential applications, including free-space optical communications and astronomy where accurate and stabilized optical pointing is essential.

Theoretical Analysis of Penalized Maximum-Likelihood Patlak Parametric Image Reconstruction in Dynamic PET for Lesion Detection.

PubMed

Yang, Li; Wang, Guobao; Qi, Jinyi

2016-04-01

Detecting cancerous lesions is a major clinical application of emission tomography. In a previous work, we studied penalized maximum-likelihood (PML) image reconstruction for lesion detection in static PET. Here we extend our theoretical analysis of static PET reconstruction to dynamic PET. We study both the conventional indirect reconstruction and direct reconstruction for Patlak parametric image estimation. In indirect reconstruction, Patlak parametric images are generated by first reconstructing a sequence of dynamic PET images, and then performing Patlak analysis on the time activity curves (TACs) pixel-by-pixel. In direct reconstruction, Patlak parametric images are estimated directly from raw sinogram data by incorporating the Patlak model into the image reconstruction procedure. PML reconstruction is used in both the indirect and direct reconstruction methods. We use a channelized Hotelling observer (CHO) to assess lesion detectability in Patlak parametric images. Simplified expressions for evaluating the lesion detectability have been derived and applied to the selection of the regularization parameter value to maximize detection performance. The proposed method is validated using computer-based Monte Carlo simulations. Good agreements between the theoretical predictions and the Monte Carlo results are observed. Both theoretical predictions and Monte Carlo simulation results show the benefit of the indirect and direct methods under optimized regularization parameters in dynamic PET reconstruction for lesion detection, when compared with the conventional static PET reconstruction.

Functional magnetic resonance imaging during emotion recognition in social anxiety disorder: an activation likelihood meta-analysis

PubMed Central

Hattingh, Coenraad J.; Ipser, J.; Tromp, S. A.; Syal, S.; Lochner, C.; Brooks, S. J.; Stein, D. J.

2012-01-01

Background: Social anxiety disorder (SAD) is characterized by abnormal fear and anxiety in social situations. Functional magnetic resonance imaging (fMRI) is a brain imaging technique that can be used to demonstrate neural activation to emotionally salient stimuli. However, no attempt has yet been made to statistically collate fMRI studies of brain activation, using the activation likelihood-estimate (ALE) technique, in response to emotion recognition tasks in individuals with SAD. Methods: A systematic search of fMRI studies of neural responses to socially emotive cues in SAD was undertaken. ALE meta-analysis, a voxel-based meta-analytic technique, was used to estimate the most significant activations during emotional recognition. Results: Seven studies were eligible for inclusion in the meta-analysis, constituting a total of 91 subjects with SAD, and 93 healthy controls. The most significant areas of activation during emotional vs. neutral stimuli in individuals with SAD compared to controls were: bilateral amygdala, left medial temporal lobe encompassing the entorhinal cortex, left medial aspect of the inferior temporal lobe encompassing perirhinal cortex and parahippocampus, right anterior cingulate, right globus pallidus, and distal tip of right postcentral gyrus. Conclusion: The results are consistent with neuroanatomic models of the role of the amygdala in fear conditioning, and the importance of the limbic circuitry in mediating anxiety symptoms. PMID:23335892

Improvement and comparison of likelihood functions for model calibration and parameter uncertainty analysis within a Markov chain Monte Carlo scheme

NASA Astrophysics Data System (ADS)

Cheng, Qin-Bo; Chen, Xi; Xu, Chong-Yu; Reinhardt-Imjela, Christian; Schulte, Achim

2014-11-01

In this study, the likelihood functions for uncertainty analysis of hydrological models are compared and improved through the following steps: (1) the equivalent relationship between the Nash-Sutcliffe Efficiency coefficient (NSE) and the likelihood function with Gaussian independent and identically distributed residuals is proved; (2) a new estimation method of the Box-Cox transformation (BC) parameter is developed to improve the effective elimination of the heteroscedasticity of model residuals; and (3) three likelihood functions-NSE, Generalized Error Distribution with BC (BC-GED) and Skew Generalized Error Distribution with BC (BC-SGED)-are applied for SWAT-WB-VSA (Soil and Water Assessment Tool - Water Balance - Variable Source Area) model calibration in the Baocun watershed, Eastern China. Performances of calibrated models are compared using the observed river discharges and groundwater levels. The result shows that the minimum variance constraint can effectively estimate the BC parameter. The form of the likelihood function significantly impacts on the calibrated parameters and the simulated results of high and low flow components. SWAT-WB-VSA with the NSE approach simulates flood well, but baseflow badly owing to the assumption of Gaussian error distribution, where the probability of the large error is low, but the small error around zero approximates equiprobability. By contrast, SWAT-WB-VSA with the BC-GED or BC-SGED approach mimics baseflow well, which is proved in the groundwater level simulation. The assumption of skewness of the error distribution may be unnecessary, because all the results of the BC-SGED approach are nearly the same as those of the BC-GED approach.

Mapping grass communities based on multi-temporal Landsat TM imagery and environmental variables

NASA Astrophysics Data System (ADS)

Zeng, Yuandi; Liu, Yanfang; Liu, Yaolin; de Leeuw, Jan

2007-06-01

Information on the spatial distribution of grass communities in wetland is increasingly recognized as important for effective wetland management and biological conservation. Remote sensing techniques has been proved to be an effective alternative to intensive and costly ground surveys for mapping grass community. However, the mapping accuracy of grass communities in wetland is still not preferable. The aim of this paper is to develop an effective method to map grass communities in Poyang Lake Natural Reserve. Through statistic analysis, elevation is selected as an environmental variable for its high relationship with the distribution of grass communities; NDVI stacked from images of different months was used to generate Carex community map; the image in October was used to discriminate Miscanthus and Cynodon communities. Classifications were firstly performed with maximum likelihood classifier using single date satellite image with and without elevation; then layered classifications were performed using multi-temporal satellite imagery and elevation with maximum likelihood classifier, decision tree and artificial neural network separately. The results show that environmental variables can improve the mapping accuracy; and the classification with multitemporal imagery and elevation is significantly better than that with single date image and elevation (p=0.001). Besides, maximum likelihood (a=92.71%, k=0.90) and artificial neural network (a=94.79%, k=0.93) perform significantly better than decision tree (a=86.46%, k=0.83).

Selected aspects of prior and likelihood information for a Bayesian classifier in a road safety analysis.

PubMed

Nowakowska, Marzena

2017-04-01

The development of the Bayesian logistic regression model classifying the road accident severity is discussed. The already exploited informative priors (method of moments, maximum likelihood estimation, and two-stage Bayesian updating), along with the original idea of a Boot prior proposal, are investigated when no expert opinion has been available. In addition, two possible approaches to updating the priors, in the form of unbalanced and balanced training data sets, are presented. The obtained logistic Bayesian models are assessed on the basis of a deviance information criterion (DIC), highest probability density (HPD) intervals, and coefficients of variation estimated for the model parameters. The verification of the model accuracy has been based on sensitivity, specificity and the harmonic mean of sensitivity and specificity, all calculated from a test data set. The models obtained from the balanced training data set have a better classification quality than the ones obtained from the unbalanced training data set. The two-stage Bayesian updating prior model and the Boot prior model, both identified with the use of the balanced training data set, outperform the non-informative, method of moments, and maximum likelihood estimation prior models. It is important to note that one should be careful when interpreting the parameters since different priors can lead to different models. Copyright © 2017 Elsevier Ltd. All rights reserved.

Likelihood of Breastfeeding Within the USDA's Food and Nutrition Service Special Supplemental Nutrition Program for Women, Infants, and Children Population.

PubMed

Houghtaling, Bailey; Byker Shanks, Carmen; Jenkins, Mica

2017-02-01

Breastfeeding is an important public health initiative. Low-income women benefiting from the U.S. Department of Agriculture's Food and Nutrition Service Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) are a prime population for breastfeeding promotion efforts. Research aim: This study aims to determine factors associated with increased likelihood of breastfeeding for WIC participants. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement guided the systematic review of literature. Database searches occurred in September and October 2014 and included studies limited to the previous 10 years. The following search terms were used: low-income; WIC; women, infants, and children; breastfeeding; breast milk; and maternal and child health. The criterion for inclusion was a study sample of women and children enrolled in the WIC program, thereby excluding non-United States-based research. Factors that increased the likelihood of breastfeeding for WIC participants included sociodemographic and health characteristics ( n = 17); environmental and media support ( n = 4); government policy ( n = 2); intention to breastfeed, breastfeeding in hospital, or previous breastfeeding experience ( n = 9); attitudes toward and knowledge of breastfeeding benefits ( n = 6); health care provider or social support; and time exposure to WIC services ( n = 5). The complexity of breastfeeding behaviors within this population is clear. Results provide multisectored insight for future research, policies, and practices in support of increasing breastfeeding rates among WIC participants.

On the insufficiency of arbitrarily precise covariance matrices: non-Gaussian weak-lensing likelihoods

NASA Astrophysics Data System (ADS)

Sellentin, Elena; Heavens, Alan F.

2018-01-01

We investigate whether a Gaussian likelihood, as routinely assumed in the analysis of cosmological data, is supported by simulated survey data. We define test statistics, based on a novel method that first destroys Gaussian correlations in a data set, and then measures the non-Gaussian correlations that remain. This procedure flags pairs of data points that depend on each other in a non-Gaussian fashion, and thereby identifies where the assumption of a Gaussian likelihood breaks down. Using this diagnosis, we find that non-Gaussian correlations in the CFHTLenS cosmic shear correlation functions are significant. With a simple exclusion of the most contaminated data points, the posterior for s8 is shifted without broadening, but we find no significant reduction in the tension with s8 derived from Planck cosmic microwave background data. However, we also show that the one-point distributions of the correlation statistics are noticeably skewed, such that sound weak-lensing data sets are intrinsically likely to lead to a systematically low lensing amplitude being inferred. The detected non-Gaussianities get larger with increasing angular scale such that for future wide-angle surveys such as Euclid or LSST, with their very small statistical errors, the large-scale modes are expected to be increasingly affected. The shifts in posteriors may then not be negligible and we recommend that these diagnostic tests be run as part of future analyses.

Responder analysis without dichotomization.

PubMed

Zhang, Zhiwei; Chu, Jianxiong; Rahardja, Dewi; Zhang, Hui; Tang, Li

2016-01-01

In clinical trials, it is common practice to categorize subjects as responders and non-responders on the basis of one or more clinical measurements under pre-specified rules. Such a responder analysis is often criticized for the loss of information in dichotomizing one or more continuous or ordinal variables. It is worth noting that a responder analysis can be performed without dichotomization, because the proportion of responders for each treatment can be derived from a model for the original clinical variables (used to define a responder) and estimated by substituting maximum likelihood estimators of model parameters. This model-based approach can be considerably more efficient and more effective for dealing with missing data than the usual approach based on dichotomization. For parameter estimation, the model-based approach generally requires correct specification of the model for the original variables. However, under the sharp null hypothesis, the model-based approach remains unbiased for estimating the treatment difference even if the model is misspecified. We elaborate on these points and illustrate them with a series of simulation studies mimicking a study of Parkinson's disease, which involves longitudinal continuous data in the definition of a responder.

Computer aided manual validation of mass spectrometry-based proteomic data.

PubMed

Curran, Timothy G; Bryson, Bryan D; Reigelhaupt, Michael; Johnson, Hannah; White, Forest M

2013-06-15

Advances in mass spectrometry-based proteomic technologies have increased the speed of analysis and the depth provided by a single analysis. Computational tools to evaluate the accuracy of peptide identifications from these high-throughput analyses have not kept pace with technological advances; currently the most common quality evaluation methods are based on statistical analysis of the likelihood of false positive identifications in large-scale data sets. While helpful, these calculations do not consider the accuracy of each identification, thus creating a precarious situation for biologists relying on the data to inform experimental design. Manual validation is the gold standard approach to confirm accuracy of database identifications, but is extremely time-intensive. To palliate the increasing time required to manually validate large proteomic datasets, we provide computer aided manual validation software (CAMV) to expedite the process. Relevant spectra are collected, catalogued, and pre-labeled, allowing users to efficiently judge the quality of each identification and summarize applicable quantitative information. CAMV significantly reduces the burden associated with manual validation and will hopefully encourage broader adoption of manual validation in mass spectrometry-based proteomics. Copyright © 2013 Elsevier Inc. All rights reserved.

