A comparison of linear and nonlinear statistical techniques in performance attribution.

PubMed

Chan, N H; Genovese, C R

2001-01-01

Performance attribution is usually conducted under the linear framework of multifactor models. Although commonly used by practitioners in finance, linear multifactor models are known to be less than satisfactory in many situations. After a brief survey of nonlinear methods, nonlinear statistical techniques are applied to performance attribution of a portfolio constructed from a fixed universe of stocks using factors derived from some commonly used cross sectional linear multifactor models. By rebalancing this portfolio monthly, the cumulative returns for procedures based on standard linear multifactor model and three nonlinear techniques-model selection, additive models, and neural networks-are calculated and compared. It is found that the first two nonlinear techniques, especially in combination, outperform the standard linear model. The results in the neural-network case are inconclusive because of the great variety of possible models. Although these methods are more complicated and may require some tuning, toolboxes are developed and suggestions on calibration are proposed. This paper demonstrates the usefulness of modern nonlinear statistical techniques in performance attribution.

On the implications of the classical ergodic theorems: analysis of developmental processes has to focus on intra-individual variation.

PubMed

Molenaar, Peter C M

2008-01-01

It is argued that general mathematical-statistical theorems imply that standard statistical analysis techniques of inter-individual variation are invalid to investigate developmental processes. Developmental processes have to be analyzed at the level of individual subjects, using time series data characterizing the patterns of intra-individual variation. It is shown that standard statistical techniques based on the analysis of inter-individual variation appear to be insensitive to the presence of arbitrary large degrees of inter-individual heterogeneity in the population. An important class of nonlinear epigenetic models of neural growth is described which can explain the occurrence of such heterogeneity in brain structures and behavior. Links with models of developmental instability are discussed. A simulation study based on a chaotic growth model illustrates the invalidity of standard analysis of inter-individual variation, whereas time series analysis of intra-individual variation is able to recover the true state of affairs. (c) 2007 Wiley Periodicals, Inc.

Using the Modification Index and Standardized Expected Parameter Change for Model Modification

ERIC Educational Resources Information Center

Whittaker, Tiffany A.

2012-01-01

Model modification is oftentimes conducted after discovering a badly fitting structural equation model. During the modification process, the modification index (MI) and the standardized expected parameter change (SEPC) are 2 statistics that may be used to aid in the selection of parameters to add to a model to improve the fit. The purpose of this…

Observation of the rare $$B^0_s\\to\\mu^+\\mu^-$$ decay from the combined analysis of CMS and LHCb data

DOE PAGES

Khachatryan, Vardan

2015-05-13

The standard model of particle physics describes the fundamental particles and their interactions via the strong, electromagnetic and weak forces. It provides precise predictions for measurable quantities that can be tested experimentally. We foudn that the probabilities, or branching fractions, of the strange B meson (B 0 2 ) and the B 0 meson decaying into two oppositely charged muons (μ + and μ -) are especially interesting because of their sensitivity to theories that extend the standard model. The standard model predicts that the B 0 2 → μ + and μ - and (B 0 → μ +more » and μ - decays are very rare, with about four of the former occurring for every billion mesons produced, and one of the latter occurring for every ten billion B 0 mesons1. A difference in the observed branching fractions with respect to the predictions of the standard model would provide a direction in which the standard model should be extended. Before the Large Hadron Collider (LHC) at CERN2 started operating, no evidence for either decay mode had been found. Upper limits on the branching fractions were an order of magnitude above the standard model predictions. The CMS (Compact Muon Solenoid) and LHCb (Large Hadron Collider beauty) collaborations have performed a joint analysis of the data from proton–proton collisions that they collected in 2011 at a centre-of-mass energy of seven teraelectronvolts and in 2012 at eight teraelectronvolts. Here we report the first observation of the μ + and μ -decay, with a statistical significance exceeding six standard deviations, and the best measurement so far of its branching fraction. We then obtained evidence for the B 0 → μ + and μ - decay with a statistical significance of three standard deviations. Both measurements are statistically compatible with standard model predictions and allow stringent constraints to be placed on theories beyond the standard model. The LHC experiments will resume taking data in 2015, recording proton–proton collisions at a centre-of-mass energy of 13 teraelectronvolts, which will approximately double the production rates of B 0 2 and B 0 mesons and lead to further improvements in the precision of these crucial tests of the standard model.« less

Observation of the rare $$B^0_s\\to\\mu^+\\mu^-$$ decay from the combined analysis of CMS and LHCb data

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

Khachatryan, Vardan

The standard model of particle physics describes the fundamental particles and their interactions via the strong, electromagnetic and weak forces. It provides precise predictions for measurable quantities that can be tested experimentally. We foudn that the probabilities, or branching fractions, of the strange B meson (B 0 2 ) and the B 0 meson decaying into two oppositely charged muons (μ + and μ -) are especially interesting because of their sensitivity to theories that extend the standard model. The standard model predicts that the B 0 2 → μ + and μ - and (B 0 → μ +more » and μ - decays are very rare, with about four of the former occurring for every billion mesons produced, and one of the latter occurring for every ten billion B 0 mesons1. A difference in the observed branching fractions with respect to the predictions of the standard model would provide a direction in which the standard model should be extended. Before the Large Hadron Collider (LHC) at CERN2 started operating, no evidence for either decay mode had been found. Upper limits on the branching fractions were an order of magnitude above the standard model predictions. The CMS (Compact Muon Solenoid) and LHCb (Large Hadron Collider beauty) collaborations have performed a joint analysis of the data from proton–proton collisions that they collected in 2011 at a centre-of-mass energy of seven teraelectronvolts and in 2012 at eight teraelectronvolts. Here we report the first observation of the μ + and μ -decay, with a statistical significance exceeding six standard deviations, and the best measurement so far of its branching fraction. We then obtained evidence for the B 0 → μ + and μ - decay with a statistical significance of three standard deviations. Both measurements are statistically compatible with standard model predictions and allow stringent constraints to be placed on theories beyond the standard model. The LHC experiments will resume taking data in 2015, recording proton–proton collisions at a centre-of-mass energy of 13 teraelectronvolts, which will approximately double the production rates of B 0 2 and B 0 mesons and lead to further improvements in the precision of these crucial tests of the standard model.« less

Observation of the rare B(s)(0) →µ+µ− decay from the combined analysis of CMS and LHCb data.

PubMed

2015-06-04

The standard model of particle physics describes the fundamental particles and their interactions via the strong, electromagnetic and weak forces. It provides precise predictions for measurable quantities that can be tested experimentally. The probabilities, or branching fractions, of the strange B meson (B(s)(0)) and the B0 meson decaying into two oppositely charged muons (μ+ and μ−) are especially interesting because of their sensitivity to theories that extend the standard model. The standard model predicts that the B(s)(0) →µ+µ− and B(0) →µ+µ− decays are very rare, with about four of the former occurring for every billion mesons produced, and one of the latter occurring for every ten billion B0 mesons. A difference in the observed branching fractions with respect to the predictions of the standard model would provide a direction in which the standard model should be extended. Before the Large Hadron Collider (LHC) at CERN started operating, no evidence for either decay mode had been found. Upper limits on the branching fractions were an order of magnitude above the standard model predictions. The CMS (Compact Muon Solenoid) and LHCb (Large Hadron Collider beauty) collaborations have performed a joint analysis of the data from proton–proton collisions that they collected in 2011 at a centre-of-mass energy of seven teraelectronvolts and in 2012 at eight teraelectronvolts. Here we report the first observation of the B(s)(0) → µ+µ− decay, with a statistical significance exceeding six standard deviations, and the best measurement so far of its branching fraction. Furthermore, we obtained evidence for the B(0) → µ+µ− decay with a statistical significance of three standard deviations. Both measurements are statistically compatible with standard model predictions and allow stringent constraints to be placed on theories beyond the standard model. The LHC experiments will resume taking data in 2015, recording proton–proton collisions at a centre-of-mass energy of 13 teraelectronvolts, which will approximately double the production rates of B(s)(0) and B0 mesons and lead to further improvements in the precision of these crucial tests of the standard model.

Ensuring Positiveness of the Scaled Difference Chi-square Test Statistic.

PubMed

Satorra, Albert; Bentler, Peter M

2010-06-01

A scaled difference test statistic [Formula: see text] that can be computed from standard software of structural equation models (SEM) by hand calculations was proposed in Satorra and Bentler (2001). The statistic [Formula: see text] is asymptotically equivalent to the scaled difference test statistic T̄(d) introduced in Satorra (2000), which requires more involved computations beyond standard output of SEM software. The test statistic [Formula: see text] has been widely used in practice, but in some applications it is negative due to negativity of its associated scaling correction. Using the implicit function theorem, this note develops an improved scaling correction leading to a new scaled difference statistic T̄(d) that avoids negative chi-square values.

Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable.

PubMed

Austin, Peter C; Steyerberg, Ewout W

2012-06-20

When outcomes are binary, the c-statistic (equivalent to the area under the Receiver Operating Characteristic curve) is a standard measure of the predictive accuracy of a logistic regression model. An analytical expression was derived under the assumption that a continuous explanatory variable follows a normal distribution in those with and without the condition. We then conducted an extensive set of Monte Carlo simulations to examine whether the expressions derived under the assumption of binormality allowed for accurate prediction of the empirical c-statistic when the explanatory variable followed a normal distribution in the combined sample of those with and without the condition. We also examine the accuracy of the predicted c-statistic when the explanatory variable followed a gamma, log-normal or uniform distribution in combined sample of those with and without the condition. Under the assumption of binormality with equality of variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the product of the standard deviation of the normal components (reflecting more heterogeneity) and the log-odds ratio (reflecting larger effects). Under the assumption of binormality with unequal variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the standardized difference of the explanatory variable in those with and without the condition. In our Monte Carlo simulations, we found that these expressions allowed for reasonably accurate prediction of the empirical c-statistic when the distribution of the explanatory variable was normal, gamma, log-normal, and uniform in the entire sample of those with and without the condition. The discriminative ability of a continuous explanatory variable cannot be judged by its odds ratio alone, but always needs to be considered in relation to the heterogeneity of the population.

Analysis and meta-analysis of single-case designs: an introduction.

PubMed

Shadish, William R

2014-04-01

The last 10 years have seen great progress in the analysis and meta-analysis of single-case designs (SCDs). This special issue includes five articles that provide an overview of current work on that topic, including standardized mean difference statistics, multilevel models, Bayesian statistics, and generalized additive models. Each article analyzes a common example across articles and presents syntax or macros for how to do them. These articles are followed by commentaries from single-case design researchers and journal editors. This introduction briefly describes each article and then discusses several issues that must be addressed before we can know what analyses will eventually be best to use in SCD research. These issues include modeling trend, modeling error covariances, computing standardized effect size estimates, assessing statistical power, incorporating more accurate models of outcome distributions, exploring whether Bayesian statistics can improve estimation given the small samples common in SCDs, and the need for annotated syntax and graphical user interfaces that make complex statistics accessible to SCD researchers. The article then discusses reasons why SCD researchers are likely to incorporate statistical analyses into their research more often in the future, including changing expectations and contingencies regarding SCD research from outside SCD communities, changes and diversity within SCD communities, corrections of erroneous beliefs about the relationship between SCD research and statistics, and demonstrations of how statistics can help SCD researchers better meet their goals. Copyright © 2013 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

How to compare cross-lagged associations in a multilevel autoregressive model.

PubMed

Schuurman, Noémi K; Ferrer, Emilio; de Boer-Sonnenschein, Mieke; Hamaker, Ellen L

2016-06-01

By modeling variables over time it is possible to investigate the Granger-causal cross-lagged associations between variables. By comparing the standardized cross-lagged coefficients, the relative strength of these associations can be evaluated in order to determine important driving forces in the dynamic system. The aim of this study was twofold: first, to illustrate the added value of a multilevel multivariate autoregressive modeling approach for investigating these associations over more traditional techniques; and second, to discuss how the coefficients of the multilevel autoregressive model should be standardized for comparing the strength of the cross-lagged associations. The hierarchical structure of multilevel multivariate autoregressive models complicates standardization, because subject-based statistics or group-based statistics can be used to standardize the coefficients, and each method may result in different conclusions. We argue that in order to make a meaningful comparison of the strength of the cross-lagged associations, the coefficients should be standardized within persons. We further illustrate the bivariate multilevel autoregressive model and the standardization of the coefficients, and we show that disregarding individual differences in dynamics can prove misleading, by means of an empirical example on experienced competence and exhaustion in persons diagnosed with burnout. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Ensuring Positiveness of the Scaled Difference Chi-Square Test Statistic

ERIC Educational Resources Information Center

Satorra, Albert; Bentler, Peter M.

2010-01-01

A scaled difference test statistic T[tilde][subscript d] that can be computed from standard software of structural equation models (SEM) by hand calculations was proposed in Satorra and Bentler (Psychometrika 66:507-514, 2001). The statistic T[tilde][subscript d] is asymptotically equivalent to the scaled difference test statistic T[bar][subscript…

The Standard Model in the history of the Natural Sciences, Econometrics, and the social sciences

NASA Astrophysics Data System (ADS)

Fisher, W. P., Jr.

2010-07-01

In the late 18th and early 19th centuries, scientists appropriated Newton's laws of motion as a model for the conduct of any other field of investigation that would purport to be a science. This early form of a Standard Model eventually informed the basis of analogies for the mathematical expression of phenomena previously studied qualitatively, such as cohesion, affinity, heat, light, electricity, and magnetism. James Clerk Maxwell is known for his repeated use of a formalized version of this method of analogy in lectures, teaching, and the design of experiments. Economists transferring skills learned in physics made use of the Standard Model, especially after Maxwell demonstrated the value of conceiving it in abstract mathematics instead of as a concrete and literal mechanical analogy. Haavelmo's probability approach in econometrics and R. Fisher's Statistical Methods for Research Workers brought a statistical approach to bear on the Standard Model, quietly reversing the perspective of economics and the social sciences relative to that of physics. Where physicists, and Maxwell in particular, intuited scientific method as imposing stringent demands on the quality and interrelations of data, instruments, and theory in the name of inferential and comparative stability, statistical models and methods disconnected theory from data by removing the instrument as an essential component. New possibilities for reconnecting economics and the social sciences to Maxwell's sense of the method of analogy are found in Rasch's probabilistic models for measurement.

[Applications of the hospital statistics management system].

PubMed

Zhai, Hong; Ren, Yong; Liu, Jing; Li, You-Zhang; Ma, Xiao-Long; Jiao, Tao-Tao

2008-01-01

The Hospital Statistics Management System is built on an Office Automation Platform of Shandong provincial hospital system. Its workflow, role and popedom technologies are used to standardize and optimize the management program of statistics in the total quality control of hospital statistics. The system's applications have combined the office automation platform with the statistics management in a hospital and this provides a practical example of a modern hospital statistics management model.

A Model for Investigating Predictive Validity at Highly Selective Institutions.

ERIC Educational Resources Information Center

Gross, Alan L.; And Others

A statistical model for investigating predictive validity at highly selective institutions is described. When the selection ratio is small, one must typically deal with a data set containing relatively large amounts of missing data on both criterion and predictor variables. Standard statistical approaches are based on the strong assumption that…

[Establishment of the mathematic model of total quantum statistical moment standard similarity for application to medical theoretical research].

PubMed

He, Fu-yuan; Deng, Kai-wen; Huang, Sheng; Liu, Wen-long; Shi, Ji-lian

2013-09-01

The paper aims to elucidate and establish a new mathematic model: the total quantum statistical moment standard similarity (TQSMSS) on the base of the original total quantum statistical moment model and to illustrate the application of the model to medical theoretical research. The model was established combined with the statistical moment principle and the normal distribution probability density function properties, then validated and illustrated by the pharmacokinetics of three ingredients in Buyanghuanwu decoction and of three data analytical method for them, and by analysis of chromatographic fingerprint for various extracts with different solubility parameter solvents dissolving the Buyanghanwu-decoction extract. The established model consists of four mainly parameters: (1) total quantum statistical moment similarity as ST, an overlapped area by two normal distribution probability density curves in conversion of the two TQSM parameters; (2) total variability as DT, a confidence limit of standard normal accumulation probability which is equal to the absolute difference value between the two normal accumulation probabilities within integration of their curve nodical; (3) total variable probability as 1-Ss, standard normal distribution probability within interval of D(T); (4) total variable probability (1-beta)alpha and (5) stable confident probability beta(1-alpha): the correct probability to make positive and negative conclusions under confident coefficient alpha. With the model, we had analyzed the TQSMS similarities of pharmacokinetics of three ingredients in Buyanghuanwu decoction and of three data analytical methods for them were at range of 0.3852-0.9875 that illuminated different pharmacokinetic behaviors of each other; and the TQSMS similarities (ST) of chromatographic fingerprint for various extracts with different solubility parameter solvents dissolving Buyanghuanwu-decoction-extract were at range of 0.6842-0.999 2 that showed different constituents with various solvent extracts. The TQSMSS can characterize the sample similarity, by which we can quantitate the correct probability with the test of power under to make positive and negative conclusions no matter the samples come from same population under confident coefficient a or not, by which we can realize an analysis at both macroscopic and microcosmic levels, as an important similar analytical method for medical theoretical research.

Design of experiments enhanced statistical process control for wind tunnel check standard testing

NASA Astrophysics Data System (ADS)

Phillips, Ben D.

The current wind tunnel check standard testing program at NASA Langley Research Center is focused on increasing data quality, uncertainty quantification and overall control and improvement of wind tunnel measurement processes. The statistical process control (SPC) methodology employed in the check standard testing program allows for the tracking of variations in measurements over time as well as an overall assessment of facility health. While the SPC approach can and does provide researchers with valuable information, it has certain limitations in the areas of process improvement and uncertainty quantification. It is thought by utilizing design of experiments methodology in conjunction with the current SPC practices that one can efficiently and more robustly characterize uncertainties and develop enhanced process improvement procedures. In this research, methodologies were developed to generate regression models for wind tunnel calibration coefficients, balance force coefficients and wind tunnel flow angularities. The coefficients of these regression models were then tracked in statistical process control charts, giving a higher level of understanding of the processes. The methodology outlined is sufficiently generic such that this research can be applicable to any wind tunnel check standard testing program.

A Statistical Model for Misreported Binary Outcomes in Clustered RCTs of Education Interventions

ERIC Educational Resources Information Center

Schochet, Peter Z.

2013-01-01

In education randomized control trials (RCTs), the misreporting of student outcome data could lead to biased estimates of average treatment effects (ATEs) and their standard errors. This article discusses a statistical model that adjusts for misreported binary outcomes for two-level, school-based RCTs, where it is assumed that misreporting could…

Determining the Number of Component Clusters in the Standard Multivariate Normal Mixture Model Using Model-Selection Criteria.

DTIC Science & Technology

1983-06-16

has been advocated by Gnanadesikan and 𔃾ilk (1969), and others in the literature. This suggests that, if we use the formal signficance test type...American Statistical Asso., 62, 1159-1178. Gnanadesikan , R., and Wilk, M..B. (1969). Data Analytic Methods in Multi- variate Statistical Analysis. In

Boosting Bayesian parameter inference of stochastic differential equation models with methods from statistical physics

NASA Astrophysics Data System (ADS)

Albert, Carlo; Ulzega, Simone; Stoop, Ruedi

2016-04-01

Measured time-series of both precipitation and runoff are known to exhibit highly non-trivial statistical properties. For making reliable probabilistic predictions in hydrology, it is therefore desirable to have stochastic models with output distributions that share these properties. When parameters of such models have to be inferred from data, we also need to quantify the associated parametric uncertainty. For non-trivial stochastic models, however, this latter step is typically very demanding, both conceptually and numerically, and always never done in hydrology. Here, we demonstrate that methods developed in statistical physics make a large class of stochastic differential equation (SDE) models amenable to a full-fledged Bayesian parameter inference. For concreteness we demonstrate these methods by means of a simple yet non-trivial toy SDE model. We consider a natural catchment that can be described by a linear reservoir, at the scale of observation. All the neglected processes are assumed to happen at much shorter time-scales and are therefore modeled with a Gaussian white noise term, the standard deviation of which is assumed to scale linearly with the system state (water volume in the catchment). Even for constant input, the outputs of this simple non-linear SDE model show a wealth of desirable statistical properties, such as fat-tailed distributions and long-range correlations. Standard algorithms for Bayesian inference fail, for models of this kind, because their likelihood functions are extremely high-dimensional intractable integrals over all possible model realizations. The use of Kalman filters is illegitimate due to the non-linearity of the model. Particle filters could be used but become increasingly inefficient with growing number of data points. Hamiltonian Monte Carlo algorithms allow us to translate this inference problem to the problem of simulating the dynamics of a statistical mechanics system and give us access to most sophisticated methods that have been developed in the statistical physics community over the last few decades. We demonstrate that such methods, along with automated differentiation algorithms, allow us to perform a full-fledged Bayesian inference, for a large class of SDE models, in a highly efficient and largely automatized manner. Furthermore, our algorithm is highly parallelizable. For our toy model, discretized with a few hundred points, a full Bayesian inference can be performed in a matter of seconds on a standard PC.

Progress in the improved lattice calculation of direct CP-violation in the Standard Model

NASA Astrophysics Data System (ADS)

Kelly, Christopher

2018-03-01

We discuss the ongoing effort by the RBC & UKQCD collaborations to improve our lattice calculation of the measure of Standard Model direct CP violation, ɛ', with physical kinematics. We present our progress in decreasing the (dominant) statistical error and discuss other related activities aimed at reducing the systematic errors.

Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable

PubMed Central

2012-01-01

Background When outcomes are binary, the c-statistic (equivalent to the area under the Receiver Operating Characteristic curve) is a standard measure of the predictive accuracy of a logistic regression model. Methods An analytical expression was derived under the assumption that a continuous explanatory variable follows a normal distribution in those with and without the condition. We then conducted an extensive set of Monte Carlo simulations to examine whether the expressions derived under the assumption of binormality allowed for accurate prediction of the empirical c-statistic when the explanatory variable followed a normal distribution in the combined sample of those with and without the condition. We also examine the accuracy of the predicted c-statistic when the explanatory variable followed a gamma, log-normal or uniform distribution in combined sample of those with and without the condition. Results Under the assumption of binormality with equality of variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the product of the standard deviation of the normal components (reflecting more heterogeneity) and the log-odds ratio (reflecting larger effects). Under the assumption of binormality with unequal variances, the c-statistic follows a standard normal cumulative distribution function with dependence on the standardized difference of the explanatory variable in those with and without the condition. In our Monte Carlo simulations, we found that these expressions allowed for reasonably accurate prediction of the empirical c-statistic when the distribution of the explanatory variable was normal, gamma, log-normal, and uniform in the entire sample of those with and without the condition. Conclusions The discriminative ability of a continuous explanatory variable cannot be judged by its odds ratio alone, but always needs to be considered in relation to the heterogeneity of the population. PMID:22716998

Standard and reduced radiation dose liver CT images: adaptive statistical iterative reconstruction versus model-based iterative reconstruction-comparison of findings and image quality.

PubMed

Shuman, William P; Chan, Keith T; Busey, Janet M; Mitsumori, Lee M; Choi, Eunice; Koprowicz, Kent M; Kanal, Kalpana M

2014-12-01

To investigate whether reduced radiation dose liver computed tomography (CT) images reconstructed with model-based iterative reconstruction ( MBIR model-based iterative reconstruction ) might compromise depiction of clinically relevant findings or might have decreased image quality when compared with clinical standard radiation dose CT images reconstructed with adaptive statistical iterative reconstruction ( ASIR adaptive statistical iterative reconstruction ). With institutional review board approval, informed consent, and HIPAA compliance, 50 patients (39 men, 11 women) were prospectively included who underwent liver CT. After a portal venous pass with ASIR adaptive statistical iterative reconstruction images, a 60% reduced radiation dose pass was added with MBIR model-based iterative reconstruction images. One reviewer scored ASIR adaptive statistical iterative reconstruction image quality and marked findings. Two additional independent reviewers noted whether marked findings were present on MBIR model-based iterative reconstruction images and assigned scores for relative conspicuity, spatial resolution, image noise, and image quality. Liver and aorta Hounsfield units and image noise were measured. Volume CT dose index and size-specific dose estimate ( SSDE size-specific dose estimate ) were recorded. Qualitative reviewer scores were summarized. Formal statistical inference for signal-to-noise ratio ( SNR signal-to-noise ratio ), contrast-to-noise ratio ( CNR contrast-to-noise ratio ), volume CT dose index, and SSDE size-specific dose estimate was made (paired t tests), with Bonferroni adjustment. Two independent reviewers identified all 136 ASIR adaptive statistical iterative reconstruction image findings (n = 272) on MBIR model-based iterative reconstruction images, scoring them as equal or better for conspicuity, spatial resolution, and image noise in 94.1% (256 of 272), 96.7% (263 of 272), and 99.3% (270 of 272), respectively. In 50 image sets, two reviewers (n = 100) scored overall image quality as sufficient or good with MBIR model-based iterative reconstruction in 99% (99 of 100). Liver SNR signal-to-noise ratio was significantly greater for MBIR model-based iterative reconstruction (10.8 ± 2.5 [standard deviation] vs 7.7 ± 1.4, P < .001); there was no difference for CNR contrast-to-noise ratio (2.5 ± 1.4 vs 2.4 ± 1.4, P = .45). For ASIR adaptive statistical iterative reconstruction and MBIR model-based iterative reconstruction , respectively, volume CT dose index was 15.2 mGy ± 7.6 versus 6.2 mGy ± 3.6; SSDE size-specific dose estimate was 16.4 mGy ± 6.6 versus 6.7 mGy ± 3.1 (P < .001). Liver CT images reconstructed with MBIR model-based iterative reconstruction may allow up to 59% radiation dose reduction compared with the dose with ASIR adaptive statistical iterative reconstruction , without compromising depiction of findings or image quality. © RSNA, 2014.