GRID-BASED EXPLORATION OF COSMOLOGICAL PARAMETER SPACE WITH SNAKE

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

Mikkelsen, K.; Næss, S. K.; Eriksen, H. K., E-mail: kristin.mikkelsen@astro.uio.no

2013-11-10

We present a fully parallelized grid-based parameter estimation algorithm for investigating multidimensional likelihoods called Snake, and apply it to cosmological parameter estimation. The basic idea is to map out the likelihood grid-cell by grid-cell according to decreasing likelihood, and stop when a certain threshold has been reached. This approach improves vastly on the 'curse of dimensionality' problem plaguing standard grid-based parameter estimation simply by disregarding grid cells with negligible likelihood. The main advantages of this method compared to standard Metropolis-Hastings Markov Chain Monte Carlo methods include (1) trivial extraction of arbitrary conditional distributions; (2) direct access to Bayesian evidences; (3)more » better sampling of the tails of the distribution; and (4) nearly perfect parallelization scaling. The main disadvantage is, as in the case of brute-force grid-based evaluation, a dependency on the number of parameters, N{sub par}. One of the main goals of the present paper is to determine how large N{sub par} can be, while still maintaining reasonable computational efficiency; we find that N{sub par} = 12 is well within the capabilities of the method. The performance of the code is tested by comparing cosmological parameters estimated using Snake and the WMAP-7 data with those obtained using CosmoMC, the current standard code in the field. We find fully consistent results, with similar computational expenses, but shorter wall time due to the perfect parallelization scheme.« less

What affects public acceptance of recycled and desalinated water?

PubMed Central

Dolnicar, Sara; Hurlimann, Anna; Grün, Bettina

2011-01-01

This paper identifies factors that are associated with higher levels of public acceptance for recycled and desalinated water. For the first time, a wide range of hypothesized factors, both of socio-demographic and psychographic nature, are included simultaneously. The key results, based on a survey study of about 3000 respondents are that: (1) drivers of the stated likelihood of using desalinated water differ somewhat from drivers of the stated likelihood of using recycled water; (2) positive perceptions of, and knowledge about, the respective water source are key drivers for the stated likelihood of usage; and (3) awareness of water scarcity, as well as prior experience with using water from alternative sources, increases the stated likelihood of use. Practical recommendations for public policy makers, such as key messages to be communicated to the public, are derived. PMID:20950834

SubspaceEM: A Fast Maximum-a-posteriori Algorithm for Cryo-EM Single Particle Reconstruction

PubMed Central

Dvornek, Nicha C.; Sigworth, Fred J.; Tagare, Hemant D.

2015-01-01

Single particle reconstruction methods based on the maximum-likelihood principle and the expectation-maximization (E–M) algorithm are popular because of their ability to produce high resolution structures. However, these algorithms are computationally very expensive, requiring a network of computational servers. To overcome this computational bottleneck, we propose a new mathematical framework for accelerating maximum-likelihood reconstructions. The speedup is by orders of magnitude and the proposed algorithm produces similar quality reconstructions compared to the standard maximum-likelihood formulation. Our approach uses subspace approximations of the cryo-electron microscopy (cryo-EM) data and projection images, greatly reducing the number of image transformations and comparisons that are computed. Experiments using simulated and actual cryo-EM data show that speedup in overall execution time compared to traditional maximum-likelihood reconstruction reaches factors of over 300. PMID:25839831

Integration within the Felsenstein equation for improved Markov chain Monte Carlo methods in population genetics

PubMed Central

Hey, Jody; Nielsen, Rasmus

2007-01-01

In 1988, Felsenstein described a framework for assessing the likelihood of a genetic data set in which all of the possible genealogical histories of the data are considered, each in proportion to their probability. Although not analytically solvable, several approaches, including Markov chain Monte Carlo methods, have been developed to find approximate solutions. Here, we describe an approach in which Markov chain Monte Carlo simulations are used to integrate over the space of genealogies, whereas other parameters are integrated out analytically. The result is an approximation to the full joint posterior density of the model parameters. For many purposes, this function can be treated as a likelihood, thereby permitting likelihood-based analyses, including likelihood ratio tests of nested models. Several examples, including an application to the divergence of chimpanzee subspecies, are provided. PMID:17301231

Drivers of wetland conversion: a global meta-analysis.

PubMed

van Asselen, Sanneke; Verburg, Peter H; Vermaat, Jan E; Janse, Jan H

2013-01-01

Meta-analysis of case studies has become an important tool for synthesizing case study findings in land change. Meta-analyses of deforestation, urbanization, desertification and change in shifting cultivation systems have been published. This present study adds to this literature, with an analysis of the proximate causes and underlying forces of wetland conversion at a global scale using two complementary approaches of systematic review. Firstly, a meta-analysis of 105 case-study papers describing wetland conversion was performed, showing that different combinations of multiple-factor proximate causes, and underlying forces, drive wetland conversion. Agricultural development has been the main proximate cause of wetland conversion, and economic growth and population density are the most frequently identified underlying forces. Secondly, to add a more quantitative component to the study, a logistic meta-regression analysis was performed to estimate the likelihood of wetland conversion worldwide, using globally-consistent biophysical and socioeconomic location factor maps. Significant factors explaining wetland conversion, in order of importance, are market influence, total wetland area (lower conversion probability), mean annual temperature and cropland or built-up area. The regression analyses results support the outcomes of the meta-analysis of the processes of conversion mentioned in the individual case studies. In other meta-analyses of land change, similar factors (e.g., agricultural development, population growth, market/economic factors) are also identified as important causes of various types of land change (e.g., deforestation, desertification). Meta-analysis helps to identify commonalities across the various local case studies and identify which variables may lead to individual cases to behave differently. The meta-regression provides maps indicating the likelihood of wetland conversion worldwide based on the location factors that have determined historic conversions.

Drivers of Wetland Conversion: a Global Meta-Analysis

PubMed Central

van Asselen, Sanneke; Verburg, Peter H.; Vermaat, Jan E.; Janse, Jan H.

2013-01-01

Meta-analysis of case studies has become an important tool for synthesizing case study findings in land change. Meta-analyses of deforestation, urbanization, desertification and change in shifting cultivation systems have been published. This present study adds to this literature, with an analysis of the proximate causes and underlying forces of wetland conversion at a global scale using two complementary approaches of systematic review. Firstly, a meta-analysis of 105 case-study papers describing wetland conversion was performed, showing that different combinations of multiple-factor proximate causes, and underlying forces, drive wetland conversion. Agricultural development has been the main proximate cause of wetland conversion, and economic growth and population density are the most frequently identified underlying forces. Secondly, to add a more quantitative component to the study, a logistic meta-regression analysis was performed to estimate the likelihood of wetland conversion worldwide, using globally-consistent biophysical and socioeconomic location factor maps. Significant factors explaining wetland conversion, in order of importance, are market influence, total wetland area (lower conversion probability), mean annual temperature and cropland or built-up area. The regression analyses results support the outcomes of the meta-analysis of the processes of conversion mentioned in the individual case studies. In other meta-analyses of land change, similar factors (e.g., agricultural development, population growth, market/economic factors) are also identified as important causes of various types of land change (e.g., deforestation, desertification). Meta-analysis helps to identify commonalities across the various local case studies and identify which variables may lead to individual cases to behave differently. The meta-regression provides maps indicating the likelihood of wetland conversion worldwide based on the location factors that have determined historic conversions. PMID:24282580

Hybrid pairwise likelihood analysis of animal behavior experiments.

PubMed

Cattelan, Manuela; Varin, Cristiano

2013-12-01

The study of the determinants of fights between animals is an important issue in understanding animal behavior. For this purpose, tournament experiments among a set of animals are often used by zoologists. The results of these tournament experiments are naturally analyzed by paired comparison models. Proper statistical analysis of these models is complicated by the presence of dependence between the outcomes of fights because the same animal is involved in different contests. This paper discusses two different model specifications to account for between-fights dependence. Models are fitted through the hybrid pairwise likelihood method that iterates between optimal estimating equations for the regression parameters and pairwise likelihood inference for the association parameters. This approach requires the specification of means and covariances only. For this reason, the method can be applied also when the computation of the joint distribution is difficult or inconvenient. The proposed methodology is investigated by simulation studies and applied to real data about adult male Cape Dwarf Chameleons. © 2013, The International Biometric Society.

Undernutrition among adults in India: the significance of individual-level and contextual factors impacting on the likelihood of underweight across sub-populations.

PubMed

Siddiqui, Md Zakaria; Donato, Ronald

2017-01-01

To investigate the extent to which individual-level as well as macro-level contextual factors influence the likelihood of underweight across adult sub-populations in India. Population-based cross-sectional survey included in India's National Health Family Survey conducted in 2005-06. We disaggregated into eight sub-populations. Multistage nationally representative household survey covering 99 % of India's population. The survey covered 124 385 females aged 15-49 years and 74 369 males aged 15-54 years. A social gradient in underweight exists in India. Even after allowing for wealth status, differences in the predicted probability of underweight persisted based upon rurality, age/maturity and gender. We found individual-level education lowered the likelihood of underweight for males, but no statistical association for females. Paradoxically, rural young (15-24 years) females from more educated villages had a higher likelihood of underweight relative to those in less educated villages; but for rural mature (>24 years) females the opposite was the case. Christians had a significantly lower likelihood of underweight relative to other socio-religious groups (OR=0·53-0·80). Higher state-level inequality increased the likelihood of underweight across most population groups, while neighbourhood inequality exhibited a similar relationship for the rural young population subgroups only. Individual states/neighbourhoods accounted for 5-9 % of the variation in the prediction of underweight. We found that rural young females represent a particularly highly vulnerable sub-population. Economic growth alone is unlikely to reduce the burden of malnutrition in India; accordingly, policy makers need to address the broader social determinants that contribute to higher underweight prevalence in specific demographic subgroups.

Sustainability likelihood of remediation options for metal-contaminated soil/sediment.

PubMed

Chen, Season S; Taylor, Jessica S; Baek, Kitae; Khan, Eakalak; Tsang, Daniel C W; Ok, Yong Sik

2017-05-01

Multi-criteria analysis and detailed impact analysis were carried out to assess the sustainability of four remedial alternatives for metal-contaminated soil/sediment at former timber treatment sites and harbour sediment with different scales. The sustainability was evaluated in the aspects of human health and safety, environment, stakeholder concern, and land use, under four different scenarios with varying weighting factors. The Monte Carlo simulation was performed to reveal the likelihood of accomplishing sustainable remediation with different treatment options at different sites. The results showed that in-situ remedial technologies were more sustainable than ex-situ ones, where in-situ containment demonstrated both the most sustainable result and the highest probability to achieve sustainability amongst the four remedial alternatives in this study, reflecting the lesser extent of off-site and on-site impacts. Concerns associated with ex-situ options were adverse impacts tied to all four aspects and caused by excavation, extraction, and off-site disposal. The results of this study suggested the importance of considering the uncertainties resulting from the remedial options (i.e., stochastic analysis) in addition to the overall sustainability scores (i.e., deterministic analysis). The developed framework and model simulation could serve as an assessment for the sustainability likelihood of remedial options to ensure sustainable remediation of contaminated sites. Copyright © 2017 Elsevier Ltd. All rights reserved.

Practical aspects of a maximum likelihood estimation method to extract stability and control derivatives from flight data

NASA Technical Reports Server (NTRS)

Iliff, K. W.; Maine, R. E.