A Model for Developing and Assessing Community College Students' Conceptions of the Range, Interquartile Range, and Standard Deviation

ERIC Educational Resources Information Center

Turegun, Mikhail

2011-01-01

Traditional curricular materials and pedagogical strategies have not been effective in developing conceptual understanding of statistics topics and statistical reasoning abilities of students. Much of the changes proposed by statistics education research and the reform movement over the past decade have supported efforts to transform teaching…

Water quality management using statistical analysis and time-series prediction model

NASA Astrophysics Data System (ADS)

Parmar, Kulwinder Singh; Bhardwaj, Rashmi

2014-12-01

This paper deals with water quality management using statistical analysis and time-series prediction model. The monthly variation of water quality standards has been used to compare statistical mean, median, mode, standard deviation, kurtosis, skewness, coefficient of variation at Yamuna River. Model validated using R-squared, root mean square error, mean absolute percentage error, maximum absolute percentage error, mean absolute error, maximum absolute error, normalized Bayesian information criterion, Ljung-Box analysis, predicted value and confidence limits. Using auto regressive integrated moving average model, future water quality parameters values have been estimated. It is observed that predictive model is useful at 95 % confidence limits and curve is platykurtic for potential of hydrogen (pH), free ammonia, total Kjeldahl nitrogen, dissolved oxygen, water temperature (WT); leptokurtic for chemical oxygen demand, biochemical oxygen demand. Also, it is observed that predicted series is close to the original series which provides a perfect fit. All parameters except pH and WT cross the prescribed limits of the World Health Organization /United States Environmental Protection Agency, and thus water is not fit for drinking, agriculture and industrial use.

The international growth standard for preadolescent and adolescent children: statistical considerations.

PubMed

Cole, T J

2006-12-01

This article discusses statistical considerations for the design of a new study intended to provide an International Growth Standard for Preadolescent and Adolescent Children, including issues such as cross-sectional, longitudinal, and mixed designs; sample-size derivation for the number of populations and number of children per population; modeling of growth centiles of height, weight, and other measurements; and modeling of the adolescent growth spurt. The conclusions are that a mixed longitudinal design will provide information on both growth distance and velocity; samples of children from 5 to 10 sites should be suitable for an international standard (based on political rather than statistical arguments); the samples should be broadly uniform across age but oversampled during puberty, and should include data into adulthood. The LMS method is recommended for constructing measurement centiles, and parametric or semiparametric approaches are available to estimate the timing of the adolescent growth spurt in individuals. If the new standard is to be grafted onto the 2006 World Health Organization (WHO) reference, caution is needed at the join point of 5 years, where children from the new standard are likely to be appreciably more obese than those from the WHO reference, due to the rising trends in obesity and the time gap in data collection between the two surveys.

A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more.

PubMed

Rivas, Elena; Lang, Raymond; Eddy, Sean R

2012-02-01

The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases.

A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more

PubMed Central

Rivas, Elena; Lang, Raymond; Eddy, Sean R.

2012-01-01

The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases. PMID:22194308

Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data.

PubMed

Tekwe, Carmen D; Carroll, Raymond J; Dabney, Alan R

2012-08-01

Protein abundance in quantitative proteomics is often based on observed spectral features derived from liquid chromatography mass spectrometry (LC-MS) or LC-MS/MS experiments. Peak intensities are largely non-normal in distribution. Furthermore, LC-MS-based proteomics data frequently have large proportions of missing peak intensities due to censoring mechanisms on low-abundance spectral features. Recognizing that the observed peak intensities detected with the LC-MS method are all positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney rank sum tests, and the parametric survival model and accelerated failure time-model with log-normal, log-logistic and Weibull distributions were used to detect any differentially expressed proteins. The statistical operating characteristics of each method are explored using both real and simulated datasets. Survival methods generally have greater statistical power than standard differential expression methods when the proportion of missing protein level data is 5% or more. In particular, the AFT models we consider consistently achieve greater statistical power than standard testing procedures, with the discrepancy widening with increasing missingness in the proportions. The testing procedures discussed in this article can all be performed using readily available software such as R. The R codes are provided as supplemental materials. ctekwe@stat.tamu.edu.

QCD Precision Measurements and Structure Function Extraction at a High Statistics, High Energy Neutrino Scattering Experiment:. NuSOnG

NASA Astrophysics Data System (ADS)

Adams, T.; Batra, P.; Bugel, L.; Camilleri, L.; Conrad, J. M.; de Gouvêa, A.; Fisher, P. H.; Formaggio, J. A.; Jenkins, J.; Karagiorgi, G.; Kobilarcik, T. R.; Kopp, S.; Kyle, G.; Loinaz, W. A.; Mason, D. A.; Milner, R.; Moore, R.; Morfín, J. G.; Nakamura, M.; Naples, D.; Nienaber, P.; Olness, F. I.; Owens, J. F.; Pate, S. F.; Pronin, A.; Seligman, W. G.; Shaevitz, M. H.; Schellman, H.; Schienbein, I.; Syphers, M. J.; Tait, T. M. P.; Takeuchi, T.; Tan, C. Y.; van de Water, R. G.; Yamamoto, R. K.; Yu, J. Y.

We extend the physics case for a new high-energy, ultra-high statistics neutrino scattering experiment, NuSOnG (Neutrino Scattering On Glass) to address a variety of issues including precision QCD measurements, extraction of structure functions, and the derived Parton Distribution Functions (PDF's). This experiment uses a Tevatron-based neutrino beam to obtain a sample of Deep Inelastic Scattering (DIS) events which is over two orders of magnitude larger than past samples. We outline an innovative method for fitting the structure functions using a parametrized energy shift which yields reduced systematic uncertainties. High statistics measurements, in combination with improved systematics, will enable NuSOnG to perform discerning tests of fundamental Standard Model parameters as we search for deviations which may hint of "Beyond the Standard Model" physics.

Constraints on non-Standard Model Higgs boson interactions in an effective Lagrangian using differential cross sections measured in the H→γγ decay channel at \\(\\sqrt{s} = 8\\) TeV with the ATLAS detector

DOE PAGES

Aad, G.

2015-12-02

The strength and tensor structure of the Higgs boson's interactions are investigated using an effective Lagrangian, which introduces additional CP-even and CP-odd interactions that lead to changes in the kinematic properties of the Higgs boson and associated jet spectra with respect to the Standard Model. We found that the parameters of the effective Lagrangian are probed using a fit to five differential cross sections previously measured by the ATLAS experiment in the H→γγ decay channel with an integrated luminosity of 20.3 fb -1 at \\(\\sqrt{s} = 8\\) TeV. In order to perform a simultaneous fit to the five distributions, themore » statistical correlations between them are determined by re-analysing the H→γγ candidate events in the proton–proton collision data. No significant deviations from the Standard Model predictions are observed and limits on the effective Lagrangian parameters are derived. These statistical correlations are made publicly available to allow for future analysis of theories with non-Standard Model interactions.« less

Constraints on non-Standard Model Higgs boson interactions in an effective Lagrangian using differential cross sections measured in the H→γγ decay channel at \\(\\sqrt{s} = 8\\) TeV with the ATLAS detector

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

Aad, G.

The strength and tensor structure of the Higgs boson's interactions are investigated using an effective Lagrangian, which introduces additional CP-even and CP-odd interactions that lead to changes in the kinematic properties of the Higgs boson and associated jet spectra with respect to the Standard Model. We found that the parameters of the effective Lagrangian are probed using a fit to five differential cross sections previously measured by the ATLAS experiment in the H→γγ decay channel with an integrated luminosity of 20.3 fb -1 at \\(\\sqrt{s} = 8\\) TeV. In order to perform a simultaneous fit to the five distributions, themore » statistical correlations between them are determined by re-analysing the H→γγ candidate events in the proton–proton collision data. No significant deviations from the Standard Model predictions are observed and limits on the effective Lagrangian parameters are derived. These statistical correlations are made publicly available to allow for future analysis of theories with non-Standard Model interactions.« less

Designing image segmentation studies: Statistical power, sample size and reference standard quality.

PubMed

Gibson, Eli; Hu, Yipeng; Huisman, Henkjan J; Barratt, Dean C

2017-12-01

Segmentation algorithms are typically evaluated by comparison to an accepted reference standard. The cost of generating accurate reference standards for medical image segmentation can be substantial. Since the study cost and the likelihood of detecting a clinically meaningful difference in accuracy both depend on the size and on the quality of the study reference standard, balancing these trade-offs supports the efficient use of research resources. In this work, we derive a statistical power calculation that enables researchers to estimate the appropriate sample size to detect clinically meaningful differences in segmentation accuracy (i.e. the proportion of voxels matching the reference standard) between two algorithms. Furthermore, we derive a formula to relate reference standard errors to their effect on the sample sizes of studies using lower-quality (but potentially more affordable and practically available) reference standards. The accuracy of the derived sample size formula was estimated through Monte Carlo simulation, demonstrating, with 95% confidence, a predicted statistical power within 4% of simulated values across a range of model parameters. This corresponds to sample size errors of less than 4 subjects and errors in the detectable accuracy difference less than 0.6%. The applicability of the formula to real-world data was assessed using bootstrap resampling simulations for pairs of algorithms from the PROMISE12 prostate MR segmentation challenge data set. The model predicted the simulated power for the majority of algorithm pairs within 4% for simulated experiments using a high-quality reference standard and within 6% for simulated experiments using a low-quality reference standard. A case study, also based on the PROMISE12 data, illustrates using the formulae to evaluate whether to use a lower-quality reference standard in a prostate segmentation study. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

An analytic technique for statistically modeling random atomic clock errors in estimation

NASA Technical Reports Server (NTRS)

Fell, P. J.

1981-01-01

Minimum variance estimation requires that the statistics of random observation errors be modeled properly. If measurements are derived through the use of atomic frequency standards, then one source of error affecting the observable is random fluctuation in frequency. This is the case, for example, with range and integrated Doppler measurements from satellites of the Global Positioning and baseline determination for geodynamic applications. An analytic method is presented which approximates the statistics of this random process. The procedure starts with a model of the Allan variance for a particular oscillator and develops the statistics of range and integrated Doppler measurements. A series of five first order Markov processes is used to approximate the power spectral density obtained from the Allan variance.

Standard Entropy of Crystalline Iodine from Vapor Pressure Measurements: A Physical Chemistry Experiment.

ERIC Educational Resources Information Center

Harris, Ronald M.

1978-01-01

Presents material dealing with an application of statistical thermodynamics to the diatomic solid I-2(s). The objective is to enhance the student's appreciation of the power of the statistical formulation of thermodynamics. The Simple Einstein Model is used. (Author/MA)

Baseline models of trace elements in major aquifers of the United States

USGS Publications Warehouse

Lee, L.; Helsel, D.

2005-01-01

Trace-element concentrations in baseline samples from a survey of aquifers used as potable-water supplies in the United States are summarized using methods appropriate for data with multiple detection limits. The resulting statistical distribution models are used to develop summary statistics and estimate probabilities of exceeding water-quality standards. The models are based on data from the major aquifer studies of the USGS National Water Quality Assessment (NAWQA) Program. These data were produced with a nationally-consistent sampling and analytical framework specifically designed to determine the quality of the most important potable groundwater resources during the years 1991-2001. The analytical data for all elements surveyed contain values that were below several detection limits. Such datasets are referred to as multiply-censored data. To address this issue, a robust semi-parametric statistical method called regression on order statistics (ROS) is employed. Utilizing the 90th-95th percentile as an arbitrary range for the upper limits of expected baseline concentrations, the models show that baseline concentrations of dissolved Ba and Zn are below 500 ??g/L. For the same percentile range, dissolved As, Cu and Mo concentrations are below 10 ??g/L, and dissolved Ag, Be, Cd, Co, Cr, Ni, Pb, Sb and Se are below 1-5 ??g/L. These models are also used to determine the probabilities that potable ground waters exceed drinking water standards. For dissolved Ba, Cr, Cu, Pb, Ni, Mo and Se, the likelihood of exceeding the US Environmental Protection Agency standards at the well-head is less than 1-1.5%. A notable exception is As, which has approximately a 7% chance of exceeding the maximum contaminant level (10 ??g/L) at the well head.

Manifold parametrization of the left ventricle for a statistical modelling of its complete anatomy

NASA Astrophysics Data System (ADS)

Gil, D.; Garcia-Barnes, J.; Hernández-Sabate, A.; Marti, E.

2010-03-01

Distortion of Left Ventricle (LV) external anatomy is related to some dysfunctions, such as hypertrophy. The architecture of myocardial fibers determines LV electromechanical activation patterns as well as mechanics. Thus, their joined modelling would allow the design of specific interventions (such as peacemaker implantation and LV remodelling) and therapies (such as resynchronization). On one hand, accurate modelling of external anatomy requires either a dense sampling or a continuous infinite dimensional approach, which requires non-Euclidean statistics. On the other hand, computation of fiber models requires statistics on Riemannian spaces. Most approaches compute separate statistical models for external anatomy and fibers architecture. In this work we propose a general mathematical framework based on differential geometry concepts for computing a statistical model including, both, external and fiber anatomy. Our framework provides a continuous approach to external anatomy supporting standard statistics. We also provide a straightforward formula for the computation of the Riemannian fiber statistics. We have applied our methodology to the computation of complete anatomical atlas of canine hearts from diffusion tensor studies. The orientation of fibers over the average external geometry agrees with the segmental description of orientations reported in the literature.

A Constrained Linear Estimator for Multiple Regression

ERIC Educational Resources Information Center

Davis-Stober, Clintin P.; Dana, Jason; Budescu, David V.

2010-01-01

"Improper linear models" (see Dawes, Am. Psychol. 34:571-582, "1979"), such as equal weighting, have garnered interest as alternatives to standard regression models. We analyze the general circumstances under which these models perform well by recasting a class of "improper" linear models as "proper" statistical models with a single predictor. We…

Increasing market efficiency in the stock markets

NASA Astrophysics Data System (ADS)

Yang, Jae-Suk; Kwak, Wooseop; Kaizoji, Taisei; Kim, In-Mook

2008-01-01

We study the temporal evolutions of three stock markets; Standard and Poor's 500 index, Nikkei 225 Stock Average, and the Korea Composite Stock Price Index. We observe that the probability density function of the log-return has a fat tail but the tail index has been increasing continuously in recent years. We have also found that the variance of the autocorrelation function, the scaling exponent of the standard deviation, and the statistical complexity decrease, but that the entropy density increases as time goes over time. We introduce a modified microscopic spin model and simulate the model to confirm such increasing and decreasing tendencies in statistical quantities. These findings indicate that these three stock markets are becoming more efficient.

Never too old for anonymity: a statistical standard for demographic data sharing via the HIPAA Privacy Rule

PubMed Central

Benitez, Kathleen; Masys, Daniel

2010-01-01

Objective Healthcare organizations must de-identify patient records before sharing data. Many organizations rely on the Safe Harbor Standard of the HIPAA Privacy Rule, which enumerates 18 identifiers that must be suppressed (eg, ages over 89). An alternative model in the Privacy Rule, known as the Statistical Standard, can facilitate the sharing of more detailed data, but is rarely applied because of a lack of published methodologies. The authors propose an intuitive approach to de-identifying patient demographics in accordance with the Statistical Standard. Design The authors conduct an analysis of the demographics of patient cohorts in five medical centers developed for the NIH-sponsored Electronic Medical Records and Genomics network, with respect to the US census. They report the re-identification risk of patient demographics disclosed according to the Safe Harbor policy and the relative risk rate for sharing such information via alternative policies. Measurements The re-identification risk of Safe Harbor demographics ranged from 0.01% to 0.19%. The findings show alternative de-identification models can be created with risks no greater than Safe Harbor. The authors illustrate that the disclosure of patient ages over the age of 89 is possible when other features are reduced in granularity. Limitations The de-identification approach described in this paper was evaluated with demographic data only and should be evaluated with other potential identifiers. Conclusion Alternative de-identification policies to the Safe Harbor model can be derived for patient demographics to enable the disclosure of values that were previously suppressed. The method is generalizable to any environment in which population statistics are available. PMID:21169618

A New Approach of Juvenile Age Estimation using Measurements of the Ilium and Multivariate Adaptive Regression Splines (MARS) Models for Better Age Prediction.

PubMed

Corron, Louise; Marchal, François; Condemi, Silvana; Chaumoître, Kathia; Adalian, Pascal

2017-01-01

Juvenile age estimation methods used in forensic anthropology generally lack methodological consistency and/or statistical validity. Considering this, a standard approach using nonparametric Multivariate Adaptive Regression Splines (MARS) models were tested to predict age from iliac biometric variables of male and female juveniles from Marseilles, France, aged 0-12 years. Models using unidimensional (length and width) and bidimensional iliac data (module and surface) were constructed on a training sample of 176 individuals and validated on an independent test sample of 68 individuals. Results show that MARS prediction models using iliac width, module and area give overall better and statistically valid age estimates. These models integrate punctual nonlinearities of the relationship between age and osteometric variables. By constructing valid prediction intervals whose size increases with age, MARS models take into account the normal increase of individual variability. MARS models can qualify as a practical and standardized approach for juvenile age estimation. © 2016 American Academy of Forensic Sciences.

Generalized t-statistic for two-group classification.

PubMed

Komori, Osamu; Eguchi, Shinto; Copas, John B

2015-06-01

In the classic discriminant model of two multivariate normal distributions with equal variance matrices, the linear discriminant function is optimal both in terms of the log likelihood ratio and in terms of maximizing the standardized difference (the t-statistic) between the means of the two distributions. In a typical case-control study, normality may be sensible for the control sample but heterogeneity and uncertainty in diagnosis may suggest that a more flexible model is needed for the cases. We generalize the t-statistic approach by finding the linear function which maximizes a standardized difference but with data from one of the groups (the cases) filtered by a possibly nonlinear function U. We study conditions for consistency of the method and find the function U which is optimal in the sense of asymptotic efficiency. Optimality may also extend to other measures of discriminatory efficiency such as the area under the receiver operating characteristic curve. The optimal function U depends on a scalar probability density function which can be estimated non-parametrically using a standard numerical algorithm. A lasso-like version for variable selection is implemented by adding L1-regularization to the generalized t-statistic. Two microarray data sets in the study of asthma and various cancers are used as motivating examples. © 2014, The International Biometric Society.

Mathematical models of cytotoxic effects in endpoint tumor cell line assays: critical assessment of the application of a single parametric value as a standard criterion to quantify the dose-response effects and new unexplored proposal formats.

PubMed

Calhelha, Ricardo C; Martínez, Mireia A; Prieto, M A; Ferreira, Isabel C F R

2017-10-23

The development of convenient tools for describing and quantifying the effects of standard and novel therapeutic agents is essential for the research community, to perform more precise evaluations. Although mathematical models and quantification criteria have been exchanged in the last decade between different fields of study, there are relevant methodologies that lack proper mathematical descriptions and standard criteria to quantify their responses. Therefore, part of the relevant information that can be drawn from the experimental results obtained and the quantification of its statistical reliability are lost. Despite its relevance, there is not a standard form for the in vitro endpoint tumor cell lines' assays (TCLA) that enables the evaluation of the cytotoxic dose-response effects of anti-tumor drugs. The analysis of all the specific problems associated with the diverse nature of the available TCLA used is unfeasible. However, since most TCLA share the main objectives and similar operative requirements, we have chosen the sulforhodamine B (SRB) colorimetric assay for cytotoxicity screening of tumor cell lines as an experimental case study. In this work, the common biological and practical non-linear dose-response mathematical models are tested against experimental data and, following several statistical analyses, the model based on the Weibull distribution was confirmed as the convenient approximation to test the cytotoxic effectiveness of anti-tumor compounds. Then, the advantages and disadvantages of all the different parametric criteria derived from the model, which enable the quantification of the dose-response drug-effects, are extensively discussed. Therefore, model and standard criteria for easily performing the comparisons between different compounds are established. The advantages include a simple application, provision of parametric estimations that characterize the response as standard criteria, economization of experimental effort and enabling rigorous comparisons among the effects of different compounds and experimental approaches. In all experimental data fitted, the calculated parameters were always statistically significant, the equations proved to be consistent and the correlation coefficient of determination was, in most of the cases, higher than 0.98.

Effects of atmospheric turbulence on microwave and millimeter wave satellite communications systems. [attenuation statistics and antenna design

NASA Technical Reports Server (NTRS)

Devasirvatham, D. M. J.; Hodge, D. B.

1981-01-01

A model of the microwave and millimeter wave link in the presence of atmospheric turbulence is presented with emphasis on satellite communications systems. The analysis is based on standard methods of statistical theory. The results are directly usable by the design engineer.

SPSS macros to compare any two fitted values from a regression model.

PubMed

Weaver, Bruce; Dubois, Sacha

2012-12-01

In regression models with first-order terms only, the coefficient for a given variable is typically interpreted as the change in the fitted value of Y for a one-unit increase in that variable, with all other variables held constant. Therefore, each regression coefficient represents the difference between two fitted values of Y. But the coefficients represent only a fraction of the possible fitted value comparisons that might be of interest to researchers. For many fitted value comparisons that are not captured by any of the regression coefficients, common statistical software packages do not provide the standard errors needed to compute confidence intervals or carry out statistical tests-particularly in more complex models that include interactions, polynomial terms, or regression splines. We describe two SPSS macros that implement a matrix algebra method for comparing any two fitted values from a regression model. The !OLScomp and !MLEcomp macros are for use with models fitted via ordinary least squares and maximum likelihood estimation, respectively. The output from the macros includes the standard error of the difference between the two fitted values, a 95% confidence interval for the difference, and a corresponding statistical test with its p-value.

Synchronized Trajectories in a Climate "Supermodel"

NASA Astrophysics Data System (ADS)

Duane, Gregory; Schevenhoven, Francine; Selten, Frank

2017-04-01

Differences in climate projections among state-of-the-art models can be resolved by connecting the models in run-time, either through inter-model nudging or by directly combining the tendencies for corresponding variables. Since it is clearly established that averaging model outputs typically results in improvement as compared to any individual model output, averaged re-initializations at typical analysis time intervals also seems appropriate. The resulting "supermodel" is more like a single model than it is like an ensemble, because the constituent models tend to synchronize even with limited inter-model coupling. Thus one can examine the properties of specific trajectories, rather than averaging the statistical properties of the separate models. We apply this strategy to a study of the index cycle in a supermodel constructed from several imperfect copies of the SPEEDO model (a global primitive-equation atmosphere-ocean-land climate model). As with blocking frequency, typical weather statistics of interest like probabilities of heat waves or extreme precipitation events, are improved as compared to the standard multi-model ensemble approach. In contrast to the standard approach, the supermodel approach provides detailed descriptions of typical actual events.

Correcting for population structure and kinship using the linear mixed model: theory and extensions.

PubMed

Hoffman, Gabriel E

2013-01-01

Population structure and kinship are widespread confounding factors in genome-wide association studies (GWAS). It has been standard practice to include principal components of the genotypes in a regression model in order to account for population structure. More recently, the linear mixed model (LMM) has emerged as a powerful method for simultaneously accounting for population structure and kinship. The statistical theory underlying the differences in empirical performance between modeling principal components as fixed versus random effects has not been thoroughly examined. We undertake an analysis to formalize the relationship between these widely used methods and elucidate the statistical properties of each. Moreover, we introduce a new statistic, effective degrees of freedom, that serves as a metric of model complexity and a novel low rank linear mixed model (LRLMM) to learn the dimensionality of the correction for population structure and kinship, and we assess its performance through simulations. A comparison of the results of LRLMM and a standard LMM analysis applied to GWAS data from the Multi-Ethnic Study of Atherosclerosis (MESA) illustrates how our theoretical results translate into empirical properties of the mixed model. Finally, the analysis demonstrates the ability of the LRLMM to substantially boost the strength of an association for HDL cholesterol in Europeans.

The Balance-Scale Task Revisited: A Comparison of Statistical Models for Rule-Based and Information-Integration Theories of Proportional Reasoning

PubMed Central

Hofman, Abe D.; Visser, Ingmar; Jansen, Brenda R. J.; van der Maas, Han L. J.