1976-01-01

A maximum likelihood estimation method was applied to flight data and procedures to facilitate the routine analysis of a large amount of flight data were described. Techniques that can be used to obtain stability and control derivatives from aircraft maneuvers that are less than ideal for this purpose are described. The techniques involve detecting and correcting the effects of dependent or nearly dependent variables, structural vibration, data drift, inadequate instrumentation, and difficulties with the data acquisition system and the mathematical model. The use of uncertainty levels and multiple maneuver analysis also proved to be useful in improving the quality of the estimated coefficients. The procedures used for editing the data and for overall analysis are also discussed.

Using Qualitative Hazard Analysis to Guide Quantitative Safety Analysis

NASA Technical Reports Server (NTRS)

Shortle, J. F.; Allocco, M.

2005-01-01

Quantitative methods can be beneficial in many types of safety investigations. However, there are many difficulties in using quantitative m ethods. Far example, there may be little relevant data available. This paper proposes a framework for using quantitative hazard analysis to prioritize hazard scenarios most suitable for quantitative mziysis. The framework first categorizes hazard scenarios by severity and likelihood. We then propose another metric "modeling difficulty" that desc ribes the complexity in modeling a given hazard scenario quantitatively. The combined metrics of severity, likelihood, and modeling difficu lty help to prioritize hazard scenarios for which quantitative analys is should be applied. We have applied this methodology to proposed concepts of operations for reduced wake separation for airplane operatio ns at closely spaced parallel runways.

Comparison of two different size needles in endoscopic ultrasound-guided fine-needle aspiration for diagnosing solid pancreatic lesions

PubMed Central

Xu, Mei-Mei; Jia, Hong-Yu; Yan, Li-Li; Li, Shan-Shan; Zheng, Yue

2017-01-01

Abstract Background: This meta-analysis aimed to provide a pooled analysis of prospective controlled trials comparing the diagnostic accuracy of 22-G and 25-G needles on endoscopic ultrasonography (EUS-FNA) of the solid pancreatic mass. Methods: We established a rigorous study protocol according to Cochrane Collaboration recommendations. We systematically searched the PubMed and Embase databases to identify articles to include in the meta-analysis. Sensitivity, specificity, and corresponding 95% confidence intervals were calculated for 22-G and 25-G needles of individual studies from the contingency tables. Results: Eleven prospective controlled trials included a total of 837 patients (412 with 22-G vs 425 with 25-G). Our outcomes revealed that 25-G needles (92% [95% CI, 89%–95%]) have higher sensitivity than 22-G needles (88% [95% CI, 84%–91%]) on solid pancreatic mass EUS-FNA (P = 0.046). However, there were no significant differences between the 2 groups in overall diagnostic specificity (P = 0.842). The pooled positive and negative likelihood ratio of the 22-G needle were 12.61 (95% CI, 5.65–28.14) and 0.16 (95% CI, 0.12–0.21), respectively. The pooled positive likelihood ratio was 12.61 (95% CI, 5.65–28.14), and the negative likelihood ratio was 0.16 (95% CI, 0.12–0.21) for the 22-G needle. The pooled positive likelihood ratio was 8.44 (95% CI, 3.87–18.42), and the negative likelihood ratio was 0.13 (95% CI, 0.09–0.18) for the 25-G needle. The area under the summary receiver operating characteristic curve was 0.97 for the 22-G needle and 0.96 for the 25-G needle. Conclusion: Compared to the study of 22-G EUS-FNA needles, our study showed that 25-G needles have superior sensitivity in the evaluation of solid pancreatic lesions by EUS–FNA. PMID:28151856

Minimizing pre- and post-defibrillation pauses increases the likelihood of return of spontaneous circulation (ROSC).

PubMed

Sell, Rebecca E; Sarno, Renee; Lawrence, Brenna; Castillo, Edward M; Fisher, Roger; Brainard, Criss; Dunford, James V; Davis, Daniel P

2010-07-01

The three-phase model of ventricular fibrillation (VF) arrest suggests a period of compressions to "prime" the heart prior to defibrillation attempts. In addition, post-shock compressions may increase the likelihood of return of spontaneous circulation (ROSC). The optimal intervals for shock delivery following cessation of compressions (pre-shock interval) and resumption of compressions following a shock (post-shock interval) remain unclear. To define optimal pre- and post-defibrillation compression pauses for out-of-hospital cardiac arrest (OOHCA). All patients suffering OOHCA from VF were identified over a 1-month period. Defibrillator data were abstracted and analyzed using the combination of ECG, impedance, and audio recording. Receiver-operator curve (ROC) analysis was used to define the optimal pre- and post-shock compression intervals. Multiple logistic regression analysis was used to quantify the relationship between these intervals and ROSC. Covariates included cumulative number of defibrillation attempts, intubation status, and administration of epinephrine in the immediate pre-shock compression cycle. Cluster adjustment was performed due to the possibility of multiple defibrillation attempts for each patient. A total of 36 patients with 96 defibrillation attempts were included. The ROC analysis identified an optimal pre-shock interval of <3s and an optimal post-shock interval of <6s. Increased likelihood of ROSC was observed with a pre-shock interval <3s (adjusted OR 6.7, 95% CI 2.0-22.3, p=0.002) and a post-shock interval of <6s (adjusted OR 10.7, 95% CI 2.8-41.4, p=0.001). Likelihood of ROSC was substantially increased with the optimization of both pre- and post-shock intervals (adjusted OR 13.1, 95% CI 3.4-49.9, p<0.001). Decreasing pre- and post-shock compression intervals increases the likelihood of ROSC in OOHCA from VF.

Risk Associated with the Release of Wolbachia-Infected Aedes aegypti Mosquitoes into the Environment in an Effort to Control Dengue.

PubMed

Murray, Justine V; Jansen, Cassie C; De Barro, Paul

2016-01-01

In an effort to eliminate dengue, a successful technology was developed with the stable introduction of the obligate intracellular bacteria Wolbachia pipientis into the mosquito Aedes aegypti to reduce its ability to transmit dengue fever due to life shortening and inhibition of viral replication effects. An analysis of risk was required before considering release of the modified mosquito into the environment. Expert knowledge and a risk assessment framework were used to identify risk associated with the release of the modified mosquito. Individual and group expert elicitation was performed to identify potential hazards. A Bayesian network (BN) was developed to capture the relationship between hazards and the likelihood of events occurring. Risk was calculated from the expert likelihood estimates populating the BN and the consequence estimates elicited from experts. The risk model for "Don't Achieve Release" provided an estimated 46% likelihood that the release would not occur by a nominated time but generated an overall risk rating of very low. The ability to obtain compliance had the greatest influence on the likelihood of release occurring. The risk model for "Cause More Harm" provided a 12.5% likelihood that more harm would result from the release, but the overall risk was considered negligible. The efficacy of mosquito management had the most influence, with the perception that the threat of dengue fever had been eliminated, resulting in less household mosquito control, and was scored as the highest ranked individual hazard (albeit low risk). The risk analysis was designed to incorporate the interacting complexity of hazards that may affect the release of the technology into the environment. The risk analysis was a small, but important, implementation phase in the success of this innovative research introducing a new technology to combat dengue transmission in the environment.

A scaling transformation for classifier output based on likelihood ratio: Applications to a CAD workstation for diagnosis of breast cancer

PubMed Central

Horsch, Karla; Pesce, Lorenzo L.; Giger, Maryellen L.; Metz, Charles E.; Jiang, Yulei

2012-01-01

Purpose: The authors developed scaling methods that monotonically transform the output of one classifier to the “scale” of another. Such transformations affect the distribution of classifier output while leaving the ROC curve unchanged. In particular, they investigated transformations between radiologists and computer classifiers, with the goal of addressing the problem of comparing and interpreting case-specific values of output from two classifiers. Methods: Using both simulated and radiologists’ rating data of breast imaging cases, the authors investigated a likelihood-ratio-scaling transformation, based on “matching” classifier likelihood ratios. For comparison, three other scaling transformations were investigated that were based on matching classifier true positive fraction, false positive fraction, or cumulative distribution function, respectively. The authors explored modifying the computer output to reflect the scale of the radiologist, as well as modifying the radiologist’s ratings to reflect the scale of the computer. They also evaluated how dataset size affects the transformations. Results: When ROC curves of two classifiers differed substantially, the four transformations were found to be quite different. The likelihood-ratio scaling transformation was found to vary widely from radiologist to radiologist. Similar results were found for the other transformations. Our simulations explored the effect of database sizes on the accuracy of the estimation of our scaling transformations. Conclusions: The likelihood-ratio-scaling transformation that the authors have developed and evaluated was shown to be capable of transforming computer and radiologist outputs to a common scale reliably, thereby allowing the comparison of the computer and radiologist outputs on the basis of a clinically relevant statistic. PMID:22559651

Neural correlates of social exclusion across ages: A coordinate-based meta-analysis of functional MRI studies.

PubMed

Vijayakumar, Nandita; Cheng, Theresa W; Pfeifer, Jennifer H

2017-06-01

Given the recent surge in functional neuroimaging studies on social exclusion, the current study employed activation likelihood estimation (ALE) based meta-analyses to identify brain regions that have consistently been implicated across different experimental paradigms used to investigate exclusion. We also examined the neural correlates underlying Cyberball, the most commonly used paradigm to study exclusion, as well as differences in exclusion-related activation between developing (7-18 years of age, from pre-adolescence up to late adolescence) and emerging adult (broadly defined as undergraduates, including late adolescence and young adulthood) samples. Results revealed involvement of the bilateral medial prefrontal and posterior cingulate cortices, right precuneus and left ventrolateral prefrontal cortex across the different paradigms used to examine social exclusion; similar activation patterns were identified when restricting the analysis to Cyberball studies. Investigations into age-related effects revealed that ventrolateral prefrontal activations identified in the full sample were driven by (i.e. present in) developmental samples, while medial prefrontal activations were driven by emerging adult samples. In addition, the right ventral striatum was implicated in exclusion, but only in developmental samples. Subtraction analysis revealed significantly greater activation likelihood in striatal and ventrolateral prefrontal clusters in the developmental samples as compared to emerging adults, though the opposite contrast failed to identify any significant regions. Findings integrate the knowledge accrued from functional neuroimaging studies on social exclusion to date, highlighting involvement of lateral prefrontal regions implicated in regulation and midline structures involved in social cognitive and self-evaluative processes across experimental paradigms and ages, as well as limbic structures in developing samples specifically. Copyright © 2017 Elsevier Inc. All rights reserved.

Population Synthesis of Radio and Gamma-ray Pulsars using the Maximum Likelihood Approach

NASA Astrophysics Data System (ADS)

Billman, Caleb; Gonthier, P. L.; Harding, A. K.

2012-01-01

We present the results of a pulsar population synthesis of normal pulsars from the Galactic disk using a maximum likelihood method. We seek to maximize the likelihood of a set of parameters in a Monte Carlo population statistics code to better understand their uncertainties and the confidence region of the model's parameter space. The maximum likelihood method allows for the use of more applicable Poisson statistics in the comparison of distributions of small numbers of detected gamma-ray and radio pulsars. Our code simulates pulsars at birth using Monte Carlo techniques and evolves them to the present assuming initial spatial, kick velocity, magnetic field, and period distributions. Pulsars are spun down to the present and given radio and gamma-ray emission characteristics. We select measured distributions of radio pulsars from the Parkes Multibeam survey and Fermi gamma-ray pulsars to perform a likelihood analysis of the assumed model parameters such as initial period and magnetic field, and radio luminosity. We present the results of a grid search of the parameter space as well as a search for the maximum likelihood using a Markov Chain Monte Carlo method. We express our gratitude for the generous support of the Michigan Space Grant Consortium, of the National Science Foundation (REU and RUI), the NASA Astrophysics Theory and Fundamental Program and the NASA Fermi Guest Investigator Program.

Prognostic significance of normal quantitative planar thallium-201 stress scintigraphy in patients with chest pain

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

Wackers, F.J.; Russo, D.J.; Russo, D.