2015-01-01

We propose and test three statistical models for the analysis of children’s responses to the balance scale task, a seminal task to study proportional reasoning. We use a latent class modelling approach to formulate a rule-based latent class model (RB LCM) following from a rule-based perspective on proportional reasoning and a new statistical model, the Weighted Sum Model, following from an information-integration approach. Moreover, a hybrid LCM using item covariates is proposed, combining aspects of both a rule-based and information-integration perspective. These models are applied to two different datasets, a standard paper-and-pencil test dataset (N = 779), and a dataset collected within an online learning environment that included direct feedback, time-pressure, and a reward system (N = 808). For the paper-and-pencil dataset the RB LCM resulted in the best fit, whereas for the online dataset the hybrid LCM provided the best fit. The standard paper-and-pencil dataset yielded more evidence for distinct solution rules than the online data set in which quantitative item characteristics are more prominent in determining responses. These results shed new light on the discussion on sequential rule-based and information-integration perspectives of cognitive development. PMID:26505905

Apparent cosmic acceleration from Type Ia supernovae

NASA Astrophysics Data System (ADS)

Dam, Lawrence H.; Heinesen, Asta; Wiltshire, David L.

2017-11-01

Parameters that quantify the acceleration of cosmic expansion are conventionally determined within the standard Friedmann-Lemaître-Robertson-Walker (FLRW) model, which fixes spatial curvature to be homogeneous. Generic averages of Einstein's equations in inhomogeneous cosmology lead to models with non-rigidly evolving average spatial curvature, and different parametrizations of apparent cosmic acceleration. The timescape cosmology is a viable example of such a model without dark energy. Using the largest available supernova data set, the JLA catalogue, we find that the timescape model fits the luminosity distance-redshift data with a likelihood that is statistically indistinguishable from the standard spatially flat Λ cold dark matter cosmology by Bayesian comparison. In the timescape case cosmic acceleration is non-zero but has a marginal amplitude, with best-fitting apparent deceleration parameter, q_{0}=-0.043^{+0.004}_{-0.000}. Systematic issues regarding standardization of supernova light curves are analysed. Cuts of data at the statistical homogeneity scale affect light-curve parameter fits independent of cosmology. A cosmological model dependence of empirical changes to the mean colour parameter is also found. Irrespective of which model ultimately fits better, we argue that as a competitive model with a non-FLRW expansion history, the timescape model may prove a useful diagnostic tool for disentangling selection effects and astrophysical systematics from the underlying expansion history.

Statistical analysis of water-quality data containing multiple detection limits: S-language software for regression on order statistics

USGS Publications Warehouse

Lee, L.; Helsel, D.

2005-01-01

Trace contaminants in water, including metals and organics, often are measured at sufficiently low concentrations to be reported only as values below the instrument detection limit. Interpretation of these "less thans" is complicated when multiple detection limits occur. Statistical methods for multiply censored, or multiple-detection limit, datasets have been developed for medical and industrial statistics, and can be employed to estimate summary statistics or model the distributions of trace-level environmental data. We describe S-language-based software tools that perform robust linear regression on order statistics (ROS). The ROS method has been evaluated as one of the most reliable procedures for developing summary statistics of multiply censored data. It is applicable to any dataset that has 0 to 80% of its values censored. These tools are a part of a software library, or add-on package, for the R environment for statistical computing. This library can be used to generate ROS models and associated summary statistics, plot modeled distributions, and predict exceedance probabilities of water-quality standards. ?? 2005 Elsevier Ltd. All rights reserved.

A new item response theory model to adjust data allowing examinee choice

PubMed Central

Costa, Marcelo Azevedo; Braga Oliveira, Rivert Paulo

2018-01-01

In a typical questionnaire testing situation, examinees are not allowed to choose which items they answer because of a technical issue in obtaining satisfactory statistical estimates of examinee ability and item difficulty. This paper introduces a new item response theory (IRT) model that incorporates information from a novel representation of questionnaire data using network analysis. Three scenarios in which examinees select a subset of items were simulated. In the first scenario, the assumptions required to apply the standard Rasch model are met, thus establishing a reference for parameter accuracy. The second and third scenarios include five increasing levels of violating those assumptions. The results show substantial improvements over the standard model in item parameter recovery. Furthermore, the accuracy was closer to the reference in almost every evaluated scenario. To the best of our knowledge, this is the first proposal to obtain satisfactory IRT statistical estimates in the last two scenarios. PMID:29389996

Infants are superior in implicit crossmodal learning and use other learning mechanisms than adults

PubMed Central

von Frieling, Marco; Röder, Brigitte

2017-01-01

During development internal models of the sensory world must be acquired which have to be continuously adapted later. We used event-related potentials (ERP) to test the hypothesis that infants extract crossmodal statistics implicitly while adults learn them when task relevant. Participants were passively exposed to frequent standard audio-visual combinations (A1V1, A2V2, p=0.35 each), rare recombinations of these standard stimuli (A1V2, A2V1, p=0.10 each), and a rare audio-visual deviant with infrequent auditory and visual elements (A3V3, p=0.10). While both six-month-old infants and adults differentiated between rare deviants and standards involving early neural processing stages only infants were sensitive to crossmodal statistics as indicated by a late ERP difference between standard and recombined stimuli. A second experiment revealed that adults differentiated recombined and standard combinations when crossmodal combinations were task relevant. These results demonstrate a heightened sensitivity for crossmodal statistics in infants and a change in learning mode from infancy to adulthood. PMID:28949291

Beyond δ : Tailoring marked statistics to reveal modified gravity

NASA Astrophysics Data System (ADS)

Valogiannis, Georgios; Bean, Rachel

2018-01-01

Models that seek to explain cosmic acceleration through modifications to general relativity (GR) evade stringent Solar System constraints through a restoring, screening mechanism. Down-weighting the high-density, screened regions in favor of the low density, unscreened ones offers the potential to enhance the amount of information carried in such modified gravity models. In this work, we assess the performance of a new "marked" transformation and perform a systematic comparison with the clipping and logarithmic transformations, in the context of Λ CDM and the symmetron and f (R ) modified gravity models. Performance is measured in terms of the fractional boost in the Fisher information and the signal-to-noise ratio (SNR) for these models relative to the statistics derived from the standard density distribution. We find that all three statistics provide improved Fisher boosts over the basic density statistics. The model parameters for the marked and clipped transformation that best enhance signals and the Fisher boosts are determined. We also show that the mark is useful both as a Fourier and real-space transformation; a marked correlation function also enhances the SNR relative to the standard correlation function, and can on mildly nonlinear scales show a significant difference between the Λ CDM and the modified gravity models. Our results demonstrate how a series of simple analytical transformations could dramatically increase the predicted information extracted on deviations from GR, from large-scale surveys, and give the prospect for a much more feasible potential detection.

20 CFR 634.4 - Statistical standards.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 20 Employees' Benefits 3 2011-04-01 2011-04-01 false Statistical standards. 634.4 Section 634.4... System § 634.4 Statistical standards. Recipients shall agree to provide required data following the statistical standards prescribed by the Bureau of Labor Statistics for cooperative statistical programs. ...

20 CFR 634.4 - Statistical standards.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false Statistical standards. 634.4 Section 634.4... System § 634.4 Statistical standards. Recipients shall agree to provide required data following the statistical standards prescribed by the Bureau of Labor Statistics for cooperative statistical programs. ...

Particle size distributions by transmission electron microscopy: an interlaboratory comparison case study

PubMed Central

Rice, Stephen B; Chan, Christopher; Brown, Scott C; Eschbach, Peter; Han, Li; Ensor, David S; Stefaniak, Aleksandr B; Bonevich, John; Vladár, András E; Hight Walker, Angela R; Zheng, Jiwen; Starnes, Catherine; Stromberg, Arnold; Ye, Jia; Grulke, Eric A

2015-01-01

This paper reports an interlaboratory comparison that evaluated a protocol for measuring and analysing the particle size distribution of discrete, metallic, spheroidal nanoparticles using transmission electron microscopy (TEM). The study was focused on automated image capture and automated particle analysis. NIST RM8012 gold nanoparticles (30 nm nominal diameter) were measured for area-equivalent diameter distributions by eight laboratories. Statistical analysis was used to (1) assess the data quality without using size distribution reference models, (2) determine reference model parameters for different size distribution reference models and non-linear regression fitting methods and (3) assess the measurement uncertainty of a size distribution parameter by using its coefficient of variation. The interlaboratory area-equivalent diameter mean, 27.6 nm ± 2.4 nm (computed based on a normal distribution), was quite similar to the area-equivalent diameter, 27.6 nm, assigned to NIST RM8012. The lognormal reference model was the preferred choice for these particle size distributions as, for all laboratories, its parameters had lower relative standard errors (RSEs) than the other size distribution reference models tested (normal, Weibull and Rosin–Rammler–Bennett). The RSEs for the fitted standard deviations were two orders of magnitude higher than those for the fitted means, suggesting that most of the parameter estimate errors were associated with estimating the breadth of the distributions. The coefficients of variation for the interlaboratory statistics also confirmed the lognormal reference model as the preferred choice. From quasi-linear plots, the typical range for good fits between the model and cumulative number-based distributions was 1.9 fitted standard deviations less than the mean to 2.3 fitted standard deviations above the mean. Automated image capture, automated particle analysis and statistical evaluation of the data and fitting coefficients provide a framework for assessing nanoparticle size distributions using TEM for image acquisition. PMID:26361398

10 CFR 420.2 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Planning Organization means that organization required by the Department of Transportation, and designated... planning provisions in a Standard Metropolitan Statistical Area. Model Energy Code, 1993, including Errata, means the model building code published by the Council of American Building Officials, which is...

Statistical analysis of arsenic contamination in drinking water in a city of Iran and its modeling using GIS.

PubMed

Sadeghi, Fatemeh; Nasseri, Simin; Mosaferi, Mohammad; Nabizadeh, Ramin; Yunesian, Masud; Mesdaghinia, Alireza

2017-05-01

In this research, probable arsenic contamination in drinking water in the city of Ardabil was studied in 163 samples during four seasons. In each season, sampling was carried out randomly in the study area. Results were analyzed statistically applying SPSS 19 software, and the data was also modeled by Arc GIS 10.1 software. The maximum permissible arsenic concentration in drinking water defined by the World Health Organization and Iranian national standard is 10 μg/L. Statistical analysis showed 75, 88, 47, and 69% of samples in autumn, winter, spring, and summer, respectively, had concentrations higher than the national standard. The mean concentrations of arsenic in autumn, winter, spring, and summer were 19.89, 15.9, 10.87, and 14.6 μg/L, respectively, and the overall average in all samples through the year was 15.32 μg/L. Although GIS outputs indicated that the concentration distribution profiles changed in four consecutive seasons, variance analysis of the results showed that statistically there is no significant difference in arsenic levels in four seasons.

Independent review : statistical analyses of relationship between vehicle curb weight, track width, wheelbase and fatality rates.

DOT National Transportation Integrated Search

2011-03-01

"NHTSA selected the vehicle footprint (the measure of a vehicles wheelbase multiplied by its average track width) as the attribute upon which to base the CAFE standards for model year 2012-2016 passenger cars and light trucks. These standards are ...

Statistical prediction with Kanerva's sparse distributed memory

NASA Technical Reports Server (NTRS)

Rogers, David

1989-01-01

A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presented. In conditions of near- or over-capacity, where the associative-memory behavior of the model breaks down, the processing performed by the model can be interpreted as that of a statistical predictor. Mathematical results are presented which serve as the framework for a new statistical viewpoint of sparse distributed memory and for which the standard formulation of SDM is a special case. This viewpoint suggests possible enhancements to the SDM model, including a procedure for improving the predictiveness of the system based on Holland's work with genetic algorithms, and a method for improving the capacity of SDM even when used as an associative memory.

The Real World Significance of Performance Prediction

ERIC Educational Resources Information Center

Pardos, Zachary A.; Wang, Qing Yang; Trivedi, Shubhendu

2012-01-01

In recent years, the educational data mining and user modeling communities have been aggressively introducing models for predicting student performance on external measures such as standardized tests as well as within-tutor performance. While these models have brought statistically reliable improvement to performance prediction, the real world…

Model fit evaluation in multilevel structural equation models

PubMed Central

Ryu, Ehri

2014-01-01

Assessing goodness of model fit is one of the key questions in structural equation modeling (SEM). Goodness of fit is the extent to which the hypothesized model reproduces the multivariate structure underlying the set of variables. During the earlier development of multilevel structural equation models, the “standard” approach was to evaluate the goodness of fit for the entire model across all levels simultaneously. The model fit statistics produced by the standard approach have a potential problem in detecting lack of fit in the higher-level model for which the effective sample size is much smaller. Also when the standard approach results in poor model fit, it is not clear at which level the model does not fit well. This article reviews two alternative approaches that have been proposed to overcome the limitations of the standard approach. One is a two-step procedure which first produces estimates of saturated covariance matrices at each level and then performs single-level analysis at each level with the estimated covariance matrices as input (Yuan and Bentler, 2007). The other level-specific approach utilizes partially saturated models to obtain test statistics and fit indices for each level separately (Ryu and West, 2009). Simulation studies (e.g., Yuan and Bentler, 2007; Ryu and West, 2009) have consistently shown that both alternative approaches performed well in detecting lack of fit at any level, whereas the standard approach failed to detect lack of fit at the higher level. It is recommended that the alternative approaches are used to assess the model fit in multilevel structural equation model. Advantages and disadvantages of the two alternative approaches are discussed. The alternative approaches are demonstrated in an empirical example. PMID:24550882

Study on Standard Fatigue Vehicle Load Model

NASA Astrophysics Data System (ADS)

Huang, H. Y.; Zhang, J. P.; Li, Y. H.

2018-02-01

Based on the measured data of truck from three artery expressways in Guangdong Province, the statistical analysis of truck weight was conducted according to axle number. The standard fatigue vehicle model applied to industrial areas in the middle and late was obtained, which adopted equivalence damage principle, Miner linear accumulation law, water discharge method and damage ratio theory. Compared with the fatigue vehicle model Specified by the current bridge design code, the proposed model has better applicability. It is of certain reference value for the fatigue design of bridge in China.

Statistical tools for transgene copy number estimation based on real-time PCR.

PubMed

Yuan, Joshua S; Burris, Jason; Stewart, Nathan R; Mentewab, Ayalew; Stewart, C Neal

2007-11-01

As compared with traditional transgene copy number detection technologies such as Southern blot analysis, real-time PCR provides a fast, inexpensive and high-throughput alternative. However, the real-time PCR based transgene copy number estimation tends to be ambiguous and subjective stemming from the lack of proper statistical analysis and data quality control to render a reliable estimation of copy number with a prediction value. Despite the recent progresses in statistical analysis of real-time PCR, few publications have integrated these advancements in real-time PCR based transgene copy number determination. Three experimental designs and four data quality control integrated statistical models are presented. For the first method, external calibration curves are established for the transgene based on serially-diluted templates. The Ct number from a control transgenic event and putative transgenic event are compared to derive the transgene copy number or zygosity estimation. Simple linear regression and two group T-test procedures were combined to model the data from this design. For the second experimental design, standard curves were generated for both an internal reference gene and the transgene, and the copy number of transgene was compared with that of internal reference gene. Multiple regression models and ANOVA models can be employed to analyze the data and perform quality control for this approach. In the third experimental design, transgene copy number is compared with reference gene without a standard curve, but rather, is based directly on fluorescence data. Two different multiple regression models were proposed to analyze the data based on two different approaches of amplification efficiency integration. Our results highlight the importance of proper statistical treatment and quality control integration in real-time PCR-based transgene copy number determination. These statistical methods allow the real-time PCR-based transgene copy number estimation to be more reliable and precise with a proper statistical estimation. Proper confidence intervals are necessary for unambiguous prediction of trangene copy number. The four different statistical methods are compared for their advantages and disadvantages. Moreover, the statistical methods can also be applied for other real-time PCR-based quantification assays including transfection efficiency analysis and pathogen quantification.

Statistical study of air pollutant concentrations via generalized gamma distribution

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

Marani, A.; Lavagnini, I.; Buttazzoni, C.

1986-11-01

This paper deals with modeling observed frequency distributions of air quality data measured in the area of Venice, Italy. The paper discusses the application of the generalized gamma distribution (ggd) which has not been commonly applied to air quality data notwithstanding the fact that it embodies most distribution models used for air quality analyses. The approach yields important simplifications for statistical analyses. A comparison among the ggd and other relevant models (standard gamma, Weibull, lognormal), carried out on daily sulfur dioxide concentrations in the area of Venice underlines the efficiency of ggd models in portraying experimental data.

A method to preserve trends in quantile mapping bias correction of climate modeled temperature

NASA Astrophysics Data System (ADS)

Grillakis, Manolis G.; Koutroulis, Aristeidis G.; Daliakopoulos, Ioannis N.; Tsanis, Ioannis K.

2017-09-01

Bias correction of climate variables is a standard practice in climate change impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long-term statistics due to the time dependency of the temperature bias. Here, a method to overcome this issue without compromising the day-to-day correction statistics is presented. The methodology separates the modeled temperature signal into a normalized and a residual component relative to the modeled reference period climatology, in order to adjust the biases only for the former and preserve the signal of the later. The results show that this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. To illustrate the improvements, the methodology is tested on daily time series obtained from five Euro CORDEX regional climate models (RCMs).

Incorporating principal component analysis into air quality model evaluation

EPA Science Inventory

The efficacy of standard air quality model evaluation techniques is becoming compromised as the simulation periods continue to lengthen in response to ever increasing computing capacity. Accordingly, the purpose of this paper is to demonstrate a statistical approach called Princi...

Manpower Projection Model Project, Ventura County.

ERIC Educational Resources Information Center

Van Zant, John L.; Lawson, William H.

The final report on Phase 1 of the Manpower Projection Model (MPM) Project provides a guide for implementation of the model system by area Vocational Education Practitioners within any Standard Metropolitan Statistical Area (SMSA). A cooperative effort between Ventura County Superintendent of Schools Office and the Community College District, the…

Bias and inference from misspecified mixed-effect models in stepped wedge trial analysis.

PubMed

Thompson, Jennifer A; Fielding, Katherine L; Davey, Calum; Aiken, Alexander M; Hargreaves, James R; Hayes, Richard J

2017-10-15

Many stepped wedge trials (SWTs) are analysed by using a mixed-effect model with a random intercept and fixed effects for the intervention and time periods (referred to here as the standard model). However, it is not known whether this model is robust to misspecification. We simulated SWTs with three groups of clusters and two time periods; one group received the intervention during the first period and two groups in the second period. We simulated period and intervention effects that were either common-to-all or varied-between clusters. Data were analysed with the standard model or with additional random effects for period effect or intervention effect. In a second simulation study, we explored the weight given to within-cluster comparisons by simulating a larger intervention effect in the group of the trial that experienced both the control and intervention conditions and applying the three analysis models described previously. Across 500 simulations, we computed bias and confidence interval coverage of the estimated intervention effect. We found up to 50% bias in intervention effect estimates when period or intervention effects varied between clusters and were treated as fixed effects in the analysis. All misspecified models showed undercoverage of 95% confidence intervals, particularly the standard model. A large weight was given to within-cluster comparisons in the standard model. In the SWTs simulated here, mixed-effect models were highly sensitive to departures from the model assumptions, which can be explained by the high dependence on within-cluster comparisons. Trialists should consider including a random effect for time period in their SWT analysis model. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Heavy flavor decay of Zγ at CDF

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

Timothy M. Harrington-Taber

2013-01-01

Diboson production is an important and frequently measured parameter of the Standard Model. This analysis considers the previously neglected pmore » $$\\bar{p}$$ →Z γ→ b$$\\bar{b}$$ channel, as measured at the Collider Detector at Fermilab. Using the entire Tevatron Run II dataset, the measured result is consistent with Standard Model predictions, but the statistical error associated with this method of measurement limits the strength of this correlation.« less

Academic Outcome Measures of a Dedicated Education Unit Over Time: Help or Hinder?

PubMed

Smyer, Tish; Gatlin, Tricia; Tan, Rhigel; Tejada, Marianne; Feng, Du

2015-01-01

Critical thinking, nursing process, quality and safety measures, and standardized RN exit examination scores were compared between students (n = 144) placed in a dedicated education unit (DEU) and those in a traditional clinical model. Standardized test scores showed that differences between the clinical groups were not statistically significant. This study shows that the DEU model is 1 approach to clinical education that can enhance students' academic outcomes.

Probabilistic performance estimators for computational chemistry methods: The empirical cumulative distribution function of absolute errors

NASA Astrophysics Data System (ADS)

Pernot, Pascal; Savin, Andreas

2018-06-01

Benchmarking studies in computational chemistry use reference datasets to assess the accuracy of a method through error statistics. The commonly used error statistics, such as the mean signed and mean unsigned errors, do not inform end-users on the expected amplitude of prediction errors attached to these methods. We show that, the distributions of model errors being neither normal nor zero-centered, these error statistics cannot be used to infer prediction error probabilities. To overcome this limitation, we advocate for the use of more informative statistics, based on the empirical cumulative distribution function of unsigned errors, namely, (1) the probability for a new calculation to have an absolute error below a chosen threshold and (2) the maximal amplitude of errors one can expect with a chosen high confidence level. Those statistics are also shown to be well suited for benchmarking and ranking studies. Moreover, the standard error on all benchmarking statistics depends on the size of the reference dataset. Systematic publication of these standard errors would be very helpful to assess the statistical reliability of benchmarking conclusions.

Statistics of SU(5) D-brane models on a type II orientifold

NASA Astrophysics Data System (ADS)

Gmeiner, Florian; Stein, Maren

2006-06-01

We perform a statistical analysis of models with SU(5) and flipped SU(5) gauge group in a type II orientifold setup. We investigate the distribution and correlation of properties of these models, including the number of generations and the hidden sector gauge group. Compared to the recent analysis [F. Gmeiner, R. Blumenhagen, G. Honecker, D. Lüst, and T. Weigand, J. High Energy Phys.JHEPFG1029-8479 01 (2006) 004; F. Gmeiner, Fortschr. Phys.FPYKA60015-8208 54, 391 (2006).10.1088/1126-6708/2006/01/004] of models with a standard model-like gauge group, we find very similar results.

A statistical model for interpreting computerized dynamic posturography data

NASA Technical Reports Server (NTRS)

Feiveson, Alan H.; Metter, E. Jeffrey; Paloski, William H.

2002-01-01

Computerized dynamic posturography (CDP) is widely used for assessment of altered balance control. CDP trials are quantified using the equilibrium score (ES), which ranges from zero to 100, as a decreasing function of peak sway angle. The problem of how best to model and analyze ESs from a controlled study is considered. The ES often exhibits a skewed distribution in repeated trials, which can lead to incorrect inference when applying standard regression or analysis of variance models. Furthermore, CDP trials are terminated when a patient loses balance. In these situations, the ES is not observable, but is assigned the lowest possible score--zero. As a result, the response variable has a mixed discrete-continuous distribution, further compromising inference obtained by standard statistical methods. Here, we develop alternative methodology for analyzing ESs under a stochastic model extending the ES to a continuous latent random variable that always exists, but is unobserved in the event of a fall. Loss of balance occurs conditionally, with probability depending on the realized latent ES. After fitting the model by a form of quasi-maximum-likelihood, one may perform statistical inference to assess the effects of explanatory variables. An example is provided, using data from the NIH/NIA Baltimore Longitudinal Study on Aging.

Cross-validation of Peak Oxygen Consumption Prediction Models From OMNI Perceived Exertion.

PubMed

Mays, R J; Goss, F L; Nagle, E F; Gallagher, M; Haile, L; Schafer, M A; Kim, K H; Robertson, R J

2016-09-01

This study cross-validated statistical models for prediction of peak oxygen consumption using ratings of perceived exertion from the Adult OMNI Cycle Scale of Perceived Exertion. 74 participants (men: n=36; women: n=38) completed a graded cycle exercise test. Ratings of perceived exertion for the overall body, legs, and chest/breathing were recorded each test stage and entered into previously developed 3-stage peak oxygen consumption prediction models. There were no significant differences (p>0.05) between measured and predicted peak oxygen consumption from ratings of perceived exertion for the overall body, legs, and chest/breathing within men (mean±standard deviation: 3.16±0.52 vs. 2.92±0.33 vs. 2.90±0.29 vs. 2.90±0.26 L·min(-1)) and women (2.17±0.29 vs. 2.02±0.22 vs. 2.03±0.19 vs. 2.01±0.19 L·min(-1)) participants. Previously developed statistical models for prediction of peak oxygen consumption based on subpeak OMNI ratings of perceived exertion responses were similar to measured peak oxygen consumption in a separate group of participants. These findings provide practical implications for the use of the original statistical models in standard health-fitness settings. © Georg Thieme Verlag KG Stuttgart · New York.

A Method of Relating General Circulation Model Simulated Climate to the Observed Local Climate. Part I: Seasonal Statistics.

NASA Astrophysics Data System (ADS)

Karl, Thomas R.; Wang, Wei-Chyung; Schlesinger, Michael E.; Knight, Richard W.; Portman, David

1990-10-01

Important surface observations such as the daily maximum and minimum temperature, daily precipitation, and cloud ceilings often have localized characteristics that are difficult to reproduce with the current resolution and the physical parameterizations in state-of-the-art General Circulation climate Models (GCMs). Many of the difficulties can be partially attributed to mismatches in scale, local topography. regional geography and boundary conditions between models and surface-based observations. Here, we present a method, called climatological projection by model statistics (CPMS), to relate GCM grid-point flee-atmosphere statistics, the predictors, to these important local surface observations. The method can be viewed as a generalization of the model output statistics (MOS) and perfect prog (PP) procedures used in numerical weather prediction (NWP) models. It consists of the application of three statistical methods: 1) principle component analysis (FICA), 2) canonical correlation, and 3) inflated regression analysis. The PCA reduces the redundancy of the predictors The canonical correlation is used to develop simultaneous relationships between linear combinations of the predictors, the canonical variables, and the surface-based observations. Finally, inflated regression is used to relate the important canonical variables to each of the surface-based observed variables.We demonstrate that even an early version of the Oregon State University two-level atmospheric GCM (with prescribed sea surface temperature) produces free-atmosphere statistics than can, when standardized using the model's internal means and variances (the MOS-like version of CPMS), closely approximate the observed local climate. When the model data are standardized by the observed free-atmosphere means and variances (the PP version of CPMS), however, the model does not reproduce the observed surface climate as well. Our results indicate that in the MOS-like version of CPMS the differences between the output of a ten-year GCM control run and the surface-based observations are often smaller than the differences between the observations of two ten-year periods. Such positive results suggest that GCMs may already contain important climatological information that can be used to infer the local climate.

Cleanroom certification model

NASA Technical Reports Server (NTRS)

Currit, P. A.

1983-01-01

The Cleanroom software development methodology is designed to take the gamble out of product releases for both suppliers and receivers of the software. The ingredients of this procedure are a life cycle of executable product increments, representative statistical testing, and a standard estimate of the MTTF (Mean Time To Failure) of the product at the time of its release. A statistical approach to software product testing using randomly selected samples of test cases is considered. A statistical model is defined for the certification process which uses the timing data recorded during test. A reasonableness argument for this model is provided that uses previously published data on software product execution. Also included is a derivation of the certification model estimators and a comparison of the proposed least squares technique with the more commonly used maximum likelihood estimators.

Risk Estimation for Lung Cancer in Libya: Analysis Based on Standardized Morbidity Ratio, Poisson-Gamma Model, BYM Model and Mixture Model

PubMed

Alhdiri, Maryam Ahmed; Samat, Nor Azah; Mohamed, Zulkifley

2017-03-01

Cancer is the most rapidly spreading disease in the world, especially in developing countries, including Libya. Cancer represents a significant burden on patients, families, and their societies. This disease can be controlled if detected early. Therefore, disease mapping has recently become an important method in the fields of public health research and disease epidemiology. The correct choice of statistical model is a very important step to producing a good map of a disease. Libya was selected to perform this work and to examine its geographical variation in the incidence of lung cancer. The objective of this paper is to estimate the relative risk for lung cancer. Four statistical models to estimate the relative risk for lung cancer and population censuses of the study area for the time period 2006 to 2011 were used in this work. They are initially known as Standardized Morbidity Ratio, which is the most popular statistic, which used in the field of disease mapping, Poisson-gamma model, which is one of the earliest applications of Bayesian methodology, Besag, York and Mollie (BYM) model and Mixture model. As an initial step, this study begins by providing a review of all proposed models, which we then apply to lung cancer data in Libya. Maps, tables and graph, goodness-of-fit (GOF) were used to compare and present the preliminary results. This GOF is common in statistical modelling to compare fitted models. The main general results presented in this study show that the Poisson-gamma model, BYM model, and Mixture model can overcome the problem of the first model (SMR) when there is no observed lung cancer case in certain districts. Results show that the Mixture model is most robust and provides better relative risk estimates across a range of models. Creative Commons Attribution License

Risk Estimation for Lung Cancer in Libya: Analysis Based on Standardized Morbidity Ratio, Poisson-Gamma Model, BYM Model and Mixture Model

PubMed Central

Alhdiri, Maryam Ahmed; Samat, Nor Azah; Mohamed, Zulkifley

2017-01-01

Cancer is the most rapidly spreading disease in the world, especially in developing countries, including Libya. Cancer represents a significant burden on patients, families, and their societies. This disease can be controlled if detected early. Therefore, disease mapping has recently become an important method in the fields of public health research and disease epidemiology. The correct choice of statistical model is a very important step to producing a good map of a disease. Libya was selected to perform this work and to examine its geographical variation in the incidence of lung cancer. The objective of this paper is to estimate the relative risk for lung cancer. Four statistical models to estimate the relative risk for lung cancer and population censuses of the study area for the time period 2006 to 2011 were used in this work. They are initially known as Standardized Morbidity Ratio, which is the most popular statistic, which used in the field of disease mapping, Poisson-gamma model, which is one of the earliest applications of Bayesian methodology, Besag, York and Mollie (BYM) model and Mixture model. As an initial step, this study begins by providing a review of all proposed models, which we then apply to lung cancer data in Libya. Maps, tables and graph, goodness-of-fit (GOF) were used to compare and present the preliminary results. This GOF is common in statistical modelling to compare fitted models. The main general results presented in this study show that the Poisson-gamma model, BYM model, and Mixture model can overcome the problem of the first model (SMR) when there is no observed lung cancer case in certain districts. Results show that the Mixture model is most robust and provides better relative risk estimates across a range of models. PMID:28440974

How Does One Assess the Accuracy of Academic Success Predictors? ROC Analysis Applied to University Entrance Factors

ERIC Educational Resources Information Center

Vivo, Juana-Maria; Franco, Manuel

2008-01-01

This article attempts to present a novel application of a method of measuring accuracy for academic success predictors that could be used as a standard. This procedure is known as the receiver operating characteristic (ROC) curve, which comes from statistical decision techniques. The statistical prediction techniques provide predictor models and…

Analysis of Multiple Contingency Tables by Exact Conditional Tests for Zero Partial Association.

ERIC Educational Resources Information Center

Kreiner, Svend

The tests for zero partial association in a multiple contingency table have gained new importance with the introduction of graphical models. It is shown how these may be performed as exact conditional tests, using as test criteria either the ordinary likelihood ratio, the standard x squared statistic, or any other appropriate statistics. A…

Quantifying falsifiability of scientific theories

NASA Astrophysics Data System (ADS)

Nemenman, Ilya

I argue that the notion of falsifiability, a key concept in defining a valid scientific theory, can be quantified using Bayesian Model Selection, which is a standard tool in modern statistics. This relates falsifiability to the quantitative version of the statistical Occam's razor, and allows transforming some long-running arguments about validity of scientific theories from philosophical discussions to rigorous mathematical calculations.

Metrological traceability in education: A practical online system for measuring and managing middle school mathematics instruction

NASA Astrophysics Data System (ADS)

Torres Irribarra, D.; Freund, R.; Fisher, W.; Wilson, M.

2015-02-01

Computer-based, online assessments modelled, designed, and evaluated for adaptively administered invariant measurement are uniquely suited to defining and maintaining traceability to standardized units in education. An assessment of this kind is embedded in the Assessing Data Modeling and Statistical Reasoning (ADM) middle school mathematics curriculum. Diagnostic information about middle school students' learning of statistics and modeling is provided via computer-based formative assessments for seven constructs that comprise a learning progression for statistics and modeling from late elementary through the middle school grades. The seven constructs are: Data Display, Meta-Representational Competence, Conceptions of Statistics, Chance, Modeling Variability, Theory of Measurement, and Informal Inference. The end product is a web-delivered system built with Ruby on Rails for use by curriculum development teams working with classroom teachers in designing, developing, and delivering formative assessments. The online accessible system allows teachers to accurately diagnose students' unique comprehension and learning needs in a common language of real-time assessment, logging, analysis, feedback, and reporting.

Tests of Alignment among Assessment, Standards, and Instruction Using Generalized Linear Model Regression

ERIC Educational Resources Information Center

Fulmer, Gavin W.; Polikoff, Morgan S.

2014-01-01

An essential component in school accountability efforts is for assessments to be well-aligned with the standards or curriculum they are intended to measure. However, relatively little prior research has explored methods to determine statistical significance of alignment or misalignment. This study explores analyses of alignment as a special case…

Estimation of social value of statistical life using willingness-to-pay method in Nanjing, China.

PubMed

Yang, Zhao; Liu, Pan; Xu, Xin

2016-10-01

Rational decision making regarding the safety related investment programs greatly depends on the economic valuation of traffic crashes. The primary objective of this study was to estimate the social value of statistical life in the city of Nanjing in China. A stated preference survey was conducted to investigate travelers' willingness to pay for traffic risk reduction. Face-to-face interviews were conducted at stations, shopping centers, schools, and parks in different districts in the urban area of Nanjing. The respondents were categorized into two groups, including motorists and non-motorists. Both the binary logit model and mixed logit model were developed for the two groups of people. The results revealed that the mixed logit model is superior to the fixed coefficient binary logit model. The factors that significantly affect people's willingness to pay for risk reduction include income, education, gender, age, drive age (for motorists), occupation, whether the charged fees were used to improve private vehicle equipment (for motorists), reduction in fatality rate, and change in travel cost. The Monte Carlo simulation method was used to generate the distribution of value of statistical life (VSL). Based on the mixed logit model, the VSL had a mean value of 3,729,493 RMB ($586,610) with a standard deviation of 2,181,592 RMB ($343,142) for motorists; and a mean of 3,281,283 RMB ($505,318) with a standard deviation of 2,376,975 RMB ($366,054) for non-motorists. Using the tax system to illustrate the contribution of different income groups to social funds, the social value of statistical life was estimated. The average social value of statistical life was found to be 7,184,406 RMB ($1,130,032). Copyright © 2016 Elsevier Ltd. All rights reserved.

Assessment of variations in thermal cycle life data of thermal barrier coated rods

NASA Astrophysics Data System (ADS)

Hendricks, R. C.; McDonald, G.

An analysis of thermal cycle life data for 22 thermal barrier coated (TBC) specimens was conducted. The Zr02-8Y203/NiCrAlY plasma spray coated Rene 41 rods were tested in a Mach 0.3 Jet A/air burner flame. All specimens were subjected to the same coating and subsequent test procedures in an effort to control three parametric groups; material properties, geometry and heat flux. Statistically, the data sample space had a mean of 1330 cycles with a standard deviation of 520 cycles. The data were described by normal or log-normal distributions, but other models could also apply; the sample size must be increased to clearly delineate a statistical failure model. The statistical methods were also applied to adhesive/cohesive strength data for 20 TBC discs of the same composition, with similar results. The sample space had a mean of 9 MPa with a standard deviation of 4.2 MPa.

Assessment of variations in thermal cycle life data of thermal barrier coated rods

NASA Technical Reports Server (NTRS)

Hendricks, R. C.; Mcdonald, G.

1981-01-01

An analysis of thermal cycle life data for 22 thermal barrier coated (TBC) specimens was conducted. The Zr02-8Y203/NiCrAlY plasma spray coated Rene 41 rods were tested in a Mach 0.3 Jet A/air burner flame. All specimens were subjected to the same coating and subsequent test procedures in an effort to control three parametric groups; material properties, geometry and heat flux. Statistically, the data sample space had a mean of 1330 cycles with a standard deviation of 520 cycles. The data were described by normal or log-normal distributions, but other models could also apply; the sample size must be increased to clearly delineate a statistical failure model. The statistical methods were also applied to adhesive/cohesive strength data for 20 TBC discs of the same composition, with similar results. The sample space had a mean of 9 MPa with a standard deviation of 4.2 MPa.

Using statistical and machine learning to help institutions detect suspicious access to electronic health records.

PubMed

Boxwala, Aziz A; Kim, Jihoon; Grillo, Janice M; Ohno-Machado, Lucila

2011-01-01

To determine whether statistical and machine-learning methods, when applied to electronic health record (EHR) access data, could help identify suspicious (ie, potentially inappropriate) access to EHRs. From EHR access logs and other organizational data collected over a 2-month period, the authors extracted 26 features likely to be useful in detecting suspicious accesses. Selected events were marked as either suspicious or appropriate by privacy officers, and served as the gold standard set for model evaluation. The authors trained logistic regression (LR) and support vector machine (SVM) models on 10-fold cross-validation sets of 1291 labeled events. The authors evaluated the sensitivity of final models on an external set of 58 events that were identified as truly inappropriate and investigated independently from this study using standard operating procedures. The area under the receiver operating characteristic curve of the models on the whole data set of 1291 events was 0.91 for LR, and 0.95 for SVM. The sensitivity of the baseline model on this set was 0.8. When the final models were evaluated on the set of 58 investigated events, all of which were determined as truly inappropriate, the sensitivity was 0 for the baseline method, 0.76 for LR, and 0.79 for SVM. The LR and SVM models may not generalize because of interinstitutional differences in organizational structures, applications, and workflows. Nevertheless, our approach for constructing the models using statistical and machine-learning techniques can be generalized. An important limitation is the relatively small sample used for the training set due to the effort required for its construction. The results suggest that statistical and machine-learning methods can play an important role in helping privacy officers detect suspicious accesses to EHRs.

Using statistical and machine learning to help institutions detect suspicious access to electronic health records

PubMed Central

Kim, Jihoon; Grillo, Janice M; Ohno-Machado, Lucila

2011-01-01

Objective To determine whether statistical and machine-learning methods, when applied to electronic health record (EHR) access data, could help identify suspicious (ie, potentially inappropriate) access to EHRs. Methods From EHR access logs and other organizational data collected over a 2-month period, the authors extracted 26 features likely to be useful in detecting suspicious accesses. Selected events were marked as either suspicious or appropriate by privacy officers, and served as the gold standard set for model evaluation. The authors trained logistic regression (LR) and support vector machine (SVM) models on 10-fold cross-validation sets of 1291 labeled events. The authors evaluated the sensitivity of final models on an external set of 58 events that were identified as truly inappropriate and investigated independently from this study using standard operating procedures. Results The area under the receiver operating characteristic curve of the models on the whole data set of 1291 events was 0.91 for LR, and 0.95 for SVM. The sensitivity of the baseline model on this set was 0.8. When the final models were evaluated on the set of 58 investigated events, all of which were determined as truly inappropriate, the sensitivity was 0 for the baseline method, 0.76 for LR, and 0.79 for SVM. Limitations The LR and SVM models may not generalize because of interinstitutional differences in organizational structures, applications, and workflows. Nevertheless, our approach for constructing the models using statistical and machine-learning techniques can be generalized. An important limitation is the relatively small sample used for the training set due to the effort required for its construction. Conclusion The results suggest that statistical and machine-learning methods can play an important role in helping privacy officers detect suspicious accesses to EHRs. PMID:21672912

Pattern statistics on Markov chains and sensitivity to parameter estimation

PubMed Central

Nuel, Grégory

2006-01-01

Background: In order to compute pattern statistics in computational biology a Markov model is commonly used to take into account the sequence composition. Usually its parameter must be estimated. The aim of this paper is to determine how sensitive these statistics are to parameter estimation, and what are the consequences of this variability on pattern studies (finding the most over-represented words in a genome, the most significant common words to a set of sequences,...). Results: In the particular case where pattern statistics (overlap counting only) computed through binomial approximations we use the delta-method to give an explicit expression of σ, the standard deviation of a pattern statistic. This result is validated using simulations and a simple pattern study is also considered. Conclusion: We establish that the use of high order Markov model could easily lead to major mistakes due to the high sensitivity of pattern statistics to parameter estimation. PMID:17044916

Pattern statistics on Markov chains and sensitivity to parameter estimation.

PubMed

Nuel, Grégory

2006-10-17

In order to compute pattern statistics in computational biology a Markov model is commonly used to take into account the sequence composition. Usually its parameter must be estimated. The aim of this paper is to determine how sensitive these statistics are to parameter estimation, and what are the consequences of this variability on pattern studies (finding the most over-represented words in a genome, the most significant common words to a set of sequences,...). In the particular case where pattern statistics (overlap counting only) computed through binomial approximations we use the delta-method to give an explicit expression of sigma, the standard deviation of a pattern statistic. This result is validated using simulations and a simple pattern study is also considered. We establish that the use of high order Markov model could easily lead to major mistakes due to the high sensitivity of pattern statistics to parameter estimation.

Composite Linear Models | Division of Cancer Prevention

Cancer.gov

By Stuart G. Baker The composite linear models software is a matrix approach to compute maximum likelihood estimates and asymptotic standard errors for models for incomplete multinomial data. It implements the method described in Baker SG. Composite linear models for incomplete multinomial data. Statistics in Medicine 1994;13:609-622. The software includes a library of thirty

The Equivalence of Regression Models Using Difference Scores and Models Using Separate Scores for Each Informant: Implications for the Study of Informant Discrepancies

ERIC Educational Resources Information Center

Laird, Robert D.; Weems, Carl F.

2011-01-01

Research on informant discrepancies has increasingly utilized difference scores. This article demonstrates the statistical equivalence of regression models using difference scores (raw or standardized) and regression models using separate scores for each informant to show that interpretations should be consistent with both models. First,…

Statistical properties of four effect-size measures for mediation models.

PubMed

Miočević, Milica; O'Rourke, Holly P; MacKinnon, David P; Brown, Hendricks C

2018-02-01

This project examined the performance of classical and Bayesian estimators of four effect size measures for the indirect effect in a single-mediator model and a two-mediator model. Compared to the proportion and ratio mediation effect sizes, standardized mediation effect-size measures were relatively unbiased and efficient in the single-mediator model and the two-mediator model. Percentile and bias-corrected bootstrap interval estimates of ab/s Y , and ab(s X )/s Y in the single-mediator model outperformed interval estimates of the proportion and ratio effect sizes in terms of power, Type I error rate, coverage, imbalance, and interval width. For the two-mediator model, standardized effect-size measures were superior to the proportion and ratio effect-size measures. Furthermore, it was found that Bayesian point and interval summaries of posterior distributions of standardized effect-size measures reduced excessive relative bias for certain parameter combinations. The standardized effect-size measures are the best effect-size measures for quantifying mediated effects.

Trends in study design and the statistical methods employed in a leading general medicine journal.

PubMed

Gosho, M; Sato, Y; Nagashima, K; Takahashi, S

2018-02-01

Study design and statistical methods have become core components of medical research, and the methodology has become more multifaceted and complicated over time. The study of the comprehensive details and current trends of study design and statistical methods is required to support the future implementation of well-planned clinical studies providing information about evidence-based medicine. Our purpose was to illustrate study design and statistical methods employed in recent medical literature. This was an extension study of Sato et al. (N Engl J Med 2017; 376: 1086-1087), which reviewed 238 articles published in 2015 in the New England Journal of Medicine (NEJM) and briefly summarized the statistical methods employed in NEJM. Using the same database, we performed a new investigation of the detailed trends in study design and individual statistical methods that were not reported in the Sato study. Due to the CONSORT statement, prespecification and justification of sample size are obligatory in planning intervention studies. Although standard survival methods (eg Kaplan-Meier estimator and Cox regression model) were most frequently applied, the Gray test and Fine-Gray proportional hazard model for considering competing risks were sometimes used for a more valid statistical inference. With respect to handling missing data, model-based methods, which are valid for missing-at-random data, were more frequently used than single imputation methods. These methods are not recommended as a primary analysis, but they have been applied in many clinical trials. Group sequential design with interim analyses was one of the standard designs, and novel design, such as adaptive dose selection and sample size re-estimation, was sometimes employed in NEJM. Model-based approaches for handling missing data should replace single imputation methods for primary analysis in the light of the information found in some publications. Use of adaptive design with interim analyses is increasing after the presentation of the FDA guidance for adaptive design. © 2017 John Wiley & Sons Ltd.

BTS statistical standards manual

DOT National Transportation Integrated Search

2005-10-01

The Bureau of Transportation Statistics (BTS), like other federal statistical agencies, establishes professional standards to guide the methods and procedures for the collection, processing, storage, and presentation of statistical data. Standards an...

40 CFR Appendix K to Part 50 - Interpretation of the National Ambient Air Quality Standards for Particulate Matter

Code of Federal Regulations, 2010 CFR

2010-07-01

..., other techniques, such as the use of statistical models or the use of historical data could be..., mathematical techniques should be applied to account for the trends to ensure that the expected annual values... emission patterns, either the most recent representative year(s) could be used or statistical techniques or...

A statistical analysis of seat belt effectiveness in 1973-1975 model cars involved in towaway crashes. Volume 1

DOT National Transportation Integrated Search

1976-09-01

Standardized injury rates and seat belt effectiveness measures are derived from a probability sample of towaway accidents involving 1973-1975 model cars. The data were collected in five different geographic regions. Weighted sample size available for...

The Consolidation/Transition Model in Moral Reasoning Development.

ERIC Educational Resources Information Center

Walker, Lawrence J.; Gustafson, Paul; Hennig, Karl H.

2001-01-01

This longitudinal study with 62 children and adolescents examined the validity of the consolidation/transition model in the context of moral reasoning development. Results of standard statistical and Bayesian techniques supported the hypotheses regarding cyclical patterns of change and predictors of stage transition, and demonstrated the utility…

TOMS and SBUV Data: Comparison to 3D Chemical-Transport Model Results

NASA Technical Reports Server (NTRS)

Stolarski, Richard S.; Douglass, Anne R.; Steenrod, Steve; Frith, Stacey

2003-01-01

We have updated our merged ozone data (MOD) set using the TOMS data from the new version 8 algorithm. We then analyzed these data for contributions from solar cycle, volcanoes, QBO, and halogens using a standard statistical time series model. We have recently completed a hindcast run of our 3D chemical-transport model for the same years. This model uses off-line winds from the finite-volume GCM, a full stratospheric photochemistry package, and time-varying forcing due to halogens, solar uv, and volcanic aerosols. We will report on a parallel analysis of these model results using the same statistical time series technique as used for the MOD data.

Bladder cancer mapping in Libya based on standardized morbidity ratio and log-normal model

NASA Astrophysics Data System (ADS)

Alhdiri, Maryam Ahmed; Samat, Nor Azah; Mohamed, Zulkifley

2017-05-01

Disease mapping contains a set of statistical techniques that detail maps of rates based on estimated mortality, morbidity, and prevalence. A traditional approach to measure the relative risk of the disease is called Standardized Morbidity Ratio (SMR). It is the ratio of an observed and expected number of accounts in an area, which has the greatest uncertainty if the disease is rare or if geographical area is small. Therefore, Bayesian models or statistical smoothing based on Log-normal model are introduced which might solve SMR problem. This study estimates the relative risk for bladder cancer incidence in Libya from 2006 to 2007 based on the SMR and log-normal model, which were fitted to data using WinBUGS software. This study starts with a brief review of these models, starting with the SMR method and followed by the log-normal model, which is then applied to bladder cancer incidence in Libya. All results are compared using maps and tables. The study concludes that the log-normal model gives better relative risk estimates compared to the classical method. The log-normal model has can overcome the SMR problem when there is no observed bladder cancer in an area.

Precision, Reliability, and Effect Size of Slope Variance in Latent Growth Curve Models: Implications for Statistical Power Analysis

PubMed Central

Brandmaier, Andreas M.; von Oertzen, Timo; Ghisletta, Paolo; Lindenberger, Ulman; Hertzog, Christopher

2018-01-01

Latent Growth Curve Models (LGCM) have become a standard technique to model change over time. Prediction and explanation of inter-individual differences in change are major goals in lifespan research. The major determinants of statistical power to detect individual differences in change are the magnitude of true inter-individual differences in linear change (LGCM slope variance), design precision, alpha level, and sample size. Here, we show that design precision can be expressed as the inverse of effective error. Effective error is determined by instrument reliability and the temporal arrangement of measurement occasions. However, it also depends on another central LGCM component, the variance of the latent intercept and its covariance with the latent slope. We derive a new reliability index for LGCM slope variance—effective curve reliability (ECR)—by scaling slope variance against effective error. ECR is interpretable as a standardized effect size index. We demonstrate how effective error, ECR, and statistical power for a likelihood ratio test of zero slope variance formally relate to each other and how they function as indices of statistical power. We also provide a computational approach to derive ECR for arbitrary intercept-slope covariance. With practical use cases, we argue for the complementary utility of the proposed indices of a study's sensitivity to detect slope variance when making a priori longitudinal design decisions or communicating study designs. PMID:29755377

Automated finite element modeling of the lumbar spine: Using a statistical shape model to generate a virtual population of models.