The prognostic significance of normal quantitative planar thallium-201 stress scintigraphy was evaluated in patients with a chest pain syndrome. The prevalence of cardiac events during follow-up was related to the pretest (that is, before stress scintigraphy) likelihood of coronary artery disease determined on the basis of symptoms, age, sex and stress electrocardiography. In a consecutive series of 344 patients who had adequate thallium-201 stress scintigrams, 95 had unequivocally normal studies by quantitative analysis. The pretest likelihood of coronary artery disease in the 95 patients had a bimodal distribution. During a mean follow-up period of 22 +/- 3 months, no patientmore » died. Three patients (3%) had a cardiac event: two of these patients (pretest likelihood of coronary artery disease 54 and 94%) had a nonfatal myocardial infarction 8 and 22 months, respectively, after stress scintigraphy, and one patient (pretest likelihood 98%) underwent percutaneous transluminal coronary angioplasty 16 months after stress scintigraphy for persisting anginal complaints. Three patients were lost to follow-up; all three had a low pretest likelihood of coronary artery disease. It is concluded that patients with chest pain and normal findings on quantitative thallium-201 scintigraphy have an excellent prognosis. Cardiac events are rare (infarction rate 1% per year) and occur in patients with a moderate to high pretest likelihood of coronary artery disease.« less

GRO/EGRET data analysis software: An integrated system of custom and commercial software using standard interfaces

NASA Technical Reports Server (NTRS)

Laubenthal, N. A.; Bertsch, D.; Lal, N.; Etienne, A.; Mcdonald, L.; Mattox, J.; Sreekumar, P.; Nolan, P.; Fierro, J.

1992-01-01

The Energetic Gamma Ray Telescope Experiment (EGRET) on the Compton Gamma Ray Observatory has been in orbit for more than a year and is being used to map the full sky for gamma rays in a wide energy range from 30 to 20,000 MeV. Already these measurements have resulted in a wide range of exciting new information on quasars, pulsars, galactic sources, and diffuse gamma ray emission. The central part of the analysis is done with sky maps that typically cover an 80 x 80 degree section of the sky for an exposure time of several days. Specific software developed for this program generates the counts, exposure, and intensity maps. The analysis is done on a network of UNIX based workstations and takes full advantage of a custom-built user interface called X-dialog. The maps that are generated are stored in the FITS format for a collection of energies. These, along with similar diffuse emission background maps generated from a model calculation, serve as input to a maximum likelihood program that produces maps of likelihood with optional contours that are used to evaluate regions for sources. Likelihood also evaluates the background corrected intensity at each location for each energy interval from which spectra can be generated. Being in a standard FITS format permits all of the maps to be easily accessed by the full complement of tools available in several commercial astronomical analysis systems. In the EGRET case, IDL is used to produce graphics plots in two and three dimensions and to quickly implement any special evaluation that might be desired. Other custom-built software, such as the spectral and pulsar analyses, take advantage of the XView toolkit for display and Postscript output for the color hard copy. This poster paper outlines the data flow and provides examples of the user interfaces and output products. It stresses the advantages that are derived from the integration of the specific instrument-unique software and powerful commercial tools for graphics and statistical evaluation. This approach has several proven advantages including flexibility, a minimum of development effort, ease of use, and portability.

Generalizing Terwilliger's likelihood approach: a new score statistic to test for genetic association.

PubMed

el Galta, Rachid; Uitte de Willige, Shirley; de Visser, Marieke C H; Helmer, Quinta; Hsu, Li; Houwing-Duistermaat, Jeanine J

2007-09-24

In this paper, we propose a one degree of freedom test for association between a candidate gene and a binary trait. This method is a generalization of Terwilliger's likelihood ratio statistic and is especially powerful for the situation of one associated haplotype. As an alternative to the likelihood ratio statistic, we derive a score statistic, which has a tractable expression. For haplotype analysis, we assume that phase is known. By means of a simulation study, we compare the performance of the score statistic to Pearson's chi-square statistic and the likelihood ratio statistic proposed by Terwilliger. We illustrate the method on three candidate genes studied in the Leiden Thrombophilia Study. We conclude that the statistic follows a chi square distribution under the null hypothesis and that the score statistic is more powerful than Terwilliger's likelihood ratio statistic when the associated haplotype has frequency between 0.1 and 0.4 and has a small impact on the studied disorder. With regard to Pearson's chi-square statistic, the score statistic has more power when the associated haplotype has frequency above 0.2 and the number of variants is above five.

Efficient computation of the phylogenetic likelihood function on multi-gene alignments and multi-core architectures.

PubMed

Stamatakis, Alexandros; Ott, Michael

2008-12-27

The continuous accumulation of sequence data, for example, due to novel wet-laboratory techniques such as pyrosequencing, coupled with the increasing popularity of multi-gene phylogenies and emerging multi-core processor architectures that face problems of cache congestion, poses new challenges with respect to the efficient computation of the phylogenetic maximum-likelihood (ML) function. Here, we propose two approaches that can significantly speed up likelihood computations that typically represent over 95 per cent of the computational effort conducted by current ML or Bayesian inference programs. Initially, we present a method and an appropriate data structure to efficiently compute the likelihood score on 'gappy' multi-gene alignments. By 'gappy' we denote sampling-induced gaps owing to missing sequences in individual genes (partitions), i.e. not real alignment gaps. A first proof-of-concept implementation in RAXML indicates that this approach can accelerate inferences on large and gappy alignments by approximately one order of magnitude. Moreover, we present insights and initial performance results on multi-core architectures obtained during the transition from an OpenMP-based to a Pthreads-based fine-grained parallelization of the ML function.

A hybrid model for combining case-control and cohort studies in systematic reviews of diagnostic tests

PubMed Central

Chen, Yong; Liu, Yulun; Ning, Jing; Cormier, Janice; Chu, Haitao

2014-01-01

Systematic reviews of diagnostic tests often involve a mixture of case-control and cohort studies. The standard methods for evaluating diagnostic accuracy only focus on sensitivity and specificity and ignore the information on disease prevalence contained in cohort studies. Consequently, such methods cannot provide estimates of measures related to disease prevalence, such as population averaged or overall positive and negative predictive values, which reflect the clinical utility of a diagnostic test. In this paper, we propose a hybrid approach that jointly models the disease prevalence along with the diagnostic test sensitivity and specificity in cohort studies, and the sensitivity and specificity in case-control studies. In order to overcome the potential computational difficulties in the standard full likelihood inference of the proposed hybrid model, we propose an alternative inference procedure based on the composite likelihood. Such composite likelihood based inference does not suffer computational problems and maintains high relative efficiency. In addition, it is more robust to model mis-specifications compared to the standard full likelihood inference. We apply our approach to a review of the performance of contemporary diagnostic imaging modalities for detecting metastases in patients with melanoma. PMID:25897179

Clinicians' practice environment is associated with a higher likelihood of recommending cesarean deliveries.

PubMed

Cheng, Yvonne W; Snowden, Jonathan M; Handler, Stephanie; Tager, Ira B; Hubbard, Alan; Caughey, Aaron B

2014-08-01

Little data exist regarding clinicians' role in the rising annual incidence rate of cesarean delivery in the US. We aimed to examine if clinicians' practice environment is associated with recommending cesarean deliveries. This is a survey study of clinicians who practice obstetrics in the US. This survey included eight clinical vignettes and 27 questions regarding clinicians' practice environment. Chi-square test and multivariable logistic regression were used for statistical comparison. Of 27 675 survey links sent, 3646 clinicians received and opened the survey electronically, and 1555 (43%) participated and 1486 (94%) completed the survey. Clinicians were categorized into three groups based on eight common obstetric vignettes as: more likely (n = 215), average likelihood (n = 1099), and less likely (n = 168) to recommend cesarean. Clinician environment factors associated with a higher likelihood of recommending cesarean included Laborists/Hospitalists practice model (p < 0.001), as-needed anesthesia support (p = 0.003), and rural/suburban practice setting (p < 0.001). We identified factors in clinicians' environment associated with their likelihood of recommending cesarean delivery. The decision to recommend cesarean delivery is a complicated one and is likely not solely based on patient factors.

Parent-child communication and marijuana initiation: evidence using discrete-time survival analysis.

PubMed

Nonnemaker, James M; Silber-Ashley, Olivia; Farrelly, Matthew C; Dench, Daniel

2012-12-01

This study supplements existing literature on the relationship between parent-child communication and adolescent drug use by exploring whether parental and/or adolescent recall of specific drug-related conversations differentially impact youth's likelihood of initiating marijuana use. Using discrete-time survival analysis, we estimated the hazard of marijuana initiation using a logit model to obtain an estimate of the relative risk of initiation. Our results suggest that parent-child communication about drug use is either not protective (no effect) or - in the case of youth reports of communication - potentially harmful (leading to increased likelihood of marijuana initiation). Copyright © 2012 Elsevier Ltd. All rights reserved.

Restricted maximum likelihood estimation of genetic principal components and smoothed covariance matrices

PubMed Central

Meyer, Karin; Kirkpatrick, Mark

2005-01-01

Principal component analysis is a widely used 'dimension reduction' technique, albeit generally at a phenotypic level. It is shown that we can estimate genetic principal components directly through a simple reparameterisation of the usual linear, mixed model. This is applicable to any analysis fitting multiple, correlated genetic effects, whether effects for individual traits or sets of random regression coefficients to model trajectories. Depending on the magnitude of genetic correlation, a subset of the principal component generally suffices to capture the bulk of genetic variation. Corresponding estimates of genetic covariance matrices are more parsimonious, have reduced rank and are smoothed, with the number of parameters required to model the dispersion structure reduced from k(k + 1)/2 to m(2k - m + 1)/2 for k effects and m principal components. Estimation of these parameters, the largest eigenvalues and pertaining eigenvectors of the genetic covariance matrix, via restricted maximum likelihood using derivatives of the likelihood, is described. It is shown that reduced rank estimation can reduce computational requirements of multivariate analyses substantially. An application to the analysis of eight traits recorded via live ultrasound scanning of beef cattle is given. PMID:15588566

Case-Deletion Diagnostics for Maximum Likelihood Multipoint Quantitative Trait Locus Linkage Analysis

PubMed Central

Mendoza, Maria C.B.; Burns, Trudy L.; Jones, Michael P.

2009-01-01

Objectives Case-deletion diagnostic methods are tools that allow identification of influential observations that may affect parameter estimates and model fitting conclusions. The goal of this paper was to develop two case-deletion diagnostics, the exact case deletion (ECD) and the empirical influence function (EIF), for detecting outliers that can affect results of sib-pair maximum likelihood quantitative trait locus (QTL) linkage analysis. Methods Subroutines to compute the ECD and EIF were incorporated into the maximum likelihood QTL variance estimation components of the linkage analysis program MAPMAKER/SIBS. Performance of the diagnostics was compared in simulation studies that evaluated the proportion of outliers correctly identified (sensitivity), and the proportion of non-outliers correctly identified (specificity). Results Simulations involving nuclear family data sets with one outlier showed EIF sensitivities approximated ECD sensitivities well for outlier-affected parameters. Sensitivities were high, indicating the outlier was identified a high proportion of the time. Simulations also showed the enormous computational time advantage of the EIF. Diagnostics applied to body mass index in nuclear families detected observations influential on the lod score and model parameter estimates. Conclusions The EIF is a practical diagnostic tool that has the advantages of high sensitivity and quick computation. PMID:19172086

An empirical likelihood ratio test robust to individual heterogeneity for differential expression analysis of RNA-seq.