PubMed

Campbell, J Q; Petrella, A J

2016-09-06

Population-based modeling of the lumbar spine has the potential to be a powerful clinical tool. However, developing a fully parameterized model of the lumbar spine with accurate geometry has remained a challenge. The current study used automated methods for landmark identification to create a statistical shape model of the lumbar spine. The shape model was evaluated using compactness, generalization ability, and specificity. The primary shape modes were analyzed visually, quantitatively, and biomechanically. The biomechanical analysis was performed by using the statistical shape model with an automated method for finite element model generation to create a fully parameterized finite element model of the lumbar spine. Functional finite element models of the mean shape and the extreme shapes (±3 standard deviations) of all 17 shape modes were created demonstrating the robust nature of the methods. This study represents an advancement in finite element modeling of the lumbar spine and will allow population-based modeling in the future. Copyright © 2016 Elsevier Ltd. All rights reserved.

Assessing risk factors for dental caries: a statistical modeling approach.

PubMed

Trottini, Mario; Bossù, Maurizio; Corridore, Denise; Ierardo, Gaetano; Luzzi, Valeria; Saccucci, Matteo; Polimeni, Antonella

2015-01-01

The problem of identifying potential determinants and predictors of dental caries is of key importance in caries research and it has received considerable attention in the scientific literature. From the methodological side, a broad range of statistical models is currently available to analyze dental caries indices (DMFT, dmfs, etc.). These models have been applied in several studies to investigate the impact of different risk factors on the cumulative severity of dental caries experience. However, in most of the cases (i) these studies focus on a very specific subset of risk factors; and (ii) in the statistical modeling only few candidate models are considered and model selection is at best only marginally addressed. As a result, our understanding of the robustness of the statistical inferences with respect to the choice of the model is very limited; the richness of the set of statistical models available for analysis in only marginally exploited; and inferences could be biased due the omission of potentially important confounding variables in the model's specification. In this paper we argue that these limitations can be overcome considering a general class of candidate models and carefully exploring the model space using standard model selection criteria and measures of global fit and predictive performance of the candidate models. Strengths and limitations of the proposed approach are illustrated with a real data set. In our illustration the model space contains more than 2.6 million models, which require inferences to be adjusted for 'optimism'.

Probabilistic Evaluation of Competing Climate Models

NASA Astrophysics Data System (ADS)

Braverman, A. J.; Chatterjee, S.; Heyman, M.; Cressie, N.

2017-12-01

A standard paradigm for assessing the quality of climate model simulations is to compare what these models produce for past and present time periods, to observations of the past and present. Many of these comparisons are based on simple summary statistics called metrics. Here, we propose an alternative: evaluation of competing climate models through probabilities derived from tests of the hypothesis that climate-model-simulated and observed time sequences share common climate-scale signals. The probabilities are based on the behavior of summary statistics of climate model output and observational data, over ensembles of pseudo-realizations. These are obtained by partitioning the original time sequences into signal and noise components, and using a parametric bootstrap to create pseudo-realizations of the noise sequences. The statistics we choose come from working in the space of decorrelated and dimension-reduced wavelet coefficients. We compare monthly sequences of CMIP5 model output of average global near-surface temperature anomalies to similar sequences obtained from the well-known HadCRUT4 data set, as an illustration.

BIG BANG NUCLEOSYNTHESIS WITH A NON-MAXWELLIAN DISTRIBUTION

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

Bertulani, C. A.; Fuqua, J.; Hussein, M. S.

The abundances of light elements based on the big bang nucleosynthesis model are calculated using the Tsallis non-extensive statistics. The impact of the variation of the non-extensive parameter q from the unity value is compared to observations and to the abundance yields from the standard big bang model. We find large differences between the reaction rates and the abundance of light elements calculated with the extensive and the non-extensive statistics. We found that the observations are consistent with a non-extensive parameter q = 1{sub -} {sub 0.12}{sup +0.05}, indicating that a large deviation from the Boltzmann-Gibbs statistics (q = 1)more » is highly unlikely.« less

Infinite von Mises-Fisher Mixture Modeling of Whole Brain fMRI Data.

PubMed

Røge, Rasmus E; Madsen, Kristoffer H; Schmidt, Mikkel N; Mørup, Morten

2017-10-01

Cluster analysis of functional magnetic resonance imaging (fMRI) data is often performed using gaussian mixture models, but when the time series are standardized such that the data reside on a hypersphere, this modeling assumption is questionable. The consequences of ignoring the underlying spherical manifold are rarely analyzed, in part due to the computational challenges imposed by directional statistics. In this letter, we discuss a Bayesian von Mises-Fisher (vMF) mixture model for data on the unit hypersphere and present an efficient inference procedure based on collapsed Markov chain Monte Carlo sampling. Comparing the vMF and gaussian mixture models on synthetic data, we demonstrate that the vMF model has a slight advantage inferring the true underlying clustering when compared to gaussian-based models on data generated from both a mixture of vMFs and a mixture of gaussians subsequently normalized. Thus, when performing model selection, the two models are not in agreement. Analyzing multisubject whole brain resting-state fMRI data from healthy adult subjects, we find that the vMF mixture model is considerably more reliable than the gaussian mixture model when comparing solutions across models trained on different groups of subjects, and again we find that the two models disagree on the optimal number of components. The analysis indicates that the fMRI data support more than a thousand clusters, and we confirm this is not a result of overfitting by demonstrating better prediction on data from held-out subjects. Our results highlight the utility of using directional statistics to model standardized fMRI data and demonstrate that whole brain segmentation of fMRI data requires a very large number of functional units in order to adequately account for the discernible statistical patterns in the data.

Spatial Ensemble Postprocessing of Precipitation Forecasts Using High Resolution Analyses

NASA Astrophysics Data System (ADS)

Lang, Moritz N.; Schicker, Irene; Kann, Alexander; Wang, Yong

2017-04-01

Ensemble prediction systems are designed to account for errors or uncertainties in the initial and boundary conditions, imperfect parameterizations, etc. However, due to sampling errors and underestimation of the model errors, these ensemble forecasts tend to be underdispersive, and to lack both reliability and sharpness. To overcome such limitations, statistical postprocessing methods are commonly applied to these forecasts. In this study, a full-distributional spatial post-processing method is applied to short-range precipitation forecasts over Austria using Standardized Anomaly Model Output Statistics (SAMOS). Following Stauffer et al. (2016), observation and forecast fields are transformed into standardized anomalies by subtracting a site-specific climatological mean and dividing by the climatological standard deviation. Due to the need of fitting only a single regression model for the whole domain, the SAMOS framework provides a computationally inexpensive method to create operationally calibrated probabilistic forecasts for any arbitrary location or for all grid points in the domain simultaneously. Taking advantage of the INCA system (Integrated Nowcasting through Comprehensive Analysis), high resolution analyses are used for the computation of the observed climatology and for model training. The INCA system operationally combines station measurements and remote sensing data into real-time objective analysis fields at 1 km-horizontal resolution and 1 h-temporal resolution. The precipitation forecast used in this study is obtained from a limited area model ensemble prediction system also operated by ZAMG. The so called ALADIN-LAEF provides, by applying a multi-physics approach, a 17-member forecast at a horizontal resolution of 10.9 km and a temporal resolution of 1 hour. The performed SAMOS approach statistically combines the in-house developed high resolution analysis and ensemble prediction system. The station-based validation of 6 hour precipitation sums shows a mean improvement of more than 40% in CRPS when compared to bilinearly interpolated uncalibrated ensemble forecasts. The validation on randomly selected grid points, representing the true height distribution over Austria, still indicates a mean improvement of 35%. The applied statistical model is currently set up for 6-hourly and daily accumulation periods, but will be extended to a temporal resolution of 1-3 hours within a new probabilistic nowcasting system operated by ZAMG.

Simulation Study Using a New Type of Sample Variance

NASA Technical Reports Server (NTRS)

Howe, D. A.; Lainson, K. J.

1996-01-01

We evaluate with simulated data a new type of sample variance for the characterization of frequency stability. The new statistic (referred to as TOTALVAR and its square root TOTALDEV) is a better predictor of long-term frequency variations than the present sample Allan deviation. The statistical model uses the assumption that a time series of phase or frequency differences is wrapped (periodic) with overall frequency difference removed. We find that the variability at long averaging times is reduced considerably for the five models of power-law noise commonly encountered with frequency standards and oscillators.

The Importance of Practice in the Development of Statistics.

DTIC Science & Technology

1983-01-01

RESOLUTION TEST CHART NATIONAL BUREAU OIF STANDARDS 1963 -A NRC Technical Summary Report #2471 C THE IMORTANCE OF PRACTICE IN to THE DEVELOPMENT OF STATISTICS...component analysis, bioassay, limits for a ratio, quality control, sampling inspection, non-parametric tests , transformation theory, ARIMA time series...models, sequential tests , cumulative sum charts, data analysis plotting techniques, and a resolution of the Bayes - frequentist controversy. It appears

Cure Models as a Useful Statistical Tool for Analyzing Survival

PubMed Central

Othus, Megan; Barlogie, Bart; LeBlanc, Michael L.; Crowley, John J.

2013-01-01

Cure models are a popular topic within statistical literature but are not as widely known in the clinical literature. Many patients with cancer can be long-term survivors of their disease, and cure models can be a useful tool to analyze and describe cancer survival data. The goal of this article is to review what a cure model is, explain when cure models can be used, and use cure models to describe multiple myeloma survival trends. Multiple myeloma is generally considered an incurable disease, and this article shows that by using cure models, rather than the standard Cox proportional hazards model, we can evaluate whether there is evidence that therapies at the University of Arkansas for Medical Sciences induce a proportion of patients to be long-term survivors. PMID:22675175

Selected topics in high energy physics: Flavon, neutrino and extra-dimensional models

NASA Astrophysics Data System (ADS)

Dorsner, Ilja

There is already significant evidence, both experimental and theoretical, that the Standard Model of elementary particle physics is just another effective physical theory. Thus, it is crucial (a) to anticipate the experiments in search for signatures of the physics beyond the Standard Model, and (b) whether some theoretically preferred structure can reproduce the low-energy signature of the Standard Model. This work pursues these two directions by investigating various extensions of the Standard Model. One of them is a simple flavon model that accommodates the observed hierarchy of the charged fermion masses and mixings. We show that flavor changing and CP violating signatures of this model are equally near the present experimental limits. We find that, for a significant range of parameters, mu-e conversion can be the most sensitive place to look for such signatures. We then propose two variants of an SO(10) model in five-dimensional framework. The first variant demonstrates that one can embed a four-dimensional flipped SU(5) model into a five-dimensional SO(10) model. This allows one to maintain the advantages of flipped SU(5) while avoiding its well-known drawbacks. The second variant shows that exact unification of the gauge couplings is possible even in the higher dimensional setting. This unification yields low-energy values of the gauge couplings that are in a perfect agreement with experimental values. We show that the corrections to the usual four-dimensional running, due to the Kaluza-Klein towers of states, can be unambiguously and systematically evaluated. We also consider the various main types of models of neutrino masses and mixings from the point of view of how naturally they give the large mixing angle MSW solution to the solar neutrino problem. Special attention is given to one particular "lopsided" SU(5) model, which is then analyzed in a completely statistical manner. We suggest that this sort of statistical analysis should be applicable to other models of neutrino mixing.

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

Zheng, Xiaogang; Biesiada, Marek; Cao, Shuo

A new compilation of 012 angular-size/redshift data for compact radio quasars from very-long-baseline interferometry (VLBI) surveys motivates us to revisit the interaction between dark energy and dark matter with these probes reaching high redshifts z ∼ 3.0. In this paper, we investigate observational constraints on different phenomenological interacting dark energy (IDE) models with the intermediate-luminosity radio quasars acting as individual standard rulers, combined with the newest BAO and CMB observation from Planck results acting as statistical rulers. The results obtained from the MCMC method and other statistical methods including figure of Merit and Information Criteria show that: (1) Compared withmore » the current standard candle data and standard clock data, the intermediate-luminosity radio quasar standard rulers , probing much higher redshifts, could provide comparable constraints on different IDE scenarios. (2) The strong degeneracies between the interaction term and Hubble constant may contribute to alleviate the tension of H {sub 0} between the recent Planck and HST measurements. (3) Concerning the ranking of competing dark energy models, IDE with more free parameters are substantially penalized by the BIC criterion, which agrees very well with the previous results derived from other cosmological probes.« less

Statistical testing of association between menstruation and migraine.

PubMed

Barra, Mathias; Dahl, Fredrik A; Vetvik, Kjersti G

2015-02-01

To repair and refine a previously proposed method for statistical analysis of association between migraine and menstruation. Menstrually related migraine (MRM) affects about 20% of female migraineurs in the general population. The exact pathophysiological link from menstruation to migraine is hypothesized to be through fluctuations in female reproductive hormones, but the exact mechanisms remain unknown. Therefore, the main diagnostic criterion today is concurrency of migraine attacks with menstruation. Methods aiming to exclude spurious associations are wanted, so that further research into these mechanisms can be performed on a population with a true association. The statistical method is based on a simple two-parameter null model of MRM (which allows for simulation modeling), and Fisher's exact test (with mid-p correction) applied to standard 2 × 2 contingency tables derived from the patients' headache diaries. Our method is a corrected version of a previously published flawed framework. To our best knowledge, no other published methods for establishing a menstruation-migraine association by statistical means exist today. The probabilistic methodology shows good performance when subjected to receiver operator characteristic curve analysis. Quick reference cutoff values for the clinical setting were tabulated for assessing association given a patient's headache history. In this paper, we correct a proposed method for establishing association between menstruation and migraine by statistical methods. We conclude that the proposed standard of 3-cycle observations prior to setting an MRM diagnosis should be extended with at least one perimenstrual window to obtain sufficient information for statistical processing. © 2014 American Headache Society.

Development of Learning Models Based on Problem Solving and Meaningful Learning Standards by Expert Validity for Animal Development Course

NASA Astrophysics Data System (ADS)

Lufri, L.; Fitri, R.; Yogica, R.

2018-04-01

The purpose of this study is to produce a learning model based on problem solving and meaningful learning standards by expert assessment or validation for the course of Animal Development. This research is a development research that produce the product in the form of learning model, which consist of sub product, namely: the syntax of learning model and student worksheets. All of these products are standardized through expert validation. The research data is the level of validity of all sub products obtained using questionnaire, filled by validators from various field of expertise (field of study, learning strategy, Bahasa). Data were analysed using descriptive statistics. The result of the research shows that the problem solving and meaningful learning model has been produced. Sub products declared appropriate by expert include the syntax of learning model and student worksheet.

Statistical distribution of mechanical properties for three graphite-epoxy material systems

NASA Technical Reports Server (NTRS)

Reese, C.; Sorem, J., Jr.

1981-01-01

Graphite-epoxy composites are playing an increasing role as viable alternative materials in structural applications necessitating thorough investigation into the predictability and reproducibility of their material strength properties. This investigation was concerned with tension, compression, and short beam shear coupon testing of large samples from three different material suppliers to determine their statistical strength behavior. Statistical results indicate that a two Parameter Weibull distribution model provides better overall characterization of material behavior for the graphite-epoxy systems tested than does the standard Normal distribution model that is employed for most design work. While either a Weibull or Normal distribution model provides adequate predictions for average strength values, the Weibull model provides better characterization in the lower tail region where the predictions are of maximum design interest. The two sets of the same material were found to have essentially the same material properties, and indicate that repeatability can be achieved.

A model for indexing medical documents combining statistical and symbolic knowledge.

PubMed

Avillach, Paul; Joubert, Michel; Fieschi, Marius

2007-10-11

To develop and evaluate an information processing method based on terminologies, in order to index medical documents in any given documentary context. We designed a model using both symbolic general knowledge extracted from the Unified Medical Language System (UMLS) and statistical knowledge extracted from a domain of application. Using statistical knowledge allowed us to contextualize the general knowledge for every particular situation. For each document studied, the extracted terms are ranked to highlight the most significant ones. The model was tested on a set of 17,079 French standardized discharge summaries (SDSs). The most important ICD-10 term of each SDS was ranked 1st or 2nd by the method in nearly 90% of the cases. The use of several terminologies leads to more precise indexing. The improvement achieved in the models implementation performances as a result of using semantic relationships is encouraging.

Cost Modeling for Space Telescope

NASA Technical Reports Server (NTRS)

Stahl, H. Philip

2011-01-01

Parametric cost models are an important tool for planning missions, compare concepts and justify technology investments. This paper presents on-going efforts to develop single variable and multi-variable cost models for space telescope optical telescope assembly (OTA). These models are based on data collected from historical space telescope missions. Standard statistical methods are used to derive CERs for OTA cost versus aperture diameter and mass. The results are compared with previously published models.

Model Fit and Item Factor Analysis: Overfactoring, Underfactoring, and a Program to Guide Interpretation.

PubMed

Clark, D Angus; Bowles, Ryan P

2018-04-23

In exploratory item factor analysis (IFA), researchers may use model fit statistics and commonly invoked fit thresholds to help determine the dimensionality of an assessment. However, these indices and thresholds may mislead as they were developed in a confirmatory framework for models with continuous, not categorical, indicators. The present study used Monte Carlo simulation methods to investigate the ability of popular model fit statistics (chi-square, root mean square error of approximation, the comparative fit index, and the Tucker-Lewis index) and their standard cutoff values to detect the optimal number of latent dimensions underlying sets of dichotomous items. Models were fit to data generated from three-factor population structures that varied in factor loading magnitude, factor intercorrelation magnitude, number of indicators, and whether cross loadings or minor factors were included. The effectiveness of the thresholds varied across fit statistics, and was conditional on many features of the underlying model. Together, results suggest that conventional fit thresholds offer questionable utility in the context of IFA.

Evaluating bacterial gene-finding HMM structures as probabilistic logic programs.

PubMed

Mørk, Søren; Holmes, Ian

2012-03-01

Probabilistic logic programming offers a powerful way to describe and evaluate structured statistical models. To investigate the practicality of probabilistic logic programming for structure learning in bioinformatics, we undertook a simplified bacterial gene-finding benchmark in PRISM, a probabilistic dialect of Prolog. We evaluate Hidden Markov Model structures for bacterial protein-coding gene potential, including a simple null model structure, three structures based on existing bacterial gene finders and two novel model structures. We test standard versions as well as ADPH length modeling and three-state versions of the five model structures. The models are all represented as probabilistic logic programs and evaluated using the PRISM machine learning system in terms of statistical information criteria and gene-finding prediction accuracy, in two bacterial genomes. Neither of our implementations of the two currently most used model structures are best performing in terms of statistical information criteria or prediction performances, suggesting that better-fitting models might be achievable. The source code of all PRISM models, data and additional scripts are freely available for download at: http://github.com/somork/codonhmm. Supplementary data are available at Bioinformatics online.

Mathematical and Statistical Techniques for Systems Medicine: The Wnt Signaling Pathway as a Case Study.

PubMed

MacLean, Adam L; Harrington, Heather A; Stumpf, Michael P H; Byrne, Helen M

2016-01-01

The last decade has seen an explosion in models that describe phenomena in systems medicine. Such models are especially useful for studying signaling pathways, such as the Wnt pathway. In this chapter we use the Wnt pathway to showcase current mathematical and statistical techniques that enable modelers to gain insight into (models of) gene regulation and generate testable predictions. We introduce a range of modeling frameworks, but focus on ordinary differential equation (ODE) models since they remain the most widely used approach in systems biology and medicine and continue to offer great potential. We present methods for the analysis of a single model, comprising applications of standard dynamical systems approaches such as nondimensionalization, steady state, asymptotic and sensitivity analysis, and more recent statistical and algebraic approaches to compare models with data. We present parameter estimation and model comparison techniques, focusing on Bayesian analysis and coplanarity via algebraic geometry. Our intention is that this (non-exhaustive) review may serve as a useful starting point for the analysis of models in systems medicine.

Dynamic Uncertain Causality Graph for Knowledge Representation and Reasoning: Utilization of Statistical Data and Domain Knowledge in Complex Cases.

PubMed

Zhang, Qin; Yao, Quanying

2018-05-01

The dynamic uncertain causality graph (DUCG) is a newly presented framework for uncertain causality representation and probabilistic reasoning. It has been successfully applied to online fault diagnoses of large, complex industrial systems, and decease diagnoses. This paper extends the DUCG to model more complex cases than what could be previously modeled, e.g., the case in which statistical data are in different groups with or without overlap, and some domain knowledge and actions (new variables with uncertain causalities) are introduced. In other words, this paper proposes to use -mode, -mode, and -mode of the DUCG to model such complex cases and then transform them into either the standard -mode or the standard -mode. In the former situation, if no directed cyclic graph is involved, the transformed result is simply a Bayesian network (BN), and existing inference methods for BNs can be applied. In the latter situation, an inference method based on the DUCG is proposed. Examples are provided to illustrate the methodology.

Precipitation projections under GCMs perspective and Turkish Water Foundation (TWF) statistical downscaling model procedures

NASA Astrophysics Data System (ADS)

Dabanlı, İsmail; Şen, Zekai

2018-04-01

The statistical climate downscaling model by the Turkish Water Foundation (TWF) is further developed and applied to a set of monthly precipitation records. The model is structured by two phases as spatial (regional) and temporal downscaling of global circulation model (GCM) scenarios. The TWF model takes into consideration the regional dependence function (RDF) for spatial structure and Markov whitening process (MWP) for temporal characteristics of the records to set projections. The impact of climate change on monthly precipitations is studied by downscaling Intergovernmental Panel on Climate Change-Special Report on Emission Scenarios (IPCC-SRES) A2 and B2 emission scenarios from Max Plank Institute (EH40PYC) and Hadley Center (HadCM3). The main purposes are to explain the TWF statistical climate downscaling model procedures and to expose the validation tests, which are rewarded in same specifications as "very good" for all stations except one (Suhut) station in the Akarcay basin that is in the west central part of Turkey. Eventhough, the validation score is just a bit lower at the Suhut station, the results are "satisfactory." It is, therefore, possible to say that the TWF model has reasonably acceptable skill for highly accurate estimation regarding standard deviation ratio (SDR), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS) criteria. Based on the validated model, precipitation predictions are generated from 2011 to 2100 by using 30-year reference observation period (1981-2010). Precipitation arithmetic average and standard deviation have less than 5% error for EH40PYC and HadCM3 SRES (A2 and B2) scenarios.

A Generalized Form of Context-Dependent Psychophysiological Interactions (gPPI): A Comparison to Standard Approaches

PubMed Central

McLaren, Donald G.; Ries, Michele L.; Xu, Guofan; Johnson, Sterling C.

2012-01-01

Functional MRI (fMRI) allows one to study task-related regional responses and task-dependent connectivity analysis using psychophysiological interaction (PPI) methods. The latter affords the additional opportunity to understand how brain regions interact in a task-dependent manner. The current implementation of PPI in Statistical Parametric Mapping (SPM8) is configured primarily to assess connectivity differences between two task conditions, when in practice fMRI tasks frequently employ more than two conditions. Here we evaluate how a generalized form of context-dependent PPI (gPPI; http://www.nitrc.org/projects/gppi), which is configured to automatically accommodate more than two task conditions in the same PPI model by spanning the entire experimental space, compares to the standard implementation in SPM8. These comparisons are made using both simulations and an empirical dataset. In the simulated dataset, we compare the interaction beta estimates to their expected values and model fit using the Akaike Information Criterion (AIC). We found that interaction beta estimates in gPPI were robust to different simulated data models, were not different from the expected beta value, and had better model fits than when using standard PPI (sPPI) methods. In the empirical dataset, we compare the model fit of the gPPI approach to sPPI. We found that the gPPI approach improved model fit compared to sPPI. There were several regions that became non-significant with gPPI. These regions all showed significantly better model fits with gPPI. Also, there were several regions where task-dependent connectivity was only detected using gPPI methods, also with improved model fit. Regions that were detected with all methods had more similar model fits. These results suggest that gPPI may have greater sensitivity and specificity than standard implementation in SPM. This notion is tempered slightly as there is no gold standard; however, data simulations with a known outcome support our conclusions about gPPI. In sum, the generalized form of context-dependent PPI approach has increased flexibility of statistical modeling, and potentially improves model fit, specificity to true negative findings, and sensitivity to true positive findings. PMID:22484411

Rethinking Teacher Evaluation: A Conversation about Statistical Inferences and Value-Added Models

ERIC Educational Resources Information Center

Callister Everson, Kimberlee; Feinauer, Erika; Sudweeks, Richard R.

2013-01-01

In this article, the authors provide a methodological critique of the current standard of value-added modeling forwarded in educational policy contexts as a means of measuring teacher effectiveness. Conventional value-added estimates of teacher quality are attempts to determine to what degree a teacher would theoretically contribute, on average,…

A Comparison of Latent Growth Models for Constructs Measured by Multiple Items

ERIC Educational Resources Information Center

Leite, Walter L.