PubMed

Xu, Maoqi; Chen, Liang

2018-01-01

The individual sample heterogeneity is one of the biggest obstacles in biomarker identification for complex diseases such as cancers. Current statistical models to identify differentially expressed genes between disease and control groups often overlook the substantial human sample heterogeneity. Meanwhile, traditional nonparametric tests lose detailed data information and sacrifice the analysis power, although they are distribution free and robust to heterogeneity. Here, we propose an empirical likelihood ratio test with a mean-variance relationship constraint (ELTSeq) for the differential expression analysis of RNA sequencing (RNA-seq). As a distribution-free nonparametric model, ELTSeq handles individual heterogeneity by estimating an empirical probability for each observation without making any assumption about read-count distribution. It also incorporates a constraint for the read-count overdispersion, which is widely observed in RNA-seq data. ELTSeq demonstrates a significant improvement over existing methods such as edgeR, DESeq, t-tests, Wilcoxon tests and the classic empirical likelihood-ratio test when handling heterogeneous groups. It will significantly advance the transcriptomics studies of cancers and other complex disease. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A Comparison of Probabilistic and Deterministic Campaign Analysis for Human Space Exploration

NASA Technical Reports Server (NTRS)

Merrill, R. Gabe; Andraschko, Mark; Stromgren, Chel; Cirillo, Bill; Earle, Kevin; Goodliff, Kandyce

2008-01-01

Human space exploration is by its very nature an uncertain endeavor. Vehicle reliability, technology development risk, budgetary uncertainty, and launch uncertainty all contribute to stochasticity in an exploration scenario. However, traditional strategic analysis has been done in a deterministic manner, analyzing and optimizing the performance of a series of planned missions. History has shown that exploration scenarios rarely follow such a planned schedule. This paper describes a methodology to integrate deterministic and probabilistic analysis of scenarios in support of human space exploration. Probabilistic strategic analysis is used to simulate "possible" scenario outcomes, based upon the likelihood of occurrence of certain events and a set of pre-determined contingency rules. The results of the probabilistic analysis are compared to the nominal results from the deterministic analysis to evaluate the robustness of the scenario to adverse events and to test and optimize contingency planning.

A Study of Algorithms for Covariance Structure Analysis with Specific Comparisons Using Factor Analysis.

ERIC Educational Resources Information Center

Lee, S. Y.; Jennrich, R. I.

1979-01-01

A variety of algorithms for analyzing covariance structures are considered. Additionally, two methods of estimation, maximum likelihood, and weighted least squares are considered. Comparisons are made between these algorithms and factor analysis. (Author/JKS)

Pseudomonas aeruginosa dose response and bathing water infection.

PubMed

Roser, D J; van den Akker, B; Boase, S; Haas, C N; Ashbolt, N J; Rice, S A

2014-03-01

Pseudomonas aeruginosa is the opportunistic pathogen mostly implicated in folliculitis and acute otitis externa in pools and hot tubs. Nevertheless, infection risks remain poorly quantified. This paper reviews disease aetiologies and bacterial skin colonization science to advance dose-response theory development. Three model forms are identified for predicting disease likelihood from pathogen density. Two are based on Furumoto & Mickey's exponential 'single-hit' model and predict infection likelihood and severity (lesions/m2), respectively. 'Third-generation', mechanistic, dose-response algorithm development is additionally scoped. The proposed formulation integrates dispersion, epidermal interaction, and follicle invasion. The review also details uncertainties needing consideration which pertain to water quality, outbreaks, exposure time, infection sites, biofilms, cerumen, environmental factors (e.g. skin saturation, hydrodynamics), and whether P. aeruginosa is endogenous or exogenous. The review's findings are used to propose a conceptual infection model and identify research priorities including pool dose-response modelling, epidermis ecology and infection likelihood-based hygiene management.

A quantum framework for likelihood ratios

NASA Astrophysics Data System (ADS)

Bond, Rachael L.; He, Yang-Hui; Ormerod, Thomas C.

The ability to calculate precise likelihood ratios is fundamental to science, from Quantum Information Theory through to Quantum State Estimation. However, there is no assumption-free statistical methodology to achieve this. For instance, in the absence of data relating to covariate overlap, the widely used Bayes’ theorem either defaults to the marginal probability driven “naive Bayes’ classifier”, or requires the use of compensatory expectation-maximization techniques. This paper takes an information-theoretic approach in developing a new statistical formula for the calculation of likelihood ratios based on the principles of quantum entanglement, and demonstrates that Bayes’ theorem is a special case of a more general quantum mechanical expression.

Inferring the parameters of a Markov process from snapshots of the steady state

NASA Astrophysics Data System (ADS)

Dettmer, Simon L.; Berg, Johannes

2018-02-01

We seek to infer the parameters of an ergodic Markov process from samples taken independently from the steady state. Our focus is on non-equilibrium processes, where the steady state is not described by the Boltzmann measure, but is generally unknown and hard to compute, which prevents the application of established equilibrium inference methods. We propose a quantity we call propagator likelihood, which takes on the role of the likelihood in equilibrium processes. This propagator likelihood is based on fictitious transitions between those configurations of the system which occur in the samples. The propagator likelihood can be derived by minimising the relative entropy between the empirical distribution and a distribution generated by propagating the empirical distribution forward in time. Maximising the propagator likelihood leads to an efficient reconstruction of the parameters of the underlying model in different systems, both with discrete configurations and with continuous configurations. We apply the method to non-equilibrium models from statistical physics and theoretical biology, including the asymmetric simple exclusion process (ASEP), the kinetic Ising model, and replicator dynamics.

Fecal immunochemical test for predicting mucosal healing in ulcerative colitis patients: A systematic review and meta-analysis.

PubMed

Dai, Cong; Jiang, Min; Sun, Ming-Jun; Cao, Qin

2018-05-01

Fecal immunochemical test (FIT) is a promising marker for assessment of inflammatory bowel disease activity. However, the utility of FIT for predicting mucosal healing (MH) of ulcerative colitis (UC) patients has yet to be clearly demonstrated. The objective of our study was to perform a diagnostic test accuracy test meta-analysis evaluating the diagnostic accuracy of FIT in predicting MH of UC patients. We systematically searched the databases from inception to November 2017 that evaluated MH in UC. The methodological quality of each study was assessed according to the Quality Assessment of Diagnostic Accuracy Studies checklist. The extracted data were pooled using a summary receiver operating characteristic curve model. Random-effects model was used to summarize the diagnostic odds ratio, sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio. Six studies comprising 625 UC patients were included in the meta-analysis. The pooled sensitivity and specificity values for predicting MH in UC were 0.77 (95% confidence interval [CI], 0.72-0.81) and 0.81 (95% CI, 0.76-0.85), respectively. The FIT level had a high rule-in value (positive likelihood ratio, 3.79; 95% CI, 2.85-5.03) and a moderate rule-out value (negative likelihood ratio, 0.26; 95% CI, 0.16-0.43) for predicting MH in UC. The results of the receiver operating characteristic curve analysis (area under the curve, 0.88; standard error of the mean, 0.02) and diagnostic odds ratio (18.08; 95% CI, 9.57-34.13) also revealed improved discrimination for identifying MH in UC with FIT concentration. Our meta-analysis has found that FIT is a simple, reliable non-invasive marker for predicting MH in UC patients. © 2018 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Additive hazards regression and partial likelihood estimation for ecological monitoring data across space.

PubMed

Lin, Feng-Chang; Zhu, Jun

2012-01-01

We develop continuous-time models for the analysis of environmental or ecological monitoring data such that subjects are observed at multiple monitoring time points across space. Of particular interest are additive hazards regression models where the baseline hazard function can take on flexible forms. We consider time-varying covariates and take into account spatial dependence via autoregression in space and time. We develop statistical inference for the regression coefficients via partial likelihood. Asymptotic properties, including consistency and asymptotic normality, are established for parameter estimates under suitable regularity conditions. Feasible algorithms utilizing existing statistical software packages are developed for computation. We also consider a simpler additive hazards model with homogeneous baseline hazard and develop hypothesis testing for homogeneity. A simulation study demonstrates that the statistical inference using partial likelihood has sound finite-sample properties and offers a viable alternative to maximum likelihood estimation. For illustration, we analyze data from an ecological study that monitors bark beetle colonization of red pines in a plantation of Wisconsin.

Planck intermediate results. XVI. Profile likelihoods for cosmological parameters

NASA Astrophysics Data System (ADS)

Planck Collaboration; Ade, P. A. R.; Aghanim, N.; Arnaud, M.; Ashdown, M.; Aumont, J.; Baccigalupi, C.; Banday, A. J.; Barreiro, R. B.; Bartlett, J. G.; Battaner, E.; Benabed, K.; Benoit-Lévy, A.; Bernard, J.-P.; Bersanelli, M.; Bielewicz, P.; Bobin, J.; Bonaldi, A.; Bond, J. R.; Bouchet, F. R.; Burigana, C.; Cardoso, J.-F.; Catalano, A.; Chamballu, A.; Chiang, H. C.; Christensen, P. R.; Clements, D. L.; Colombi, S.; Colombo, L. P. L.; Couchot, F.; Cuttaia, F.; Danese, L.; Davies, R. D.; Davis, R. J.; de Bernardis, P.; de Rosa, A.; de Zotti, G.; Delabrouille, J.; Dickinson, C.; Diego, J. M.; Dole, H.; Donzelli, S.; Doré, O.; Douspis, M.; Dupac, X.; Enßlin, T. A.; Eriksen, H. K.; Finelli, F.; Forni, O.; Frailis, M.; Franceschi, E.; Galeotta, S.; Galli, S.; Ganga, K.; Giard, M.; Giraud-Héraud, Y.; González-Nuevo, J.; Górski, K. M.; Gregorio, A.; Gruppuso, A.; Hansen, F. K.; Harrison, D. L.; Henrot-Versillé, S.; Hernández-Monteagudo, C.; Herranz, D.; Hildebrandt, S. R.; Hivon, E.; Hobson, M.; Holmes, W. A.; Hornstrup, A.; Hovest, W.; Huffenberger, K. M.; Jaffe, A. H.; Jaffe, T. R.; Jones, W. C.; Juvela, M.; Keihänen, E.; Keskitalo, R.; Kisner, T. S.; Kneissl, R.; Knoche, J.; Knox, L.; Kunz, M.; Kurki-Suonio, H.; Lagache, G.; Lähteenmäki, A.; Lamarre, J.-M.; Lasenby, A.; Lawrence, C. R.; Leonardi, R.; Liddle, A.; Liguori, M.; Lilje, P. B.; Linden-Vørnle, M.; López-Caniego, M.; Lubin, P. M.; Macías-Pérez, J. F.; Maffei, B.; Maino, D.; Mandolesi, N.; Maris, M.; Martin, P. G.; Martínez-González, E.; Masi, S.; Massardi, M.; Matarrese, S.; Mazzotta, P.; Melchiorri, A.; Mendes, L.; Mennella, A.; Migliaccio, M.; Mitra, S.; Miville-Deschênes, M.-A.; Moneti, A.; Montier, L.; Morgante, G.; Munshi, D.; Murphy, J. A.; Naselsky, P.; Nati, F.; Natoli, P.; Noviello, F.; Novikov, D.; Novikov, I.; Oxborrow, C. A.; Pagano, L.; Pajot, F.; Paoletti, D.; Pasian, F.; Perdereau, O.; Perotto, L.; Perrotta, F.; Pettorino, V.; Piacentini, F.; Piat, M.; Pierpaoli, E.; Pietrobon, D.; Plaszczynski∗, S.; Pointecouteau, E.; Polenta, G.; Popa, L.; Pratt, G. W.; Puget, J.-L.; Rachen, J. P.; Rebolo, R.; Reinecke, M.; Remazeilles, M.; Renault, C.; Ricciardi, S.; Riller, T.; Ristorcelli, I.; Rocha, G.; Rosset, C.; Roudier, G.; Rouillé d'Orfeuil, B.; Rubiño-Martín, J. A.; Rusholme, B.; Sandri, M.; Savelainen, M.; Savini, G.; Spencer, L. D.; Spinelli, M.; Starck, J.-L.; Sureau, F.; Sutton, D.; Suur-Uski, A.-S.; Sygnet, J.-F.; Tauber, J. A.; Terenzi, L.; Toffolatti, L.; Tomasi, M.; Tristram, M.; Tucci, M.; Umana, G.; Valenziano, L.; Valiviita, J.; Van Tent, B.; Vielva, P.; Villa, F.; Wade, L. A.; Wandelt, B. D.; White, M.; Yvon, D.; Zacchei, A.; Zonca, A.