2007-01-01

Univariate latent growth modeling (LGM) of composites of multiple items (e.g., item means or sums) has been frequently used to analyze the growth of latent constructs. This study evaluated whether LGM of composites yields unbiased parameter estimates, standard errors, chi-square statistics, and adequate fit indexes. Furthermore, LGM was compared…

Statistical validation and an empirical model of hydrogen production enhancement found by utilizing passive flow disturbance in the steam-reformation process

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

Erickson, Paul A.; Liao, Chang-hsien

2007-11-15

A passive flow disturbance has been proven to enhance the conversion of fuel in a methanol-steam reformer. This study presents a statistical validation of the experiment based on a standard 2{sup k} factorial experiment design and the resulting empirical model of the enhanced hydrogen producing process. A factorial experiment design was used to statistically analyze the effects and interactions of various input factors in the experiment. Three input factors, including the number of flow disturbers, catalyst size, and reactant flow rate were investigated for their effects on the fuel conversion in the steam-reformation process. Based on the experimental results, anmore » empirical model was developed and further evaluated with an uncertainty analysis and interior point data. (author)« less

Communication Dynamics of Blog Networks

NASA Astrophysics Data System (ADS)

Goldberg, Mark; Kelley, Stephen; Magdon-Ismail, Malik; Mertsalov, Konstantin; Wallace, William (Al)

We study the communication dynamics of Blog networks, focusing on the Russian section of LiveJournal as a case study. Communication (blogger-to-blogger links) in such online communication networks is very dynamic: over 60% of the links in the network are new from one week to the next, though the set of bloggers remains approximately constant. Two fundamental questions are: (i) what models adequately describe such dynamic communication behavior; and (ii) how does one detect the phase transitions, i.e. the changes that go beyond the standard high-level dynamics? We approach these questions through the notion of stable statistics. We give strong experimental evidence to the fact that, despite the extreme amount of communication dynamics, several aggregate statistics are remarkably stable. We use stable statistics to test our models of communication dynamics postulating that any good model should produce values for these statistics which are both stable and close to the observed ones. Stable statistics can also be used to identify phase transitions, since any change in a normally stable statistic indicates a substantial change in the nature of the communication dynamics. We describe models of the communication dynamics in large social networks based on the principle of locality of communication: a node's communication energy is spent mostly within its own "social area," the locality of the node.

Standard Clock in primordial density perturbations and cosmic microwave background

NASA Astrophysics Data System (ADS)

Chen, Xingang; Namjoo, Mohammad Hossein

2014-12-01

Standard Clocks in the primordial epoch leave a special type of features in the primordial perturbations, which can be used to directly measure the scale factor of the primordial universe as a function of time a (t), thus discriminating between inflation and alternatives. We have started to search for such signals in the Planck 2013 data using the key predictions of the Standard Clock. In this Letter, we summarize the key predictions of the Standard Clock and present an interesting candidate example in Planck 2013 data. Motivated by this candidate, we construct and compute full Standard Clock models and use the more complete prediction to make more extensive comparison with data. Although this candidate is not yet statistically significant, we use it to illustrate how Standard Clocks appear in Cosmic Microwave Background (CMB) and how they can be further tested by future data. We also use it to motivate more detailed theoretical model building.

Precision electroweak physics at LEP

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

Mannelli, M.

1994-12-01

Copious event statistics, a precise understanding of the LEP energy scale, and a favorable experimental situation at the Z{sup 0} resonance have allowed the LEP experiments to provide both dramatic confirmation of the Standard Model of strong and electroweak interactions and to place substantially improved constraints on the parameters of the model. The author concentrates on those measurements relevant to the electroweak sector. It will be seen that the precision of these measurements probes sensitively the structure of the Standard Model at the one-loop level, where the calculation of the observables measured at LEP is affected by the value chosenmore » for the top quark mass. One finds that the LEP measurements are consistent with the Standard Model, but only if the mass of the top quark is measured to be within a restricted range of about 20 GeV.« less

About the Environmental Public Health Division (EPHD) of EPA's National Health and Environmental Effects Research Laboratory

EPA Pesticide Factsheets

The EPHD performs integrated epidemiological, clinical, animal and cellular biological research and statistical modeling to provide the scientific foundation in support of hazard identification, risk assessment, and standard setting.

It's Only a Phase: Applying the 5 Phases of Clinical Trials to the NSCR Model Improvement Process

NASA Technical Reports Server (NTRS)

Elgart, S. R.; Milder, C. M.; Chappell, L. J.; Semones, E. J.

2017-01-01

NASA limits astronaut radiation exposures to a 3% risk of exposure-induced death from cancer (REID) at the upper 95% confidence level. Since astronauts approach this limit, it is important that the estimate of REID be as accurate as possible. The NASA Space Cancer Risk 2012 (NSCR-2012) model has been the standard for NASA's space radiation protection guidelines since its publication in 2013. The model incorporates elements from U.S. baseline statistics, Japanese atomic bomb survivor research, animal models, cellular studies, and radiation transport to calculate astronaut baseline risk of cancer and REID. The NSCR model is under constant revision to ensure emerging research is incorporated into radiation protection standards. It is important to develop guidelines, however, to determine what new research is appropriate for integration. Certain standards of transparency are necessary in order to assess data quality, statistical quality, and analytical quality. To this effect, all original source code and any raw data used to develop the code are required to confirm there are no errors which significantly change reported outcomes. It is possible to apply a clinical trials approach to select and assess the improvement concepts that will be incorporated into future iterations of NSCR. This poster describes the five phases of clinical trials research, pre-clinical research, and clinical research phases I-IV, explaining how each step can be translated into an appropriate NSCR model selection guideline.

A comparison of data-driven groundwater vulnerability assessment methods

USGS Publications Warehouse

Sorichetta, Alessandro; Ballabio, Cristiano; Masetti, Marco; Robinson, Gilpin R.; Sterlacchini, Simone

2013-01-01

Increasing availability of geo-environmental data has promoted the use of statistical methods to assess groundwater vulnerability. Nitrate is a widespread anthropogenic contaminant in groundwater and its occurrence can be used to identify aquifer settings vulnerable to contamination. In this study, multivariate Weights of Evidence (WofE) and Logistic Regression (LR) methods, where the response variable is binary, were used to evaluate the role and importance of a number of explanatory variables associated with nitrate sources and occurrence in groundwater in the Milan District (central part of the Po Plain, Italy). The results of these models have been used to map the spatial variation of groundwater vulnerability to nitrate in the region, and we compare the similarities and differences of their spatial patterns and associated explanatory variables. We modify the standard WofE method used in previous groundwater vulnerability studies to a form analogous to that used in LR; this provides a framework to compare the results of both models and reduces the effect of sampling bias on the results of the standard WofE model. In addition, a nonlinear Generalized Additive Model has been used to extend the LR analysis. Both approaches improved discrimination of the standard WofE and LR models, as measured by the c-statistic. Groundwater vulnerability probability outputs, based on rank-order classification of the respective model results, were similar in spatial patterns and identified similar strong explanatory variables associated with nitrate source (population density as a proxy for sewage systems and septic sources) and nitrate occurrence (groundwater depth).

Physics of Electronic Materials

NASA Astrophysics Data System (ADS)

Rammer, Jørgen

2017-03-01

1. Quantum mechanics; 2. Quantum tunneling; 3. Standard metal model; 4. Standard conductor model; 5. Electric circuit theory; 6. Quantum wells; 7. Particle in a periodic potential; 8. Bloch currents; 9. Crystalline solids; 10. Semiconductor doping; 11. Transistors; 12. Heterostructures; 13. Mesoscopic physics; 14. Arithmetic, logic and machines; Appendix A. Principles of quantum mechanics; Appendix B. Dirac's delta function; Appendix C. Fourier analysis; Appendix D. Classical mechanics; Appendix E. Wave function properties; Appendix F. Transfer matrix properties; Appendix G. Momentum; Appendix H. Confined particles; Appendix I. Spin and quantum statistics; Appendix J. Statistical mechanics; Appendix K. The Fermi-Dirac distribution; Appendix L. Thermal current fluctuations; Appendix M. Gaussian wave packets; Appendix N. Wave packet dynamics; Appendix O. Screening by symmetry method; Appendix P. Commutation and common eigenfunctions; Appendix Q. Interband coupling; Appendix R. Common crystal structures; Appendix S. Effective mass approximation; Appendix T. Integral doubling formula; Bibliography; Index.

Detailed Spectral Analysis of the 260 ks XMM-Newton Data of 1E 1207.4-5209 and Significance of a 2.1 keV Absorption Feature

NASA Astrophysics Data System (ADS)

Mori, Kaya; Chonko, James C.; Hailey, Charles J.

2005-10-01

We have reanalyzed the 260 ks XMM-Newton observation of 1E 1207.4-5209. There are several significant improvements over previous work. First, a much broader range of physically plausible spectral models was used. Second, we have used a more rigorous statistical analysis. The standard F-distribution was not employed, but rather the exact finite statistics F-distribution was determined by Monte Carlo simulations. This approach was motivated by the recent work of Protassov and coworkers and Freeman and coworkers. They demonstrated that the standard F-distribution is not even asymptotically correct when applied to assess the significance of additional absorption features in a spectrum. With our improved analysis we do not find a third and fourth spectral feature in 1E 1207.4-5209 but only the two broad absorption features previously reported. Two additional statistical tests, one line model dependent and the other line model independent, confirmed our modified F-test analysis. For all physically plausible continuum models in which the weak residuals are strong enough to fit, the residuals occur at the instrument Au M edge. As a sanity check we confirmed that the residuals are consistent in strength and position with the instrument Au M residuals observed in 3C 273.

Highway runoff quality models for the protection of environmentally sensitive areas

NASA Astrophysics Data System (ADS)

Trenouth, William R.; Gharabaghi, Bahram

2016-11-01

This paper presents novel highway runoff quality models using artificial neural networks (ANN) which take into account site-specific highway traffic and seasonal storm event meteorological factors to predict the event mean concentration (EMC) statistics and mean daily unit area load (MDUAL) statistics of common highway pollutants for the design of roadside ditch treatment systems (RDTS) to protect sensitive receiving environs. A dataset of 940 monitored highway runoff events from fourteen sites located in five countries (Canada, USA, Australia, New Zealand, and China) was compiled and used to develop ANN models for the prediction of highway runoff suspended solids (TSS) seasonal EMC statistical distribution parameters, as well as the MDUAL statistics for four different heavy metal species (Cu, Zn, Cr and Pb). TSS EMCs are needed to estimate the minimum required removal efficiency of the RDTS needed in order to improve highway runoff quality to meet applicable standards and MDUALs are needed to calculate the minimum required capacity of the RDTS to ensure performance longevity.

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning.

PubMed

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-06-17

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

PubMed Central

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-01-01

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults. PMID:27322273

Discrete disorder models for many-body localization

NASA Astrophysics Data System (ADS)

Janarek, Jakub; Delande, Dominique; Zakrzewski, Jakub

2018-04-01

Using exact diagonalization technique, we investigate the many-body localization phenomenon in the 1D Heisenberg chain comparing several disorder models. In particular we consider a family of discrete distributions of disorder strengths and compare the results with the standard uniform distribution. Both statistical properties of energy levels and the long time nonergodic behavior are discussed. The results for different discrete distributions are essentially identical to those obtained for the continuous distribution, provided the disorder strength is rescaled by the standard deviation of the random distribution. Only for the binary distribution significant deviations are observed.

Statistical inference for template aging

NASA Astrophysics Data System (ADS)

Schuckers, Michael E.

2006-04-01

A change in classification error rates for a biometric device is often referred to as template aging. Here we offer two methods for determining whether the effect of time is statistically significant. The first of these is the use of a generalized linear model to determine if these error rates change linearly over time. This approach generalizes previous work assessing the impact of covariates using generalized linear models. The second approach uses of likelihood ratio tests methodology. The focus here is on statistical methods for estimation not the underlying cause of the change in error rates over time. These methodologies are applied to data from the National Institutes of Standards and Technology Biometric Score Set Release 1. The results of these applications are discussed.

Quarks, Symmetries and Strings - a Symposium in Honor of Bunji Sakita's 60th Birthday

NASA Astrophysics Data System (ADS)

Kaku, M.; Jevicki, A.; Kikkawa, K.

1991-04-01

The Table of Contents for the full book PDF is as follows: * Preface * Evening Banquet Speech * I. Quarks and Phenomenology * From the SU(6) Model to Uniqueness in the Standard Model * A Model for Higgs Mechanism in the Standard Model * Quark Mass Generation in QCD * Neutrino Masses in the Standard Model * Solar Neutrino Puzzle, Horizontal Symmetry of Electroweak Interactions and Fermion Mass Hierarchies * State of Chiral Symmetry Breaking at High Temperatures * Approximate |ΔI| = 1/2 Rule from a Perspective of Light-Cone Frame Physics * Positronium (and Some Other Systems) in a Strong Magnetic Field * Bosonic Technicolor and the Flavor Problem * II. Strings * Supersymmetry in String Theory * Collective Field Theory and Schwinger-Dyson Equations in Matrix Models * Non-Perturbative String Theory * The Structure of Non-Perturbative Quantum Gravity in One and Two Dimensions * Noncritical Virasoro Algebra of d < 1 Matrix Model and Quantized String Field * Chaos in Matrix Models ? * On the Non-Commutative Symmetry of Quantum Gravity in Two Dimensions * Matrix Model Formulation of String Field Theory in One Dimension * Geometry of the N = 2 String Theory * Modular Invariance form Gauge Invariance in the Non-Polynomial String Field Theory * Stringy Symmetry and Off-Shell Ward Identities * q-Virasoro Algebra and q-Strings * Self-Tuning Fields and Resonant Correlations in 2d-Gravity * III. Field Theory Methods * Linear Momentum and Angular Momentum in Quaternionic Quantum Mechanics * Some Comments on Real Clifford Algebras * On the Quantum Group p-adics Connection * Gravitational Instantons Revisited * A Generalized BBGKY Hierarchy from the Classical Path-Integral * A Quantum Generated Symmetry: Group-Level Duality in Conformal and Topological Field Theory * Gauge Symmetries in Extended Objects * Hidden BRST Symmetry and Collective Coordinates * Towards Stochastically Quantizing Topological Actions * IV. Statistical Methods * A Brief Summary of the s-Channel Theory of Superconductivity * Neural Networks and Models for the Brain * Relativistic One-Body Equations for Planar Particles with Arbitrary Spin * Chiral Property of Quarks and Hadron Spectrum in Lattice QCD * Scalar Lattice QCD * Semi-Superconductivity of a Charged Anyon Gas * Two-Fermion Theory of Strongly Correlated Electrons and Charge-Spin Separation * Statistical Mechanics and Error-Correcting Codes * Quantum Statistics

Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids

PubMed Central

Ali, S. M.; Mehmood, C. A; Khan, B.; Jawad, M.; Farid, U; Jadoon, J. K.; Ali, M.; Tareen, N. K.; Usman, S.; Majid, M.; Anwar, S. M.

2016-01-01

In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF) probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC) simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand) and independent variables (weather data) for utility load management, generation control, and network expansion. PMID:27314229

Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids.

PubMed

Ali, S M; Mehmood, C A; Khan, B; Jawad, M; Farid, U; Jadoon, J K; Ali, M; Tareen, N K; Usman, S; Majid, M; Anwar, S M

2016-01-01

In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF) probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC) simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand) and independent variables (weather data) for utility load management, generation control, and network expansion.

Uncertainty Analysis of Inertial Model Attitude Sensor Calibration and Application with a Recommended New Calibration Method

NASA Technical Reports Server (NTRS)

Tripp, John S.; Tcheng, Ping

1999-01-01

Statistical tools, previously developed for nonlinear least-squares estimation of multivariate sensor calibration parameters and the associated calibration uncertainty analysis, have been applied to single- and multiple-axis inertial model attitude sensors used in wind tunnel testing to measure angle of attack and roll angle. The analysis provides confidence and prediction intervals of calibrated sensor measurement uncertainty as functions of applied input pitch and roll angles. A comparative performance study of various experimental designs for inertial sensor calibration is presented along with corroborating experimental data. The importance of replicated calibrations over extended time periods has been emphasized; replication provides independent estimates of calibration precision and bias uncertainties, statistical tests for calibration or modeling bias uncertainty, and statistical tests for sensor parameter drift over time. A set of recommendations for a new standardized model attitude sensor calibration method and usage procedures is included. The statistical information provided by these procedures is necessary for the uncertainty analysis of aerospace test results now required by users of industrial wind tunnel test facilities.

Score tests for independence in semiparametric competing risks models.

PubMed

Saïd, Mériem; Ghazzali, Nadia; Rivest, Louis-Paul

2009-12-01

A popular model for competing risks postulates the existence of a latent unobserved failure time for each risk. Assuming that these underlying failure times are independent is attractive since it allows standard statistical tools for right-censored lifetime data to be used in the analysis. This paper proposes simple independence score tests for the validity of this assumption when the individual risks are modeled using semiparametric proportional hazards regressions. It assumes that covariates are available, making the model identifiable. The score tests are derived for alternatives that specify that copulas are responsible for a possible dependency between the competing risks. The test statistics are constructed by adding to the partial likelihoods for the individual risks an explanatory variable for the dependency between the risks. A variance estimator is derived by writing the score function and the Fisher information matrix for the marginal models as stochastic integrals. Pitman efficiencies are used to compare test statistics. A simulation study and a numerical example illustrate the methodology proposed in this paper.

Twice random, once mixed: applying mixed models to simultaneously analyze random effects of language and participants.

PubMed

Janssen, Dirk P

2012-03-01

Psychologists, psycholinguists, and other researchers using language stimuli have been struggling for more than 30 years with the problem of how to analyze experimental data that contain two crossed random effects (items and participants). The classical analysis of variance does not apply; alternatives have been proposed but have failed to catch on, and a statistically unsatisfactory procedure of using two approximations (known as F(1) and F(2)) has become the standard. A simple and elegant solution using mixed model analysis has been available for 15 years, and recent improvements in statistical software have made mixed models analysis widely available. The aim of this article is to increase the use of mixed models by giving a concise practical introduction and by giving clear directions for undertaking the analysis in the most popular statistical packages. The article also introduces the DJMIXED: add-on package for SPSS, which makes entering the models and reporting their results as straightforward as possible.

Efforts to improve international migration statistics: a historical perspective.

PubMed

Kraly, E P; Gnanasekaran, K S

1987-01-01

During the past decade, the international statistical community has made several efforts to develop standards for the definition, collection and publication of statistics on international migration. This article surveys the history of official initiatives to standardize international migration statistics by reviewing the recommendations of the International Statistical Institute, International Labor Organization, and the UN, and reports a recently proposed agenda for moving toward comparability among national statistical systems. Heightening awareness of the benefits of exchange and creating motivation to implement international standards requires a 3-pronged effort from the international statistical community. 1st, it is essential to continue discussion about the significance of improvement, specifically standardization, of international migration statistics. The move from theory to practice in this area requires ongoing focus by migration statisticians so that conformity to international standards itself becomes a criterion by which national statistical practices are examined and assessed. 2nd, the countries should be provided with technical documentation to support and facilitate the implementation of the recommended statistical systems. Documentation should be developed with an understanding that conformity to international standards for migration and travel statistics must be achieved within existing national statistical programs. 3rd, the call for statistical research in this area requires more efforts by the community of migration statisticians, beginning with the mobilization of bilateral and multilateral resources to undertake the preceding list of activities.

75 FR 37245 - 2010 Standards for Delineating Metropolitan and Micropolitan Statistical Areas

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-28

... Micropolitan Statistical Areas; Notice #0;#0;Federal Register / Vol. 75, No. 123 / Monday, June 28, 2010... and Micropolitan Statistical Areas AGENCY: Office of Information and Regulatory Affairs, Office of... Statistical Areas. The 2010 standards replace and supersede the 2000 Standards for Defining Metropolitan and...

Explorations in statistics: the log transformation.

PubMed

Curran-Everett, Douglas

2018-06-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This thirteenth installment of Explorations in Statistics explores the log transformation, an established technique that rescales the actual observations from an experiment so that the assumptions of some statistical analysis are better met. A general assumption in statistics is that the variability of some response Y is homogeneous across groups or across some predictor variable X. If the variability-the standard deviation-varies in rough proportion to the mean value of Y, a log transformation can equalize the standard deviations. Moreover, if the actual observations from an experiment conform to a skewed distribution, then a log transformation can make the theoretical distribution of the sample mean more consistent with a normal distribution. This is important: the results of a one-sample t test are meaningful only if the theoretical distribution of the sample mean is roughly normal. If we log-transform our observations, then we want to confirm the transformation was useful. We can do this if we use the Box-Cox method, if we bootstrap the sample mean and the statistic t itself, and if we assess the residual plots from the statistical model of the actual and transformed sample observations.

Strengthen forensic entomology in court--the need for data exploration and the validation of a generalised additive mixed model.

PubMed

Baqué, Michèle; Amendt, Jens

2013-01-01

Developmental data of juvenile blow flies (Diptera: Calliphoridae) are typically used to calculate the age of immature stages found on or around a corpse and thus to estimate a minimum post-mortem interval (PMI(min)). However, many of those data sets don't take into account that immature blow flies grow in a non-linear fashion. Linear models do not supply a sufficient reliability on age estimates and may even lead to an erroneous determination of the PMI(min). According to the Daubert standard and the need for improvements in forensic science, new statistic tools like smoothing methods and mixed models allow the modelling of non-linear relationships and expand the field of statistical analyses. The present study introduces into the background and application of these statistical techniques by analysing a model which describes the development of the forensically important blow fly Calliphora vicina at different temperatures. The comparison of three statistical methods (linear regression, generalised additive modelling and generalised additive mixed modelling) clearly demonstrates that only the latter provided regression parameters that reflect the data adequately. We focus explicitly on both the exploration of the data--to assure their quality and to show the importance of checking it carefully prior to conducting the statistical tests--and the validation of the resulting models. Hence, we present a common method for evaluating and testing forensic entomological data sets by using for the first time generalised additive mixed models.

Performance of digital RGB reflectance color extraction for plaque lesion

NASA Astrophysics Data System (ADS)

Hashim, Hadzli; Taib, Mohd Nasir; Jailani, Rozita; Sulaiman, Saadiah; Baba, Roshidah

2005-01-01

Several clinical psoriasis lesion groups are been studied for digital RGB color features extraction. Previous works have used samples size that included all the outliers lying beyond the standard deviation factors from the peak histograms. This paper described the statistical performances of the RGB model with and without removing these outliers. Plaque lesion is experimented with other types of psoriasis. The statistical tests are compared with respect to three samples size; the original 90 samples, the first size reduction by removing outliers from 2 standard deviation distances (2SD) and the second size reduction by removing outliers from 1 standard deviation distance (1SD). Quantification of data images through the normal/direct and differential of the conventional reflectance method is considered. Results performances are concluded by observing the error plots with 95% confidence interval and findings of the inference T-tests applied. The statistical tests outcomes have shown that B component for conventional differential method can be used to distinctively classify plaque from the other psoriasis groups in consistent with the error plots finding with an improvement in p-value greater than 0.5.

Micro-foundations for macroeconomics: New set-up based on statistical physics

NASA Astrophysics Data System (ADS)

Yoshikawa, Hiroshi

2016-12-01

Modern macroeconomics is built on "micro foundations." Namely, optimization of micro agent such as consumer and firm is explicitly analyzed in model. Toward this goal, standard model presumes "the representative" consumer/firm, and analyzes its behavior in detail. However, the macroeconomy consists of 107 consumers and 106 firms. For the purpose of analyzing such macro system, it is meaningless to pursue the micro behavior in detail. In this respect, there is no essential difference between economics and physics. The method of statistical physics can be usefully applied to the macroeconomy, and provides Keynesian economics with correct micro-foundations.

Statistical Evaluation of Time Series Analysis Techniques

NASA Technical Reports Server (NTRS)

Benignus, V. A.

1973-01-01

The performance of a modified version of NASA's multivariate spectrum analysis program is discussed. A multiple regression model was used to make the revisions. Performance improvements were documented and compared to the standard fast Fourier transform by Monte Carlo techniques.

Velocity bias in the distribution of dark matter halos

NASA Astrophysics Data System (ADS)

Baldauf, Tobias; Desjacques, Vincent; Seljak, Uroš

2015-12-01

The standard formalism for the coevolution of halos and dark matter predicts that any initial halo velocity bias rapidly decays to zero. We argue that, when the purpose is to compute statistics like power spectra etc., the coupling in the momentum conservation equation for the biased tracers must be modified. Our new formulation predicts the constancy in time of any statistical halo velocity bias present in the initial conditions, in agreement with peak theory. We test this prediction by studying the evolution of a conserved halo population in N -body simulations. We establish that the initial simulated halo density and velocity statistics show distinct features of the peak model and, thus, deviate from the simple local Lagrangian bias. We demonstrate, for the first time, that the time evolution of their velocity is in tension with the rapid decay expected in the standard approach.

A review of odour impact criteria in selected countries around the world.