2014-06-01

We explore the 2013 Planck likelihood function with a high-precision multi-dimensional minimizer (Minuit). This allows a refinement of the ΛCDM best-fit solution with respect to previously-released results, and the construction of frequentist confidence intervals using profile likelihoods. The agreement with the cosmological results from the Bayesian framework is excellent, demonstrating the robustness of the Planck results to the statistical methodology. We investigate the inclusion of neutrino masses, where more significant differences may appear due to the non-Gaussian nature of the posterior mass distribution. By applying the Feldman-Cousins prescription, we again obtain results very similar to those of the Bayesian methodology. However, the profile-likelihood analysis of the cosmic microwave background (CMB) combination (Planck+WP+highL) reveals a minimum well within the unphysical negative-mass region. We show that inclusion of the Planck CMB-lensing information regularizes this issue, and provide a robust frequentist upper limit ∑ mν ≤ 0.26 eV (95% confidence) from the CMB+lensing+BAO data combination.

Statistics and Discoveries at the LHC (1/4)

ScienceCinema

Cowan, Glen

2018-02-09

The lectures will give an introduction to statistics as applied in particle physics and will provide all the necessary basics for data analysis at the LHC. Special emphasis will be placed on the the problems and questions that arise when searching for new phenomena, including p-values, discovery significance, limit setting procedures, treatment of small signals in the presence of large backgrounds. Specific issues that will be addressed include the advantages and drawbacks of different statistical test procedures (cut-based, likelihood-ratio, etc.), the look-elsewhere effect and treatment of systematic uncertainties.

Statistics and Discoveries at the LHC (3/4)

ScienceCinema

Cowan, Glen

2018-02-19

The lectures will give an introduction to statistics as applied in particle physics and will provide all the necessary basics for data analysis at the LHC. Special emphasis will be placed on the the problems and questions that arise when searching for new phenomena, including p-values, discovery significance, limit setting procedures, treatment of small signals in the presence of large backgrounds. Specific issues that will be addressed include the advantages and drawbacks of different statistical test procedures (cut-based, likelihood-ratio, etc.), the look-elsewhere effect and treatment of systematic uncertainties.

Statistics and Discoveries at the LHC (4/4)

ScienceCinema

Cowan, Glen

2018-05-22

The lectures will give an introduction to statistics as applied in particle physics and will provide all the necessary basics for data analysis at the LHC. Special emphasis will be placed on the the problems and questions that arise when searching for new phenomena, including p-values, discovery significance, limit setting procedures, treatment of small signals in the presence of large backgrounds. Specific issues that will be addressed include the advantages and drawbacks of different statistical test procedures (cut-based, likelihood-ratio, etc.), the look-elsewhere effect and treatment of systematic uncertainties.

Statistics and Discoveries at the LHC (2/4)

ScienceCinema

Cowan, Glen

2018-04-26

The lectures will give an introduction to statistics as applied in particle physics and will provide all the necessary basics for data analysis at the LHC. Special emphasis will be placed on the the problems and questions that arise when searching for new phenomena, including p-values, discovery significance, limit setting procedures, treatment of small signals in the presence of large backgrounds. Specific issues that will be addressed include the advantages and drawbacks of different statistical test procedures (cut-based, likelihood-ratio, etc.), the look-elsewhere effect and treatment of systematic uncertainties.

A Bayesian approach to parameter and reliability estimation in the Poisson distribution.

NASA Technical Reports Server (NTRS)

Canavos, G. C.

1972-01-01

For life testing procedures, a Bayesian analysis is developed with respect to a random intensity parameter in the Poisson distribution. Bayes estimators are derived for the Poisson parameter and the reliability function based on uniform and gamma prior distributions of that parameter. A Monte Carlo procedure is implemented to make possible an empirical mean-squared error comparison between Bayes and existing minimum variance unbiased, as well as maximum likelihood, estimators. As expected, the Bayes estimators have mean-squared errors that are appreciably smaller than those of the other two.

Likelihood analysis of supersymmetric SU(5) GUTs

DOE PAGES

Bagnaschi, Emanuele; Costa, J. C.; Sakurai, K.; ...

2017-02-16

Here, we perform a likelihood analysis of the constraints from accelerator experiments and astrophysical observations on supersymmetric (SUSY) models with SU(5) boundary conditions on soft SUSY-breaking parameters at the GUT scale. The parameter space of the models studied has 7 parameters: a universal gaugino massmore » $$m_{1/2}$$, distinct masses for the scalar partners of matter fermions in five- and ten-dimensional representations of SU(5), $$m_5$$ and $$m_{10}$$, and for the $$\\mathbf{5}$$ and $$\\mathbf{\\bar 5}$$ Higgs representations $$m_{H_u}$$ and $$m_{H_d}$$, a universal trilinear soft SUSY-breaking parameter $$A_0$$, and the ratio of Higgs vevs $$\\tan \\beta$$. In addition to previous constraints from direct sparticle searches, low-energy and flavour observables, we incorporate constraints based on preliminary results from 13 TeV LHC searches for jets + MET events and long-lived particles, as well as the latest PandaX-II and LUX searches for direct Dark Matter detection. In addition to previously-identified mechanisms for bringing the supersymmetric relic density into the range allowed by cosmology, we identify a novel $${\\tilde u_R}/{\\tilde c_R} - \\tilde{\\chi}^0_1$$ coannihilation mechanism that appears in the supersymmetric SU(5) GUT model and discuss the role of $${\\tilde \

Constrained inference in mixed-effects models for longitudinal data with application to hearing loss.

PubMed

Davidov, Ori; Rosen, Sophia

2011-04-01

In medical studies, endpoints are often measured for each patient longitudinally. The mixed-effects model has been a useful tool for the analysis of such data. There are situations in which the parameters of the model are subject to some restrictions or constraints. For example, in hearing loss studies, we expect hearing to deteriorate with time. This means that hearing thresholds which reflect hearing acuity will, on average, increase over time. Therefore, the regression coefficients associated with the mean effect of time on hearing ability will be constrained. Such constraints should be accounted for in the analysis. We propose maximum likelihood estimation procedures, based on the expectation-conditional maximization either algorithm, to estimate the parameters of the model while accounting for the constraints on them. The proposed methods improve, in terms of mean square error, on the unconstrained estimators. In some settings, the improvement may be substantial. Hypotheses testing procedures that incorporate the constraints are developed. Specifically, likelihood ratio, Wald, and score tests are proposed and investigated. Their empirical significance levels and power are studied using simulations. It is shown that incorporating the constraints improves the mean squared error of the estimates and the power of the tests. These improvements may be substantial. The methodology is used to analyze a hearing loss study.

Gray matter atrophy in narcolepsy: An activation likelihood estimation meta-analysis.

PubMed

Weng, Hsu-Huei; Chen, Chih-Feng; Tsai, Yuan-Hsiung; Wu, Chih-Ying; Lee, Meng; Lin, Yu-Ching; Yang, Cheng-Ta; Tsai, Ying-Huang; Yang, Chun-Yuh

2015-12-01

The authors reviewed the literature on the use of voxel-based morphometry (VBM) in narcolepsy magnetic resonance imaging (MRI) studies via the use of a meta-analysis of neuroimaging to identify concordant and specific structural deficits in patients with narcolepsy as compared with healthy subjects. We used PubMed to retrieve articles published between January 2000 and March 2014. The authors included all VBM research on narcolepsy and compared the findings of the studies by using gray matter volume (GMV) or gray matter concentration (GMC) to index differences in gray matter. Stereotactic data were extracted from 8 VBM studies of 149 narcoleptic patients and 162 control subjects. We applied activation likelihood estimation (ALE) technique and found significant regional gray matter reduction in the bilateral hypothalamus, thalamus, globus pallidus, extending to nucleus accumbens (NAcc) and anterior cingulate cortex (ACC), left mid orbital and rectal gyri (BAs 10 and 11), right inferior frontal gyrus (BA 47), and the right superior temporal gyrus (BA 41) in patients with narcolepsy. The significant gray matter deficits in narcoleptic patients occurred in the bilateral hypothalamus and frontotemporal regions, which may be related to the emotional processing abnormalities and orexin/hypocretin pathway common among populations of patients with narcolepsy. Copyright © 2015. Published by Elsevier Ltd.

An automated multi-scale network-based scheme for detection and location of seismic sources

NASA Astrophysics Data System (ADS)

Poiata, N.; Aden-Antoniow, F.; Satriano, C.; Bernard, P.; Vilotte, J. P.; Obara, K.

2017-12-01

We present a recently developed method - BackTrackBB (Poiata et al. 2016) - allowing to image energy radiation from different seismic sources (e.g., earthquakes, LFEs, tremors) in different tectonic environments using continuous seismic records. The method exploits multi-scale frequency-selective coherence in the wave field, recorded by regional seismic networks or local arrays. The detection and location scheme is based on space-time reconstruction of the seismic sources through an imaging function built from the sum of station-pair time-delay likelihood functions, projected onto theoretical 3D time-delay grids. This imaging function is interpreted as the location likelihood of the seismic source. A signal pre-processing step constructs a multi-band statistical representation of the non stationary signal, i.e. time series, by means of higher-order statistics or energy envelope characteristic functions. Such signal-processing is designed to detect in time signal transients - of different scales and a priori unknown predominant frequency - potentially associated with a variety of sources (e.g., earthquakes, LFE, tremors), and to improve the performance and the robustness of the detection-and-location location step. The initial detection-location, based on a single phase analysis with the P- or S-phase only, can then be improved recursively in a station selection scheme. This scheme - exploiting the 3-component records - makes use of P- and S-phase characteristic functions, extracted after a polarization analysis of the event waveforms, and combines the single phase imaging functions with the S-P differential imaging functions. The performance of the method is demonstrated here in different tectonic environments: (1) analysis of the one year long precursory phase of 2014 Iquique earthquake in Chile; (2) detection and location of tectonic tremor sources and low-frequency earthquakes during the multiple episodes of tectonic tremor activity in southwestern Japan.

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.

Beyond valence in the perception of likelihood: the role of emotion specificity.

PubMed

DeSteno, D; Petty, R E; Wegener, D T; Rucker, D D

2000-03-01

Positive and negative moods have been shown to increase likelihood estimates of future events matching these states in valence (e.g., E. J. Johnson & A. Tversky, 1983). In the present article, 4 studies provide evidence that this congruency bias (a) is not limited to valence but functions in an emotion-specific manner, (b) derives from the informational value of emotions, and (c) is not the inevitable outcome of likelihood assessment under heightened emotion. Specifically, Study 1 demonstrates that sadness and anger, 2 distinct, negative emotions, differentially bias likelihood estimates of sad and angering events. Studies 2 and 3 replicate this finding in addition to supporting an emotion-as-information (cf. N. Schwarz & G. L. Clore, 1983), as opposed to a memory-based, mediating process for the bias. Finally, Study 4 shows that when the source of the emotion is salient, a reversal of the bias can occur given greater cognitive effort aimed at accuracy.

A blood-based screening tool for Alzheimer's disease that spans serum and plasma: findings from TARC and ADNI.

PubMed

O'Bryant, Sid E; Xiao, Guanghua; Barber, Robert; Huebinger, Ryan; Wilhelmsen, Kirk; Edwards, Melissa; Graff-Radford, Neill; Doody, Rachelle; Diaz-Arrastia, Ramon

2011-01-01

There is no rapid and cost effective tool that can be implemented as a front-line screening tool for Alzheimer's disease (AD) at the population level. To generate and cross-validate a blood-based screener for AD that yields acceptable accuracy across both serum and plasma. Analysis of serum biomarker proteins were conducted on 197 Alzheimer's disease (AD) participants and 199 control participants from the Texas Alzheimer's Research Consortium (TARC) with further analysis conducted on plasma proteins from 112 AD and 52 control participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The full algorithm was derived from a biomarker risk score, clinical lab (glucose, triglycerides, total cholesterol, homocysteine), and demographic (age, gender, education, APOE*E4 status) data. Alzheimer's disease. 11 proteins met our criteria and were utilized for the biomarker risk score. The random forest (RF) biomarker risk score from the TARC serum samples (training set) yielded adequate accuracy in the ADNI plasma sample (training set) (AUC = 0.70, sensitivity (SN) = 0.54 and specificity (SP) = 0.78), which was below that obtained from ADNI cerebral spinal fluid (CSF) analyses (t-tau/Aβ ratio AUC = 0.92). However, the full algorithm yielded excellent accuracy (AUC = 0.88, SN = 0.75, and SP = 0.91). The likelihood ratio of having AD based on a positive test finding (LR+) = 7.03 (SE = 1.17; 95% CI = 4.49-14.47), the likelihood ratio of not having AD based on the algorithm (LR-) = 3.55 (SE = 1.15; 2.22-5.71), and the odds ratio of AD were calculated in the ADNI cohort (OR) = 28.70 (1.55; 95% CI = 11.86-69.47). It is possible to create a blood-based screening algorithm that works across both serum and plasma that provides a comparable screening accuracy to that obtained from CSF analyses.

Use of Drop-In Clinic Versus Appointment-Based Care for LGBT Youth: Influences on the Likelihood to Access Different Health-Care Structures.

PubMed

Newman, Bernie S; Passidomo, Kim; Gormley, Kate; Manley, Alecia

2014-06-01

The structure of health-care service delivery can address barriers that make it difficult for lesbian, gay, bisexual, and transgender (LGBT) adolescents to use health services. This study explores the differences among youth who access care in one of two service delivery structures in an LGBT health-care center: the drop-in clinic or the traditional appointment-based model. Analysis of 578 records of LGBT and straight youth (aged 14-24) who accessed health care either through a drop-in clinic or appointment-based care within the first year of offering the drop-in clinic reveals patterns of use when both models are available. We studied demographic variables previously shown to be associated with general health-care access to determine how each correlated with a tendency to use the drop-in structure versus routine appointments. Once the covariates were identified, we conducted a logistic regression analysis to identify its association with likelihood of using the drop-in clinic. Insurance status, housing stability, education, race, and gender identity were most strongly associated with the type of clinic used. Youth who relied on Medicaid, those in unstable housing, and African Americans were most likely to use the drop-in clinic. Transgender youth and those with higher education were more likely to use the appointment-based clinic. Although sexual orientation and HIV status were not related to type of clinic used, youth who were HIV positive used the appointment-based clinic more frequently. Both routes to health care served distinct populations who often experience barriers to accessible, affordable, and knowledgeable care. Further study of the factors related to accessing health care may clarify the extent to which drop-in hours in a youth-friendly context may increase the use of health care by the most socially marginalized youth.

Spatial hydrological drought characteristics in Karkheh River basin, southwest Iran using copulas

NASA Astrophysics Data System (ADS)

Dodangeh, Esmaeel; Shahedi, Kaka; Shiau, Jenq-Tzong; MirAkbari, Maryam

2017-08-01

Investigation on drought characteristics such as severity, duration, and frequency is crucial for water resources planning and management in a river basin. While the methodology for multivariate drought frequency analysis is well established by applying the copulas, the estimation on the associated parameters by various parameter estimation methods and the effects on the obtained results have not yet been investigated. This research aims at conducting a comparative analysis between the maximum likelihood parametric and non-parametric method of the Kendall τ estimation method for copulas parameter estimation. The methods were employed to study joint severity-duration probability and recurrence intervals in Karkheh River basin (southwest Iran) which is facing severe water-deficit problems. Daily streamflow data at three hydrological gauging stations (Tang Sazbon, Huleilan and Polchehr) near the Karkheh dam were used to draw flow duration curves (FDC) of these three stations. The Q_{75} index extracted from the FDC were set as threshold level to abstract drought characteristics such as drought duration and severity on the basis of the run theory. Drought duration and severity were separately modeled using the univariate probabilistic distributions and gamma-GEV, LN2-exponential, and LN2-gamma were selected as the best paired drought severity-duration inputs for copulas according to the Akaike Information Criteria (AIC), Kolmogorov-Smirnov and chi-square tests. Archimedean Clayton, Frank, and extreme value Gumbel copulas were employed to construct joint cumulative distribution functions (JCDF) of droughts for each station. Frank copula at Tang Sazbon and Gumbel at Huleilan and Polchehr stations were identified as the best copulas based on the performance evaluation criteria including AIC, BIC, log-likelihood and root mean square error (RMSE) values. Based on the RMSE values, nonparametric Kendall-τ is preferred to the parametric maximum likelihood estimation method. The results showed greater drought return periods by the parametric ML method in comparison to the nonparametric Kendall τ estimation method. The results also showed that stations located in tributaries (Huleilan and Polchehr) have close return periods, while the station along the main river (Tang Sazbon) has the smaller return periods for the drought events with identical drought duration and severity.

Dynamically heterogenous partitions and phylogenetic inference: an evaluation of analytical strategies with cytochrome b and ND6 gene sequences in cranes.

PubMed

Krajewski, C; Fain, M G; Buckley, L; King, D G

1999-11-01

ki ctes over whether molecular sequence data should be partitioned for phylogenetic analysis often confound two types of heterogeneity among partitions. We distinguish historical heterogeneity (i.e., different partitions have different evolutionary relationships) from dynamic heterogeneity (i.e., different partitions show different patterns of sequence evolution) and explore the impact of the latter on phylogenetic accuracy and precision with a two-gene, mitochondrial data set for cranes. The well-established phylogeny of cranes allows us to contrast tree-based estimates of relevant parameter values with estimates based on pairwise comparisons and to ascertain the effects of incorporating different amounts of process information into phylogenetic estimates. We show that codon positions in the cytochrome b and NADH dehydrogenase subunit 6 genes are dynamically heterogenous under both Poisson and invariable-sites + gamma-rates versions of the F84 model and that heterogeneity includes variation in base composition and transition bias as well as substitution rate. Estimates of transition-bias and relative-rate parameters from pairwise sequence comparisons were comparable to those obtained as tree-based maximum likelihood estimates. Neither rate-category nor mixed-model partitioning strategies resulted in a loss of phylogenetic precision relative to unpartitioned analyses. We suggest that weighted-average distances provide a computationally feasible alternative to direct maximum likelihood estimates of phylogeny for mixed-model analyses of large, dynamically heterogenous data sets. Copyright 1999 Academic Press.

Estimation of parameters of dose volume models and their confidence limits

NASA Astrophysics Data System (ADS)

van Luijk, P.; Delvigne, T. C.; Schilstra, C.; Schippers, J. M.

2003-07-01

Predictions of the normal-tissue complication probability (NTCP) for the ranking of treatment plans are based on fits of dose-volume models to clinical and/or experimental data. In the literature several different fit methods are used. In this work frequently used methods and techniques to fit NTCP models to dose response data for establishing dose-volume effects, are discussed. The techniques are tested for their usability with dose-volume data and NTCP models. Different methods to estimate the confidence intervals of the model parameters are part of this study. From a critical-volume (CV) model with biologically realistic parameters a primary dataset was generated, serving as the reference for this study and describable by the NTCP model. The CV model was fitted to this dataset. From the resulting parameters and the CV model, 1000 secondary datasets were generated by Monte Carlo simulation. All secondary datasets were fitted to obtain 1000 parameter sets of the CV model. Thus the 'real' spread in fit results due to statistical spreading in the data is obtained and has been compared with estimates of the confidence intervals obtained by different methods applied to the primary dataset. The confidence limits of the parameters of one dataset were estimated using the methods, employing the covariance matrix, the jackknife method and directly from the likelihood landscape. These results were compared with the spread of the parameters, obtained from the secondary parameter sets. For the estimation of confidence intervals on NTCP predictions, three methods were tested. Firstly, propagation of errors using the covariance matrix was used. Secondly, the meaning of the width of a bundle of curves that resulted from parameters that were within the one standard deviation region in the likelihood space was investigated. Thirdly, many parameter sets and their likelihood were used to create a likelihood-weighted probability distribution of the NTCP. It is concluded that for the type of dose response data used here, only a full likelihood analysis will produce reliable results. The often-used approximations, such as the usage of the covariance matrix, produce inconsistent confidence limits on both the parameter sets and the resulting NTCP values.

Treatment utilization and outcomes in elderly patients with locally advanced esophageal carcinoma: a review of the National Cancer Database.

PubMed

Vlacich, Gregory; Samson, Pamela P; Perkins, Stephanie M; Roach, Michael C; Parikh, Parag J; Bradley, Jeffrey D; Lockhart, A Craig; Puri, Varun; Meyers, Bryan F; Kozower, Benjamin; Robinson, Cliff G

2017-12-01

For elderly patients with locally advanced esophageal cancer, therapeutic approaches and outcomes in a modern cohort are not well characterized. Patients ≥70 years old with clinical stage II and III esophageal cancer diagnosed between 1998 and 2012 were identified from the National Cancer Database and stratified based on treatment type. Variables associated with treatment utilization were evaluated using logistic regression and survival evaluated using Cox proportional hazards analysis. Propensity matching (1:1) was performed to help account for selection bias. A total of 21,593 patients were identified. Median and maximum ages were 77 and 90, respectively. Treatment included palliative therapy (24.3%), chemoradiation (37.1%), trimodality therapy (10.0%), esophagectomy alone (5.6%), or no therapy (12.9%). Age ≥80 (OR 0.73), female gender (OR 0.81), Charlson-Deyo comorbidity score ≥2 (OR 0.82), and high-volume centers (OR 0.83) were associated with a decreased likelihood of palliative therapy versus no treatment. Age ≥80 (OR 0.79) and Clinical Stage III (OR 0.33) were associated with a decreased likelihood, while adenocarcinoma histology (OR 1.33) and nonacademic cancer centers (OR 3.9), an increased likelihood of esophagectomy alone compared to definitive chemoradiation. Age ≥80 (OR 0.15), female gender (OR 0.80), and non-Caucasian race (OR 0.63) were associated with a decreased likelihood, while adenocarcinoma histology (OR 2.10) and high-volume centers (OR 2.34), an increased likelihood of trimodality therapy compared to definitive chemoradiation. Each treatment type demonstrated improved survival compared to no therapy: palliative treatment (HR 0.49) to trimodality therapy (HR 0.25) with significance between all groups. Any therapy, including palliative care, was associated with improved survival; however, subsets of elderly patients with locally advanced esophageal cancer are less likely to receive aggressive therapy. Care should be taken to not unnecessarily deprive these individuals of treatment that may improve survival. © 2017 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Statistical parsimony networks and species assemblages in Cephalotrichid nemerteans (nemertea).

PubMed

Chen, Haixia; Strand, Malin; Norenburg, Jon L; Sun, Shichun; Kajihara, Hiroshi; Chernyshev, Alexey V; Maslakova, Svetlana A; Sundberg, Per

2010-09-21

It has been suggested that statistical parsimony network analysis could be used to get an indication of species represented in a set of nucleotide data, and the approach has been used to discuss species boundaries in some taxa. Based on 635 base pairs of the mitochondrial protein-coding gene cytochrome c oxidase I (COI), we analyzed 152 nemertean specimens using statistical parsimony network analysis with the connection probability set to 95%. The analysis revealed 15 distinct networks together with seven singletons. Statistical parsimony yielded three networks supporting the species status of Cephalothrix rufifrons, C. major and C. spiralis as they currently have been delineated by morphological characters and geographical location. Many other networks contained haplotypes from nearby geographical locations. Cladistic structure by maximum likelihood analysis overall supported the network analysis, but indicated a false positive result where subnetworks should have been connected into one network/species. This probably is caused by undersampling of the intraspecific haplotype diversity. Statistical parsimony network analysis provides a rapid and useful tool for detecting possible undescribed/cryptic species among cephalotrichid nemerteans based on COI gene. It should be combined with phylogenetic analysis to get indications of false positive results, i.e., subnetworks that would have been connected with more extensive haplotype sampling.