PubMed

Brancher, Marlon; Griffiths, K David; Franco, Davide; de Melo Lisboa, Henrique

2017-02-01

Exposure to environmental odour can result in annoyance, health effects and depreciation of property values. Therefore, many jurisdictions classify odour as an atmospheric pollutant and regulate emissions and/or impacts from odour generating activities at a national, state or municipal level. In this work, a critical review of odour regulations in selected jurisdictions of 28 countries is presented. Individual approaches were identified as: comparing ambient air odour concentration and individual chemicals statistics against impact criteria (maximum impact standard); using fixed and variable separation distances (separation distance standard); maximum emission rate for mixtures of odorants and individual chemical species (maximum emission standard); number of complaints received or annoyance level determined via community surveys (maximum annoyance standard); and requiring use of best available technologies (BAT) to minimize odour emissions (technology standard). The comparison of model-predicted odour concentration statistics against odour impact criteria (OIC) is identified as one of the most common tools used by regulators to evaluate the risk of odour impacts in planning stage assessments and is also used to inform assessment of odour impacts of existing facilities. Special emphasis is given to summarizing OIC (concentration percentile and threshold) and the manner in which they are applied. The way short term odour peak to model time-step mean (peak-to-mean) effects is also captured. Furthermore, the fundamentals of odorant properties, dimensions of nuisance odour, odour sampling and analysis methods and dispersion modelling guidance are provided. Common elements of mature and effective odour regulation frameworks are identified and an integrated multi-tool strategy is recommended. Copyright © 2016 Elsevier Ltd. All rights reserved.

Comparing colon cancer outcomes: The impact of low hospital case volume and case-mix adjustment.

PubMed

Fischer, C; Lingsma, H F; van Leersum, N; Tollenaar, R A E M; Wouters, M W; Steyerberg, E W

2015-08-01

When comparing performance across hospitals it is essential to consider the noise caused by low hospital case volume and to perform adequate case-mix adjustment. We aimed to quantify the role of noise and case-mix adjustment on standardized postoperative mortality and anastomotic leakage (AL) rates. We studied 13,120 patients who underwent colon cancer resection in 85 Dutch hospitals. We addressed differences between hospitals in postoperative mortality and AL, using fixed (ignoring noise) and random effects (incorporating noise) logistic regression models with general and additional, disease specific, case-mix adjustment. Adding disease specific variables improved the performance of the case-mix adjustment models for postoperative mortality (c-statistic increased from 0.77 to 0.81). The overall variation in standardized mortality ratios was similar, but some individual hospitals changed considerably. For the standardized AL rates the performance of the adjustment models was poor (c-statistic 0.59 and 0.60) and overall variation was small. Most of the observed variation between hospitals was actually noise. Noise had a larger effect on hospital performance than extended case-mix adjustment, although some individual hospital outcome rates were affected by more detailed case-mix adjustment. To compare outcomes between hospitals it is crucial to consider noise due to low hospital case volume with a random effects model. Copyright © 2015 Elsevier Ltd. All rights reserved.

Fully Bayesian tests of neutrality using genealogical summary statistics.

PubMed

Drummond, Alexei J; Suchard, Marc A

2008-10-31

Many data summary statistics have been developed to detect departures from neutral expectations of evolutionary models. However questions about the neutrality of the evolution of genetic loci within natural populations remain difficult to assess. One critical cause of this difficulty is that most methods for testing neutrality make simplifying assumptions simultaneously about the mutational model and the population size model. Consequentially, rejecting the null hypothesis of neutrality under these methods could result from violations of either or both assumptions, making interpretation troublesome. Here we harness posterior predictive simulation to exploit summary statistics of both the data and model parameters to test the goodness-of-fit of standard models of evolution. We apply the method to test the selective neutrality of molecular evolution in non-recombining gene genealogies and we demonstrate the utility of our method on four real data sets, identifying significant departures of neutrality in human influenza A virus, even after controlling for variation in population size. Importantly, by employing a full model-based Bayesian analysis, our method separates the effects of demography from the effects of selection. The method also allows multiple summary statistics to be used in concert, thus potentially increasing sensitivity. Furthermore, our method remains useful in situations where analytical expectations and variances of summary statistics are not available. This aspect has great potential for the analysis of temporally spaced data, an expanding area previously ignored for limited availability of theory and methods.

Statistical mechanics of soft-boson phase transitions

NASA Technical Reports Server (NTRS)

Gupta, Arun K.; Hill, Christopher T.; Holman, Richard; Kolb, Edward W.

1991-01-01

The existence of structure on large (100 Mpc) scales, and limits to anisotropies in the cosmic microwave background radiation (CMBR), have imperiled models of structure formation based solely upon the standard cold dark matter scenario. Novel scenarios, which may be compatible with large scale structure and small CMBR anisotropies, invoke nonlinear fluctuations in the density appearing after recombination, accomplished via the use of late time phase transitions involving ultralow mass scalar bosons. Herein, the statistical mechanics are studied of such phase transitions in several models involving naturally ultralow mass pseudo-Nambu-Goldstone bosons (pNGB's). These models can exhibit several interesting effects at high temperature, which is believed to be the most general possibilities for pNGB's.

MTS dye based colorimetric CTLL-2 cell proliferation assay for product release and stability monitoring of interleukin-15: assay qualification, standardization and statistical analysis.

PubMed

Soman, Gopalan; Yang, Xiaoyi; Jiang, Hengguang; Giardina, Steve; Vyas, Vinay; Mitra, George; Yovandich, Jason; Creekmore, Stephen P; Waldmann, Thomas A; Quiñones, Octavio; Alvord, W Gregory

2009-08-31

A colorimetric cell proliferation assay using soluble tetrazolium salt [(CellTiter 96(R) Aqueous One Solution) cell proliferation reagent, containing the (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium, inner salt) and an electron coupling reagent phenazine ethosulfate], was optimized and qualified for quantitative determination of IL-15 dependent CTLL-2 cell proliferation activity. An in-house recombinant Human (rHu)IL-15 reference lot was standardized (IU/mg) against an international reference standard. Specificity of the assay for IL-15 was documented by illustrating the ability of neutralizing anti-IL-15 antibodies to block the product specific CTLL-2 cell proliferation and the lack of blocking effect with anti-IL-2 antibodies. Under the defined assay conditions, the linear dose-response concentration range was between 0.04 and 0.17ng/ml of the rHuIL-15 produced in-house and 0.5-3.0IU/ml for the international standard. Statistical analysis of the data was performed with the use of scripts written in the R Statistical Language and Environment utilizing a four-parameter logistic regression fit analysis procedure. The overall variation in the ED(50) values for the in-house reference standard from 55 independent estimates performed over the period of 1year was 12.3% of the average. Excellent intra-plate and within-day/inter-plate consistency was observed for all four parameter estimates in the model. Different preparations of rHuIL-15 showed excellent intra-plate consistency in the parameter estimates corresponding to the lower and upper asymptotes as well as to the 'slope' factor at the mid-point. The ED(50) values showed statistically significant differences for different lots and for control versus stressed samples. Three R-scripts improve data analysis capabilities allowing one to describe assay variations, to draw inferences between data sets from formal statistical tests, and to set up improved assay acceptance criteria based on comparability and consistency in the four parameters of the model. The assay is precise, accurate and robust and can be fully validated. Applications of the assay were established including process development support, release of the rHuIL-15 product for pre-clinical and clinical studies, and for monitoring storage stability.

Evaluating statistical consistency in the ocean model component of the Community Earth System Model (pyCECT v2.0)

NASA Astrophysics Data System (ADS)

Baker, Allison H.; Hu, Yong; Hammerling, Dorit M.; Tseng, Yu-heng; Xu, Haiying; Huang, Xiaomeng; Bryan, Frank O.; Yang, Guangwen

2016-07-01

The Parallel Ocean Program (POP), the ocean model component of the Community Earth System Model (CESM), is widely used in climate research. Most current work in CESM-POP focuses on improving the model's efficiency or accuracy, such as improving numerical methods, advancing parameterization, porting to new architectures, or increasing parallelism. Since ocean dynamics are chaotic in nature, achieving bit-for-bit (BFB) identical results in ocean solutions cannot be guaranteed for even tiny code modifications, and determining whether modifications are admissible (i.e., statistically consistent with the original results) is non-trivial. In recent work, an ensemble-based statistical approach was shown to work well for software verification (i.e., quality assurance) on atmospheric model data. The general idea of the ensemble-based statistical consistency testing is to use a qualitative measurement of the variability of the ensemble of simulations as a metric with which to compare future simulations and make a determination of statistical distinguishability. The capability to determine consistency without BFB results boosts model confidence and provides the flexibility needed, for example, for more aggressive code optimizations and the use of heterogeneous execution environments. Since ocean and atmosphere models have differing characteristics in term of dynamics, spatial variability, and timescales, we present a new statistical method to evaluate ocean model simulation data that requires the evaluation of ensemble means and deviations in a spatial manner. In particular, the statistical distribution from an ensemble of CESM-POP simulations is used to determine the standard score of any new model solution at each grid point. Then the percentage of points that have scores greater than a specified threshold indicates whether the new model simulation is statistically distinguishable from the ensemble simulations. Both ensemble size and composition are important. Our experiments indicate that the new POP ensemble consistency test (POP-ECT) tool is capable of distinguishing cases that should be statistically consistent with the ensemble and those that should not, as well as providing a simple, subjective and systematic way to detect errors in CESM-POP due to the hardware or software stack, positively contributing to quality assurance for the CESM-POP code.

Mixed Model Association with Family-Biased Case-Control Ascertainment.

PubMed

Hayeck, Tristan J; Loh, Po-Ru; Pollack, Samuela; Gusev, Alexander; Patterson, Nick; Zaitlen, Noah A; Price, Alkes L

2017-01-05

Mixed models have become the tool of choice for genetic association studies; however, standard mixed model methods may be poorly calibrated or underpowered under family sampling bias and/or case-control ascertainment. Previously, we introduced a liability threshold-based mixed model association statistic (LTMLM) to address case-control ascertainment in unrelated samples. Here, we consider family-biased case-control ascertainment, where case and control subjects are ascertained non-randomly with respect to family relatedness. Previous work has shown that this type of ascertainment can severely bias heritability estimates; we show here that it also impacts mixed model association statistics. We introduce a family-based association statistic (LT-Fam) that is robust to this problem. Similar to LTMLM, LT-Fam is computed from posterior mean liabilities (PML) under a liability threshold model; however, LT-Fam uses published narrow-sense heritability estimates to avoid the problem of biased heritability estimation, enabling correct calibration. In simulations with family-biased case-control ascertainment, LT-Fam was correctly calibrated (average χ 2 = 1.00-1.02 for null SNPs), whereas the Armitage trend test (ATT), standard mixed model association (MLM), and case-control retrospective association test (CARAT) were mis-calibrated (e.g., average χ 2 = 0.50-1.22 for MLM, 0.89-2.65 for CARAT). LT-Fam also attained higher power than other methods in some settings. In 1,259 type 2 diabetes-affected case subjects and 5,765 control subjects from the CARe cohort, downsampled to induce family-biased ascertainment, LT-Fam was correctly calibrated whereas ATT, MLM, and CARAT were again mis-calibrated. Our results highlight the importance of modeling family sampling bias in case-control datasets with related samples. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

A Model for Indexing Medical Documents Combining Statistical and Symbolic Knowledge.

PubMed Central

Avillach, Paul; Joubert, Michel; Fieschi, Marius

2007-01-01

OBJECTIVES: To develop and evaluate an information processing method based on terminologies, in order to index medical documents in any given documentary context. METHODS: We designed a model using both symbolic general knowledge extracted from the Unified Medical Language System (UMLS) and statistical knowledge extracted from a domain of application. Using statistical knowledge allowed us to contextualize the general knowledge for every particular situation. For each document studied, the extracted terms are ranked to highlight the most significant ones. The model was tested on a set of 17,079 French standardized discharge summaries (SDSs). RESULTS: The most important ICD-10 term of each SDS was ranked 1st or 2nd by the method in nearly 90% of the cases. CONCLUSIONS: The use of several terminologies leads to more precise indexing. The improvement achieved in the model’s implementation performances as a result of using semantic relationships is encouraging. PMID:18693792

Parametric Study of Urban-Like Topographic Statistical Moments Relevant to a Priori Modelling of Bulk Aerodynamic Parameters

NASA Astrophysics Data System (ADS)

Zhu, Xiaowei; Iungo, G. Valerio; Leonardi, Stefano; Anderson, William

2017-02-01

For a horizontally homogeneous, neutrally stratified atmospheric boundary layer (ABL), aerodynamic roughness length, z_0, is the effective elevation at which the streamwise component of mean velocity is zero. A priori prediction of z_0 based on topographic attributes remains an open line of inquiry in planetary boundary-layer research. Urban topographies - the topic of this study - exhibit spatial heterogeneities associated with variability of building height, width, and proximity with adjacent buildings; such variability renders a priori, prognostic z_0 models appealing. Here, large-eddy simulation (LES) has been used in an extensive parametric study to characterize the ABL response (and z_0) to a range of synthetic, urban-like topographies wherein statistical moments of the topography have been systematically varied. Using LES results, we determined the hierarchical influence of topographic moments relevant to setting z_0. We demonstrate that standard deviation and skewness are important, while kurtosis is negligible. This finding is reconciled with a model recently proposed by Flack and Schultz (J Fluids Eng 132:041203-1-041203-10, 2010), who demonstrate that z_0 can be modelled with standard deviation and skewness, and two empirical coefficients (one for each moment). We find that the empirical coefficient related to skewness is not constant, but exhibits a dependence on standard deviation over certain ranges. For idealized, quasi-uniform cubic topographies and for complex, fully random urban-like topographies, we demonstrate strong performance of the generalized Flack and Schultz model against contemporary roughness correlations.

Statistical density modification using local pattern matching

DOEpatents

Terwilliger, Thomas C.

2007-01-23

A computer implemented method modifies an experimental electron density map. A set of selected known experimental and model electron density maps is provided and standard templates of electron density are created from the selected experimental and model electron density maps by clustering and averaging values of electron density in a spherical region about each point in a grid that defines each selected known experimental and model electron density maps. Histograms are also created from the selected experimental and model electron density maps that relate the value of electron density at the center of each of the spherical regions to a correlation coefficient of a density surrounding each corresponding grid point in each one of the standard templates. The standard templates and the histograms are applied to grid points on the experimental electron density map to form new estimates of electron density at each grid point in the experimental electron density map.

Bias in logistic regression due to imperfect diagnostic test results and practical correction approaches.

PubMed

Valle, Denis; Lima, Joanna M Tucker; Millar, Justin; Amratia, Punam; Haque, Ubydul

2015-11-04

Logistic regression is a statistical model widely used in cross-sectional and cohort studies to identify and quantify the effects of potential disease risk factors. However, the impact of imperfect tests on adjusted odds ratios (and thus on the identification of risk factors) is under-appreciated. The purpose of this article is to draw attention to the problem associated with modelling imperfect diagnostic tests, and propose simple Bayesian models to adequately address this issue. A systematic literature review was conducted to determine the proportion of malaria studies that appropriately accounted for false-negatives/false-positives in a logistic regression setting. Inference from the standard logistic regression was also compared with that from three proposed Bayesian models using simulations and malaria data from the western Brazilian Amazon. A systematic literature review suggests that malaria epidemiologists are largely unaware of the problem of using logistic regression to model imperfect diagnostic test results. Simulation results reveal that statistical inference can be substantially improved when using the proposed Bayesian models versus the standard logistic regression. Finally, analysis of original malaria data with one of the proposed Bayesian models reveals that microscopy sensitivity is strongly influenced by how long people have lived in the study region, and an important risk factor (i.e., participation in forest extractivism) is identified that would have been missed by standard logistic regression. Given the numerous diagnostic methods employed by malaria researchers and the ubiquitous use of logistic regression to model the results of these diagnostic tests, this paper provides critical guidelines to improve data analysis practice in the presence of misclassification error. Easy-to-use code that can be readily adapted to WinBUGS is provided, enabling straightforward implementation of the proposed Bayesian models.

Multi-region statistical shape model for cochlear implantation

NASA Astrophysics Data System (ADS)

Romera, Jordi; Kjer, H. Martin; Piella, Gemma; Ceresa, Mario; González Ballester, Miguel A.

2016-03-01

Statistical shape models are commonly used to analyze the variability between similar anatomical structures and their use is established as a tool for analysis and segmentation of medical images. However, using a global model to capture the variability of complex structures is not enough to achieve the best results. The complexity of a proper global model increases even more when the amount of data available is limited to a small number of datasets. Typically, the anatomical variability between structures is associated to the variability of their physiological regions. In this paper, a complete pipeline is proposed for building a multi-region statistical shape model to study the entire variability from locally identified physiological regions of the inner ear. The proposed model, which is based on an extension of the Point Distribution Model (PDM), is built for a training set of 17 high-resolution images (24.5 μm voxels) of the inner ear. The model is evaluated according to its generalization ability and specificity. The results are compared with the ones of a global model built directly using the standard PDM approach. The evaluation results suggest that better accuracy can be achieved using a regional modeling of the inner ear.

Verification of relationship model between Korean new elderly class's recovery resilience and productive aging.

PubMed

Cho, Gun-Sang; Kim, Dae-Sung; Yi, Eun-Surk

2015-12-01

The purpose of this study is to verification of relationship model between Korean new elderly class's recovery resilience and productive aging. As of 2013, this study sampled preliminary elderly people in Gyeonggi-do and other provinces nationwide. Data from a total of effective 484 subjects was analyzed. The collected data was processed using the IBM SPSS 20.0 and AMOS 20.0, and underwent descriptive statistical analysis, confirmatory factor analysis, and structure model verification. The path coefficient associated with model fitness was examined. The standardization path coefficient between recovery resilience and productive aging is β=0.975 (t=14.790), revealing a statistically significant positive effect. Thus, it was found that the proposed basic model on the direct path of recovery resilience and productive aging was fit for the model.

Verification of relationship model between Korean new elderly class’s recovery resilience and productive aging

PubMed Central

Cho, Gun-Sang; Kim, Dae-Sung; Yi, Eun-Surk

2015-01-01

The purpose of this study is to verification of relationship model between Korean new elderly class’s recovery resilience and productive aging. As of 2013, this study sampled preliminary elderly people in Gyeonggi-do and other provinces nationwide. Data from a total of effective 484 subjects was analyzed. The collected data was processed using the IBM SPSS 20.0 and AMOS 20.0, and underwent descriptive statistical analysis, confirmatory factor analysis, and structure model verification. The path coefficient associated with model fitness was examined. The standardization path coefficient between recovery resilience and productive aging is β=0.975 (t=14.790), revealing a statistically significant positive effect. Thus, it was found that the proposed basic model on the direct path of recovery resilience and productive aging was fit for the model. PMID:26730383

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

Weaver, Brian Phillip

The purpose of this document is to describe the statistical modeling effort for gas concentrations in WIPP storage containers. The concentration (in ppm) of CO 2 in the headspace volume of standard waste box (SWB) 68685 is shown. A Bayesian approach and an adaptive Metropolis-Hastings algorithm were used.

Modern proposal of methodology for retrieval of characteristic synthetic rainfall hyetographs

NASA Astrophysics Data System (ADS)

Licznar, Paweł; Burszta-Adamiak, Ewa; Łomotowski, Janusz; Stańczyk, Justyna

2017-11-01

Modern engineering workshop of designing and modelling complex drainage systems is based on hydrodynamic modelling and has a probabilistic character. Its practical application requires a change regarding rainfall models accepted at the input. Previously used artificial rainfall models of simplified form, e.g. block precipitation or Euler's type II model rainfall are no longer sufficient. It is noticeable that urgent clarification is needed as regards the methodology of standardized rainfall hyetographs that would take into consideration the specifics of local storm rainfall temporal dynamics. The aim of the paper is to present a proposal for innovative methodology for determining standardized rainfall hyetographs, based on statistical processing of the collection of actual local precipitation characteristics. Proposed methodology is based on the classification of standardized rainfall hyetographs with the use of cluster analysis. Its application is presented on the example of selected rain gauges localized in Poland. Synthetic rainfall hyetographs achieved as a final result may be used for hydrodynamic modelling of sewerage systems, including probabilistic detection of necessary capacity of retention reservoirs.

Middle school science curriculum design and 8th grade student achievement in Massachusetts public schools

NASA Astrophysics Data System (ADS)

Clifford, Betsey A.

The Massachusetts Department of Elementary and Secondary Education (DESE) released proposed Science and Technology/Engineering standards in 2013 outlining the concepts that should be taught at each grade level. Previously, standards were in grade spans and each district determined the method of implementation. There are two different methods used teaching middle school science: integrated and discipline-based. In the proposed standards, the Massachusetts DESE uses grade-by-grade standards using an integrated approach. It was not known if there is a statistically significant difference in student achievement on the 8th grade science MCAS assessment for students taught with an integrated or discipline-based approach. The results on the 8th grade science MCAS test from six public school districts from 2010 -- 2013 were collected and analyzed. The methodology used was quantitative. Results of an ANOVA showed that there was no statistically significant difference in overall student achievement between the two curriculum models. Furthermore, there was no statistically significant difference for the various domains: Earth and Space Science, Life Science, Physical Science, and Technology/Engineering. This information is useful for districts hesitant to make the change from a discipline-based approach to an integrated approach. More research should be conducted on this topic with a larger sample size to better support the results.

Bivariate random-effects meta-analysis models for diagnostic test accuracy studies using arcsine-based transformations.

PubMed

Negeri, Zelalem F; Shaikh, Mateen; Beyene, Joseph

2018-05-11

Diagnostic or screening tests are widely used in medical fields to classify patients according to their disease status. Several statistical models for meta-analysis of diagnostic test accuracy studies have been developed to synthesize test sensitivity and specificity of a diagnostic test of interest. Because of the correlation between test sensitivity and specificity, modeling the two measures using a bivariate model is recommended. In this paper, we extend the current standard bivariate linear mixed model (LMM) by proposing two variance-stabilizing transformations: the arcsine square root and the Freeman-Tukey double arcsine transformation. We compared the performance of the proposed methods with the standard method through simulations using several performance measures. The simulation results showed that our proposed methods performed better than the standard LMM in terms of bias, root mean square error, and coverage probability in most of the scenarios, even when data were generated assuming the standard LMM. We also illustrated the methods using two real data sets. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Reduced Radiation Dose with Model-based Iterative Reconstruction versus Standard Dose with Adaptive Statistical Iterative Reconstruction in Abdominal CT for Diagnosis of Acute Renal Colic.

PubMed

Fontarensky, Mikael; Alfidja, Agaïcha; Perignon, Renan; Schoenig, Arnaud; Perrier, Christophe; Mulliez, Aurélien; Guy, Laurent; Boyer, Louis

2015-07-01

To evaluate the accuracy of reduced-dose abdominal computed tomographic (CT) imaging by using a new generation model-based iterative reconstruction (MBIR) to diagnose acute renal colic compared with a standard-dose abdominal CT with 50% adaptive statistical iterative reconstruction (ASIR). This institutional review board-approved prospective study included 118 patients with symptoms of acute renal colic who underwent the following two successive CT examinations: standard-dose ASIR 50% and reduced-dose MBIR. Two radiologists independently reviewed both CT examinations for presence or absence of renal calculi, differential diagnoses, and associated abnormalities. The imaging findings, radiation dose estimates, and image quality of the two CT reconstruction methods were compared. Concordance was evaluated by κ coefficient, and descriptive statistics and t test were used for statistical analysis. Intraobserver correlation was 100% for the diagnosis of renal calculi (κ = 1). Renal calculus (τ = 98.7%; κ = 0.97) and obstructive upper urinary tract disease (τ = 98.16%; κ = 0.95) were detected, and differential or alternative diagnosis was performed (τ = 98.87% κ = 0.95). MBIR allowed a dose reduction of 84% versus standard-dose ASIR 50% (mean volume CT dose index, 1.7 mGy ± 0.8 [standard deviation] vs 10.9 mGy ± 4.6; mean size-specific dose estimate, 2.2 mGy ± 0.7 vs 13.7 mGy ± 3.9; P < .001) without a conspicuous deterioration in image quality (reduced-dose MBIR vs ASIR 50% mean scores, 3.83 ± 0.49 vs 3.92 ± 0.27, respectively; P = .32) or increase in noise (reduced-dose MBIR vs ASIR 50% mean, respectively, 18.36 HU ± 2.53 vs 17.40 HU ± 3.42). Its main drawback remains the long time required for reconstruction (mean, 40 minutes). A reduced-dose protocol with MBIR allowed a dose reduction of 84% without increasing noise and without an conspicuous deterioration in image quality in patients suspected of having renal colic.

Mechanics and statistics of the worm-like chain

NASA Astrophysics Data System (ADS)

Marantan, Andrew; Mahadevan, L.

2018-02-01

The worm-like chain model is a simple continuum model for the statistical mechanics of a flexible polymer subject to an external force. We offer a tutorial introduction to it using three approaches. First, we use a mesoscopic view, treating a long polymer (in two dimensions) as though it were made of many groups of correlated links or "clinks," allowing us to calculate its average extension as a function of the external force via scaling arguments. We then provide a standard statistical mechanics approach, obtaining the average extension by two different means: the equipartition theorem and the partition function. Finally, we work in a probabilistic framework, taking advantage of the Gaussian properties of the chain in the large-force limit to improve upon the previous calculations of the average extension.