FPGA Acceleration of the phylogenetic likelihood function for Bayesian MCMC inference methods.

PubMed

Zierke, Stephanie; Bakos, Jason D

2010-04-12

Likelihood (ML)-based phylogenetic inference has become a popular method for estimating the evolutionary relationships among species based on genomic sequence data. This method is used in applications such as RAxML, GARLI, MrBayes, PAML, and PAUP. The Phylogenetic Likelihood Function (PLF) is an important kernel computation for this method. The PLF consists of a loop with no conditional behavior or dependencies between iterations. As such it contains a high potential for exploiting parallelism using micro-architectural techniques. In this paper, we describe a technique for mapping the PLF and supporting logic onto a Field Programmable Gate Array (FPGA)-based co-processor. By leveraging the FPGA's on-chip DSP modules and the high-bandwidth local memory attached to the FPGA, the resultant co-processor can accelerate ML-based methods and outperform state-of-the-art multi-core processors. We use the MrBayes 3 tool as a framework for designing our co-processor. For large datasets, we estimate that our accelerated MrBayes, if run on a current-generation FPGA, achieves a 10x speedup relative to software running on a state-of-the-art server-class microprocessor. The FPGA-based implementation achieves its performance by deeply pipelining the likelihood computations, performing multiple floating-point operations in parallel, and through a natural log approximation that is chosen specifically to leverage a deeply pipelined custom architecture. Heterogeneous computing, which combines general-purpose processors with special-purpose co-processors such as FPGAs and GPUs, is a promising approach for high-performance phylogeny inference as shown by the growing body of literature in this field. FPGAs in particular are well-suited for this task because of their low power consumption as compared to many-core processors and Graphics Processor Units (GPUs).

Maximum likelihood convolutional decoding (MCD) performance due to system losses

NASA Technical Reports Server (NTRS)

Webster, L.

1976-01-01

A model for predicting the computational performance of a maximum likelihood convolutional decoder (MCD) operating in a noisy carrier reference environment is described. This model is used to develop a subroutine that will be utilized by the Telemetry Analysis Program to compute the MCD bit error rate. When this computational model is averaged over noisy reference phase errors using a high-rate interpolation scheme, the results are found to agree quite favorably with experimental measurements.

Multifactorial Likelihood Assessment of BRCA1 and BRCA2 Missense Variants Confirms That BRCA1:c.122A>G(p.His41Arg) Is a Pathogenic Mutation

PubMed Central

Whiley, Phillip J.; Parsons, Michael T.; Leary, Jennifer; Tucker, Kathy; Warwick, Linda; Dopita, Belinda; Thorne, Heather; Lakhani, Sunil R.; Goldgar, David E.; Brown, Melissa A.; Spurdle, Amanda B.

2014-01-01

Rare exonic, non-truncating variants in known cancer susceptibility genes such as BRCA1 and BRCA2 are problematic for genetic counseling and clinical management of relevant families. This study used multifactorial likelihood analysis and/or bioinformatically-directed mRNA assays to assess pathogenicity of 19 BRCA1 or BRCA2 variants identified following patient referral to clinical genetic services. Two variants were considered to be pathogenic (Class 5). BRCA1:c.4484G> C(p.Arg1495Thr) was shown to result in aberrant mRNA transcripts predicted to encode truncated proteins. The BRCA1:c.122A>G(p.His41Arg) RING-domain variant was found from multifactorial likelihood analysis to have a posterior probability of pathogenicity of 0.995, a result consistent with existing protein functional assay data indicating lost BARD1 binding and ubiquitin ligase activity. Of the remaining variants, seven were determined to be not clinically significant (Class 1), nine were likely not pathogenic (Class 2), and one was uncertain (Class 3).These results have implications for genetic counseling and medical management of families carrying these specific variants. They also provide additional multifactorial likelihood variant classifications as reference to evaluate the sensitivity and specificity of bioinformatic prediction tools and/or functional assay data in future studies. PMID:24489791

An Activation Likelihood Estimation Meta-Analysis Study of Simple Motor Movements in Older and Young Adults

PubMed Central

Turesky, Ted K.; Turkeltaub, Peter E.; Eden, Guinevere F.

2016-01-01

The functional neuroanatomy of finger movements has been characterized with neuroimaging in young adults. However, less is known about the aging motor system. Several studies have contrasted movement-related activity in older versus young adults, but there is inconsistency among their findings. To address this, we conducted an activation likelihood estimation (ALE) meta-analysis on within-group data from older adults and young adults performing regularly paced right-hand finger movement tasks in response to external stimuli. We hypothesized that older adults would show a greater likelihood of activation in right cortical motor areas (i.e., ipsilateral to the side of movement) compared to young adults. ALE maps were examined for conjunction and between-group differences. Older adults showed overlapping likelihoods of activation with young adults in left primary sensorimotor cortex (SM1), bilateral supplementary motor area, bilateral insula, left thalamus, and right anterior cerebellum. Their ALE map differed from that of the young adults in right SM1 (extending into dorsal premotor cortex), right supramarginal gyrus, medial premotor cortex, and right posterior cerebellum. The finding that older adults uniquely use ipsilateral regions for right-hand finger movements and show age-dependent modulations in regions recruited by both age groups provides a foundation by which to understand age-related motor decline and motor disorders. PMID:27799910

Effect of symptom-based risk stratification on the costs of managing patients with chronic rhinosinusitis symptoms.

PubMed

Tan, Bruce K; Lu, Guanning; Kwasny, Mary J; Hsueh, Wayne D; Shintani-Smith, Stephanie; Conley, David B; Chandra, Rakesh K; Kern, Robert C; Leung, Randy

2013-11-01

Current symptom criteria poorly predict a diagnosis of chronic rhinosinusitis (CRS) resulting in excessive treatment of patients with presumed CRS. The objective of this study was analyze the positive predictive value of individual symptoms, or symptoms in combination, in patients with CRS symptoms and examine the costs of the subsequent diagnostic algorithm using a decision tree-based cost analysis. We analyzed previously collected patient-reported symptoms from a cross-sectional study of patients who had received a computed tomography (CT) scan of their sinuses at a tertiary care otolaryngology clinic for evaluation of CRS symptoms to calculate the positive predictive value of individual symptoms. Classification and regression tree (CART) analysis then optimized combinations of symptoms and thresholds to identify CRS patients. The calculated positive predictive values were applied to a previously developed decision tree that compared an upfront CT (uCT) algorithm against an empiric medical therapy (EMT) algorithm with further analysis that considered the availability of point of care (POC) imaging. The positive predictive value of individual symptoms ranged from 0.21 for patients reporting forehead pain and to 0.69 for patients reporting hyposmia. The CART model constructed a dichotomous model based on forehead pain, maxillary pain, hyposmia, nasal discharge, and facial pain (C-statistic 0.83). If POC CT were available, median costs ($64-$415) favored using the upfront CT for all individual symptoms. If POC CT was unavailable, median costs favored uCT for most symptoms except intercanthal pain (-$15), hyposmia (-$100), and discolored nasal discharge (-$24), although these symptoms became equivocal on cost sensitivity analysis. The three-tiered CART model could subcategorize patients into tiers where uCT was always favorable (median costs: $332-$504) and others for which EMT was always favorable (median costs -$121 to -$275). The uCT algorithm was always more costly if the nasal endoscopy was positive. Among patients with classic CRS symptoms, the frequency of individual symptoms varied the likelihood of a CRS diagnosis marginally. Only hyposmia, the absence of facial pain, and discolored discharge sufficiently increased the likelihood of diagnosis to potentially make EMT less costly. The development of an evidence-based, multisymptom-based risk stratification model could substantially affect the management costs of the subsequent diagnostic algorithm. © 2013 ARS-AAOA, LLC.

Glutamate receptor-channel gating. Maximum likelihood analysis of gigaohm seal recordings from locust muscle.

PubMed Central

Bates, S E; Sansom, M S; Ball, F G; Ramsey, R L; Usherwood, P N

1990-01-01

Gigaohm recordings have been made from glutamate receptor channels in excised, outside-out patches of collagenase-treated locust muscle membrane. The channels in the excised patches exhibit the kinetic state switching first seen in megaohm recordings from intact muscle fibers. Analysis of channel dwell time distributions reveals that the gating mechanism contains at least four open states and at least four closed states. Dwell time autocorrelation function analysis shows that there are at least three gateways linking the open states of the channel with the closed states. A maximum likelihood procedure has been used to fit six different gating models to the single channel data. Of these models, a cooperative model yields the best fit, and accurately predicts most features of the observed channel gating kinetics. PMID:1696510

Multi-Contrast Multi-Atlas Parcellation of Diffusion Tensor Imaging of the Human Brain

PubMed Central

Tang, Xiaoying; Yoshida, Shoko; Hsu, John; Huisman, Thierry A. G. M.; Faria, Andreia V.; Oishi, Kenichi; Kutten, Kwame; Poretti, Andrea; Li, Yue; Miller, Michael I.; Mori, Susumu

2014-01-01

In this paper, we propose a novel method for parcellating the human brain into 193 anatomical structures based on diffusion tensor images (DTIs). This was accomplished in the setting of multi-contrast diffeomorphic likelihood fusion using multiple DTI atlases. DTI images are modeled as high dimensional fields, with each voxel exhibiting a vector valued feature comprising of mean diffusivity (MD), fractional anisotropy (FA), and fiber angle. For each structure, the probability distribution of each element in the feature vector is modeled as a mixture of Gaussians, the parameters of which are estimated from the labeled atlases. The structure-specific feature vector is then used to parcellate the test image. For each atlas, a likelihood is iteratively computed based on the structure-specific vector feature. The likelihoods from multiple atlases are then fused. The updating and fusing of the likelihoods is achieved based on the expectation-maximization (EM) algorithm for maximum a posteriori (MAP) estimation problems. We first demonstrate the performance of the algorithm by examining the parcellation accuracy of 18 structures from 25 subjects with a varying degree of structural abnormality. Dice values ranging 0.8–0.9 were obtained. In addition, strong correlation was found between the volume size of the automated and the manual parcellation. Then, we present scan-rescan reproducibility based on another dataset of 16 DTI images – an average of 3.73%, 1.91%, and 1.79% for volume, mean FA, and mean MD respectively. Finally, the range of anatomical variability in the normal population was quantified for each structure. PMID:24809486

Improved relocatable over-the-horizon radar detection and tracking using the maximum likelihood adaptive neural system algorithm

NASA Astrophysics Data System (ADS)

Perlovsky, Leonid I.; Webb, Virgil H.; Bradley, Scott R.; Hansen, Christopher A.

1998-07-01

An advanced detection and tracking system is being developed for the U.S. Navy's Relocatable Over-the-Horizon Radar (ROTHR) to provide improved tracking performance against small aircraft typically used in drug-smuggling activities. The development is based on the Maximum Likelihood Adaptive Neural System (MLANS), a model-based neural network that combines advantages of neural network and model-based algorithmic approaches. The objective of the MLANS tracker development effort is to address user requirements for increased detection and tracking capability in clutter and improved track position, heading, and speed accuracy. The MLANS tracker is expected to outperform other approaches to detection and tracking for the following reasons. It incorporates adaptive internal models of target return signals, target tracks and maneuvers, and clutter signals, which leads to concurrent clutter suppression, detection, and tracking (track-before-detect). It is not combinatorial and thus does not require any thresholding or peak picking and can track in low signal-to-noise conditions. It incorporates superresolution spectrum estimation techniques exceeding the performance of conventional maximum likelihood and maximum entropy methods. The unique spectrum estimation method is based on the Einsteinian interpretation of the ROTHR received energy spectrum as a probability density of signal frequency. The MLANS neural architecture and learning mechanism are founded on spectrum models and maximization of the "Einsteinian" likelihood, allowing knowledge of the physical behavior of both targets and clutter to be injected into the tracker algorithms. The paper describes the addressed requirements and expected improvements, theoretical foundations, engineering methodology, and results of the development effort to date.