Effect of CorrelatedRotational Noise

NASA Astrophysics Data System (ADS)

Hancock, Benjamin; Wagner, Caleb; Baskaran, Aparna

The traditional model of a self-propelled particle (SPP) is one where the body axis along which the particle travels reorients itself through rotational diffusion. If the reorientation process was driven by colored noise, instead of the standard Gaussian white noise, the resulting statistical mechanics cannot be accessed through conventional methods. In this talk we present results comparing three methods of deriving the statistical mechanics of a SPP with a reorientation process driven by colored noise. We illustrate the differences/similarities in the resulting statistical mechanics by their ability to accurately capture the particles response to external aligning fields.

Random walk to a nonergodic equilibrium concept

NASA Astrophysics Data System (ADS)

Bel, G.; Barkai, E.

2006-01-01

Random walk models, such as the trap model, continuous time random walks, and comb models, exhibit weak ergodicity breaking, when the average waiting time is infinite. The open question is, what statistical mechanical theory replaces the canonical Boltzmann-Gibbs theory for such systems? In this paper a nonergodic equilibrium concept is investigated, for a continuous time random walk model in a potential field. In particular we show that in the nonergodic phase the distribution of the occupation time of the particle in a finite region of space approaches U- or W-shaped distributions related to the arcsine law. We show that when conditions of detailed balance are applied, these distributions depend on the partition function of the problem, thus establishing a relation between the nonergodic dynamics and canonical statistical mechanics. In the ergodic phase the distribution function of the occupation times approaches a δ function centered on the value predicted based on standard Boltzmann-Gibbs statistics. The relation of our work to single-molecule experiments is briefly discussed.

Physics-based statistical learning approach to mesoscopic model selection.

PubMed

Taverniers, Søren; Haut, Terry S; Barros, Kipton; Alexander, Francis J; Lookman, Turab

2015-11-01

In materials science and many other research areas, models are frequently inferred without considering their generalization to unseen data. We apply statistical learning using cross-validation to obtain an optimally predictive coarse-grained description of a two-dimensional kinetic nearest-neighbor Ising model with Glauber dynamics (GD) based on the stochastic Ginzburg-Landau equation (sGLE). The latter is learned from GD "training" data using a log-likelihood analysis, and its predictive ability for various complexities of the model is tested on GD "test" data independent of the data used to train the model on. Using two different error metrics, we perform a detailed analysis of the error between magnetization time trajectories simulated using the learned sGLE coarse-grained description and those obtained using the GD model. We show that both for equilibrium and out-of-equilibrium GD training trajectories, the standard phenomenological description using a quartic free energy does not always yield the most predictive coarse-grained model. Moreover, increasing the amount of training data can shift the optimal model complexity to higher values. Our results are promising in that they pave the way for the use of statistical learning as a general tool for materials modeling and discovery.

Functional annotation of regulatory pathways.

PubMed

Pandey, Jayesh; Koyutürk, Mehmet; Kim, Yohan; Szpankowski, Wojciech; Subramaniam, Shankar; Grama, Ananth

2007-07-01

Standardized annotations of biomolecules in interaction networks (e.g. Gene Ontology) provide comprehensive understanding of the function of individual molecules. Extending such annotations to pathways is a critical component of functional characterization of cellular signaling at the systems level. We propose a framework for projecting gene regulatory networks onto the space of functional attributes using multigraph models, with the objective of deriving statistically significant pathway annotations. We first demonstrate that annotations of pairwise interactions do not generalize to indirect relationships between processes. Motivated by this result, we formalize the problem of identifying statistically overrepresented pathways of functional attributes. We establish the hardness of this problem by demonstrating the non-monotonicity of common statistical significance measures. We propose a statistical model that emphasizes the modularity of a pathway, evaluating its significance based on the coupling of its building blocks. We complement the statistical model by an efficient algorithm and software, Narada, for computing significant pathways in large regulatory networks. Comprehensive results from our methods applied to the Escherichia coli transcription network demonstrate that our approach is effective in identifying known, as well as novel biological pathway annotations. Narada is implemented in Java and is available at http://www.cs.purdue.edu/homes/jpandey/narada/.

Data-optimized source modeling with the Backwards Liouville Test–Kinetic method

DOE PAGES

Woodroffe, J. R.; Brito, T. V.; Jordanova, V. K.; ...

2017-09-14

In the standard practice of neutron multiplicity counting , the first three sampled factorial moments of the event triggered neutron count distribution were used to quantify the three main neutron source terms: the spontaneous fissile material effective mass, the relative (α,n) production and the induced fission source responsible for multiplication. Our study compares three methods to quantify the statistical uncertainty of the estimated mass: the bootstrap method, propagation of variance through moments, and statistical analysis of cycle data method. Each of the three methods was implemented on a set of four different NMC measurements, held at the JRC-laboratory in Ispra,more » Italy, sampling four different Pu samples in a standard Plutonium Scrap Multiplicity Counter (PSMC) well counter.« less

Model specification and bootstrapping for multiply imputed data: An application to count models for the frequency of alcohol use

PubMed Central

Comulada, W. Scott

2015-01-01

Stata’s mi commands provide powerful tools to conduct multiple imputation in the presence of ignorable missing data. In this article, I present Stata code to extend the capabilities of the mi commands to address two areas of statistical inference where results are not easily aggregated across imputed datasets. First, mi commands are restricted to covariate selection. I show how to address model fit to correctly specify a model. Second, the mi commands readily aggregate model-based standard errors. I show how standard errors can be bootstrapped for situations where model assumptions may not be met. I illustrate model specification and bootstrapping on frequency counts for the number of times that alcohol was consumed in data with missing observations from a behavioral intervention. PMID:26973439

Evaluation of "e-rater"® for the "Praxis I"®Writing Test. Research Report. ETS RR-15-03

ERIC Educational Resources Information Center

Ramineni, Chaitanya; Trapani, Catherine S.; Williamson, David M.

2015-01-01

Automated scoring models were trained and evaluated for the essay task in the "Praxis I"® writing test. Prompt-specific and generic "e-rater"® scoring models were built, and evaluation statistics, such as quadratic weighted kappa, Pearson correlation, and standardized differences in mean scores, were examined to evaluate the…

Evaluation of the "e-rater"® Scoring Engine for the "TOEFL"® Independent and Integrated Prompts. Research Report. ETS RR-12-06

ERIC Educational Resources Information Center

Ramineni, Chaitanya; Trapani, Catherine S.; Williamson, David M.; Davey, Tim; Bridgeman, Brent

2012-01-01

Scoring models for the "e-rater"® system were built and evaluated for the "TOEFL"® exam's independent and integrated writing prompts. Prompt-specific and generic scoring models were built, and evaluation statistics, such as weighted kappas, Pearson correlations, standardized differences in mean scores, and correlations with…

Evaluation of the "e-rater"® Scoring Engine for the "GRE"® Issue and Argument Prompts. Research Report. ETS RR-12-02

ERIC Educational Resources Information Center

Ramineni, Chaitanya; Trapani, Catherine S.; Williamson, David M.; Davey, Tim; Bridgeman, Brent

2012-01-01

Automated scoring models for the "e-rater"® scoring engine were built and evaluated for the "GRE"® argument and issue-writing tasks. Prompt-specific, generic, and generic with prompt-specific intercept scoring models were built and evaluation statistics such as weighted kappas, Pearson correlations, standardized difference in…

A direct sensitivity approach to predict hourly ozone resulting from compliance with the National Ambient Air Quality Standard.

PubMed

Simon, Heather; Baker, Kirk R; Akhtar, Farhan; Napelenok, Sergey L; Possiel, Norm; Wells, Benjamin; Timin, Brian

2013-03-05

In setting primary ambient air quality standards, the EPA's responsibility under the law is to establish standards that protect public health. As part of the current review of the ozone National Ambient Air Quality Standard (NAAQS), the US EPA evaluated the health exposure and risks associated with ambient ozone pollution using a statistical approach to adjust recent air quality to simulate just meeting the current standard level, without specifying emission control strategies. One drawback of this purely statistical concentration rollback approach is that it does not take into account spatial and temporal heterogeneity of ozone response to emissions changes. The application of the higher-order decoupled direct method (HDDM) in the community multiscale air quality (CMAQ) model is discussed here to provide an example of a methodology that could incorporate this variability into the risk assessment analyses. Because this approach includes a full representation of the chemical production and physical transport of ozone in the atmosphere, it does not require assumed background concentrations, which have been applied to constrain estimates from past statistical techniques. The CMAQ-HDDM adjustment approach is extended to measured ozone concentrations by determining typical sensitivities at each monitor location and hour of the day based on a linear relationship between first-order sensitivities and hourly ozone values. This approach is demonstrated by modeling ozone responses for monitor locations in Detroit and Charlotte to domain-wide reductions in anthropogenic NOx and VOCs emissions. As seen in previous studies, ozone response calculated using HDDM compared well to brute-force emissions changes up to approximately a 50% reduction in emissions. A new stepwise approach is developed here to apply this method to emissions reductions beyond 50% allowing for the simulation of more stringent reductions in ozone concentrations. Compared to previous rollback methods, this application of modeled sensitivities to ambient ozone concentrations provides a more realistic spatial response of ozone concentrations at monitors inside and outside the urban core and at hours of both high and low ozone concentrations.

GAMBIT: the global and modular beyond-the-standard-model inference tool

NASA Astrophysics Data System (ADS)

Athron, Peter; Balazs, Csaba; Bringmann, Torsten; Buckley, Andy; Chrząszcz, Marcin; Conrad, Jan; Cornell, Jonathan M.; Dal, Lars A.; Dickinson, Hugh; Edsjö, Joakim; Farmer, Ben; Gonzalo, Tomás E.; Jackson, Paul; Krislock, Abram; Kvellestad, Anders; Lundberg, Johan; McKay, James; Mahmoudi, Farvah; Martinez, Gregory D.; Putze, Antje; Raklev, Are; Ripken, Joachim; Rogan, Christopher; Saavedra, Aldo; Savage, Christopher; Scott, Pat; Seo, Seon-Hee; Serra, Nicola; Weniger, Christoph; White, Martin; Wild, Sebastian

2017-11-01

We describe the open-source global fitting package GAMBIT: the Global And Modular Beyond-the-Standard-Model Inference Tool. GAMBIT combines extensive calculations of observables and likelihoods in particle and astroparticle physics with a hierarchical model database, advanced tools for automatically building analyses of essentially any model, a flexible and powerful system for interfacing to external codes, a suite of different statistical methods and parameter scanning algorithms, and a host of other utilities designed to make scans faster, safer and more easily-extendible than in the past. Here we give a detailed description of the framework, its design and motivation, and the current models and other specific components presently implemented in GAMBIT. Accompanying papers deal with individual modules and present first GAMBIT results. GAMBIT can be downloaded from gambit.hepforge.org.

Statistical core design methodology using the VIPRE thermal-hydraulics code

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

Lloyd, M.W.; Feltus, M.A.

1994-12-31

This Penn State Statistical Core Design Methodology (PSSCDM) is unique because it not only includes the EPRI correlation/test data standard deviation but also the computational uncertainty for the VIPRE code model and the new composite box design correlation. The resultant PSSCDM equation mimics the EPRI DNBR correlation results well, with an uncertainty of 0.0389. The combined uncertainty yields a new DNBR limit of 1.18 that will provide more plant operational flexibility. This methodology and its associated correlation and uniqe coefficients are for a very particular VIPRE model; thus, the correlation will be specifically linked with the lumped channel and subchannelmore » layout. The results of this research and methodology, however, can be applied to plant-specific VIPRE models.« less

Incorporating Midbrain Adaptation to Mean Sound Level Improves Models of Auditory Cortical Processing

PubMed Central

Schoppe, Oliver; King, Andrew J.; Schnupp, Jan W.H.; Harper, Nicol S.

2016-01-01

Adaptation to stimulus statistics, such as the mean level and contrast of recently heard sounds, has been demonstrated at various levels of the auditory pathway. It allows the nervous system to operate over the wide range of intensities and contrasts found in the natural world. Yet current standard models of the response properties of auditory neurons do not incorporate such adaptation. Here we present a model of neural responses in the ferret auditory cortex (the IC Adaptation model), which takes into account adaptation to mean sound level at a lower level of processing: the inferior colliculus (IC). The model performs high-pass filtering with frequency-dependent time constants on the sound spectrogram, followed by half-wave rectification, and passes the output to a standard linear–nonlinear (LN) model. We find that the IC Adaptation model consistently predicts cortical responses better than the standard LN model for a range of synthetic and natural stimuli. The IC Adaptation model introduces no extra free parameters, so it improves predictions without sacrificing parsimony. Furthermore, the time constants of adaptation in the IC appear to be matched to the statistics of natural sounds, suggesting that neurons in the auditory midbrain predict the mean level of future sounds and adapt their responses appropriately. SIGNIFICANCE STATEMENT An ability to accurately predict how sensory neurons respond to novel stimuli is critical if we are to fully characterize their response properties. Attempts to model these responses have had a distinguished history, but it has proven difficult to improve their predictive power significantly beyond that of simple, mostly linear receptive field models. Here we show that auditory cortex receptive field models benefit from a nonlinear preprocessing stage that replicates known adaptation properties of the auditory midbrain. This improves their predictive power across a wide range of stimuli but keeps model complexity low as it introduces no new free parameters. Incorporating the adaptive coding properties of neurons will likely improve receptive field models in other sensory modalities too. PMID:26758822

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

PubMed Central

Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. PMID:26977450

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

PubMed

Falat, Lukas; Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

Development of a funding, cost, and spending model for satellite projects

NASA Technical Reports Server (NTRS)

Johnson, Jesse P.

1989-01-01

The need for a predictive budget/funging model is obvious. The current models used by the Resource Analysis Office (RAO) are used to predict the total costs of satellite projects. An effort to extend the modeling capabilities from total budget analysis to total budget and budget outlays over time analysis was conducted. A statistical based and data driven methodology was used to derive and develop the model. Th budget data for the last 18 GSFC-sponsored satellite projects were analyzed and used to build a funding model which would describe the historical spending patterns. This raw data consisted of dollars spent in that specific year and their 1989 dollar equivalent. This data was converted to the standard format used by the RAO group and placed in a database. A simple statistical analysis was performed to calculate the gross statistics associated with project length and project cost ant the conditional statistics on project length and project cost. The modeling approach used is derived form the theory of embedded statistics which states that properly analyzed data will produce the underlying generating function. The process of funding large scale projects over extended periods of time is described by Life Cycle Cost Models (LCCM). The data was analyzed to find a model in the generic form of a LCCM. The model developed is based on a Weibull function whose parameters are found by both nonlinear optimization and nonlinear regression. In order to use this model it is necessary to transform the problem from a dollar/time space to a percentage of total budget/time space. This transformation is equivalent to moving to a probability space. By using the basic rules of probability, the validity of both the optimization and the regression steps are insured. This statistically significant model is then integrated and inverted. The resulting output represents a project schedule which relates the amount of money spent to the percentage of project completion.

Emergent kink statistics at finite temperature

DOE PAGES

Lopez-Ruiz, Miguel Angel; Yepez-Martinez, Tochtli; Szczepaniak, Adam; ...

2017-07-25

In this paper we use 1D quantum mechanical systems with Higgs-like interaction potential to study the emergence of topological objects at finite temperature. Two different model systems are studied, the standard double-well potential model and a newly introduced discrete kink model. Using Monte-Carlo simulations as well as analytic methods, we demonstrate how kinks become abundant at low temperatures. These results may shed useful insights on how topological phenomena may occur in QCD.

A statistical assessment of seismic models of the U.S. continental crust using Bayesian inversion of ambient noise surface wave dispersion data

NASA Astrophysics Data System (ADS)

Olugboji, T. M.; Lekic, V.; McDonough, W.

2017-07-01

We present a new approach for evaluating existing crustal models using ambient noise data sets and its associated uncertainties. We use a transdimensional hierarchical Bayesian inversion approach to invert ambient noise surface wave phase dispersion maps for Love and Rayleigh waves using measurements obtained from Ekström (2014). Spatiospectral analysis shows that our results are comparable to a linear least squares inverse approach (except at higher harmonic degrees), but the procedure has additional advantages: (1) it yields an autoadaptive parameterization that follows Earth structure without making restricting assumptions on model resolution (regularization or damping) and data errors; (2) it can recover non-Gaussian phase velocity probability distributions while quantifying the sources of uncertainties in the data measurements and modeling procedure; and (3) it enables statistical assessments of different crustal models (e.g., CRUST1.0, LITHO1.0, and NACr14) using variable resolution residual and standard deviation maps estimated from the ensemble. These assessments show that in the stable old crust of the Archean, the misfits are statistically negligible, requiring no significant update to crustal models from the ambient noise data set. In other regions of the U.S., significant updates to regionalization and crustal structure are expected especially in the shallow sedimentary basins and the tectonically active regions, where the differences between model predictions and data are statistically significant.

Time Series Expression Analyses Using RNA-seq: A Statistical Approach

PubMed Central

Oh, Sunghee; Song, Seongho; Grabowski, Gregory; Zhao, Hongyu; Noonan, James P.

2013-01-01

RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis. PMID:23586021

Advanced statistical energy analysis

NASA Astrophysics Data System (ADS)

Heron, K. H.

1994-09-01

A high-frequency theory (advanced statistical energy analysis (ASEA)) is developed which takes account of the mechanism of tunnelling and uses a ray theory approach to track the power flowing around a plate or a beam network and then uses statistical energy analysis (SEA) to take care of any residual power. ASEA divides the energy of each sub-system into energy that is freely available for transfer to other sub-systems and energy that is fixed within the sub-systems that are physically separate and can be interpreted as a series of mathematical models, the first of which is identical to standard SEA and subsequent higher order models are convergent on an accurate prediction. Using a structural assembly of six rods as an example, ASEA is shown to converge onto the exact results while SEA is shown to overpredict by up to 60 dB.

Time series expression analyses using RNA-seq: a statistical approach.

PubMed

Oh, Sunghee; Song, Seongho; Grabowski, Gregory; Zhao, Hongyu; Noonan, James P

2013-01-01

RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis.

Lung Cancer Risk Prediction Model Incorporating Lung Function: Development and Validation in the UK Biobank Prospective Cohort Study.

PubMed

Muller, David C; Johansson, Mattias; Brennan, Paul

2017-03-10

Purpose Several lung cancer risk prediction models have been developed, but none to date have assessed the predictive ability of lung function in a population-based cohort. We sought to develop and internally validate a model incorporating lung function using data from the UK Biobank prospective cohort study. Methods This analysis included 502,321 participants without a previous diagnosis of lung cancer, predominantly between 40 and 70 years of age. We used flexible parametric survival models to estimate the 2-year probability of lung cancer, accounting for the competing risk of death. Models included predictors previously shown to be associated with lung cancer risk, including sex, variables related to smoking history and nicotine addiction, medical history, family history of lung cancer, and lung function (forced expiratory volume in 1 second [FEV1]). Results During accumulated follow-up of 1,469,518 person-years, there were 738 lung cancer diagnoses. A model incorporating all predictors had excellent discrimination (concordance (c)-statistic [95% CI] = 0.85 [0.82 to 0.87]). Internal validation suggested that the model will discriminate well when applied to new data (optimism-corrected c-statistic = 0.84). The full model, including FEV1, also had modestly superior discriminatory power than one that was designed solely on the basis of questionnaire variables (c-statistic = 0.84 [0.82 to 0.86]; optimism-corrected c-statistic = 0.83; p FEV1 = 3.4 × 10 -13 ). The full model had better discrimination than standard lung cancer screening eligibility criteria (c-statistic = 0.66 [0.64 to 0.69]). Conclusion A risk prediction model that includes lung function has strong predictive ability, which could improve eligibility criteria for lung cancer screening programs.

Does transport time help explain the high trauma mortality rates in rural areas? New and traditional predictors assessed by new and traditional statistical methods

PubMed Central

Røislien, Jo; Lossius, Hans Morten; Kristiansen, Thomas

2015-01-01

Background Trauma is a leading global cause of death. Trauma mortality rates are higher in rural areas, constituting a challenge for quality and equality in trauma care. The aim of the study was to explore population density and transport time to hospital care as possible predictors of geographical differences in mortality rates, and to what extent choice of statistical method might affect the analytical results and accompanying clinical conclusions. Methods Using data from the Norwegian Cause of Death registry, deaths from external causes 1998–2007 were analysed. Norway consists of 434 municipalities, and municipality population density and travel time to hospital care were entered as predictors of municipality mortality rates in univariate and multiple regression models of increasing model complexity. We fitted linear regression models with continuous and categorised predictors, as well as piecewise linear and generalised additive models (GAMs). Models were compared using Akaike's information criterion (AIC). Results Population density was an independent predictor of trauma mortality rates, while the contribution of transport time to hospital care was highly dependent on choice of statistical model. A multiple GAM or piecewise linear model was superior, and similar, in terms of AIC. However, while transport time was statistically significant in multiple models with piecewise linear or categorised predictors, it was not in GAM or standard linear regression. Conclusions Population density is an independent predictor of trauma mortality rates. The added explanatory value of transport time to hospital care is marginal and model-dependent, highlighting the importance of exploring several statistical models when studying complex associations in observational data. PMID:25972600

Do classroom ventilation rates in California elementary schools influence standardized test scores? Results from a prospective study.

PubMed

Mendell, M J; Eliseeva, E A; Davies, M M; Lobscheid, A

2016-08-01

Limited evidence has associated lower ventilation rates (VRs) in schools with reduced student learning or achievement. We analyzed longitudinal data collected over two school years from 150 classrooms in 28 schools within three California school districts. We estimated daily classroom VRs from real-time indoor carbon dioxide measured by web-connected sensors. School districts provided individual-level scores on standard tests in Math and English, and classroom-level demographic data. Analyses assessing learning effects used two VR metrics: average VRs for 30 days prior to tests, and proportion of prior daily VRs above specified thresholds during the year. We estimated relationships between scores and VR metrics in multivariate models with generalized estimating equations. All school districts had median school-year VRs below the California VR standard. Most models showed some positive associations of VRs with test scores; however, estimates varied in magnitude and few 95% confidence intervals excluded the null. Combined-district models estimated statistically significant increases of 0.6 points (P = 0.01) on English tests for each 10% increase in prior 30-day VRs. Estimated increases in Math were of similar magnitude but not statistically significant. Findings suggest potential small positive associations between classroom VRs and learning. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

An Econometric Model of External Labor Supply to the Establishment Within a Confined Geographic Market.

ERIC Educational Resources Information Center

Hines, Robert James

The study conducted in the Buffalo, New York standard metropolitan statistical area, was undertaken to formulate and test a simple model of labor supply for a local labor market. The principal variables to be examined to determine the external supply function of labor to the establishment are variants of the rate of change of the entry wage and…

Improving the Power of GWAS and Avoiding Confounding from Population Stratification with PC-Select

PubMed Central

Tucker, George; Price, Alkes L.; Berger, Bonnie

2014-01-01

Using a reduced subset of SNPs in a linear mixed model can improve power for genome-wide association studies, yet this can result in insufficient correction for population stratification. We propose a hybrid approach using principal components that does not inflate statistics in the presence of population stratification and improves power over standard linear mixed models. PMID:24788602

Regression and statistical shape model based substitute CT generation for MRI alone external beam radiation therapy from standard clinical MRI sequences.

PubMed

Ghose, Soumya; Greer, Peter B; Sun, Jidi; Pichler, Peter; Rivest-Henault, David; Mitra, Jhimli; Richardson, Haylea; Wratten, Chris; Martin, Jarad; Arm, Jameen; Best, Leah; Dowling, Jason A

2017-10-27

In MR only radiation therapy planning, generation of the tissue specific HU map directly from the MRI would eliminate the need of CT image acquisition and may improve radiation therapy planning. The aim of this work is to generate and validate substitute CT (sCT) scans generated from standard T2 weighted MR pelvic scans in prostate radiation therapy dose planning. A Siemens Skyra 3T MRI scanner with laser bridge, flat couch and pelvic coil mounts was used to scan 39 patients scheduled for external beam radiation therapy for localized prostate cancer. For sCT generation a whole pelvis MRI (1.6 mm 3D isotropic T2w SPACE sequence) was acquired. Patients received a routine planning CT scan. Co-registered whole pelvis CT and T2w MRI pairs were used as training images. Advanced tissue specific non-linear regression models to predict HU for the fat, muscle, bladder and air were created from co-registered CT-MRI image pairs. On a test case T2w MRI, the bones and bladder were automatically segmented using a novel statistical shape and appearance model, while other soft tissues were separated using an Expectation-Maximization based clustering model. The CT bone in the training database that was most 'similar' to the segmented bone was then transformed with deformable registration to create the sCT component of the test case T2w MRI bone tissue. Predictions for the bone, air and soft tissue from the separate regression models were successively combined to generate a whole pelvis sCT. The change in monitor units between the sCT-based plans relative to the gold standard CT plan for the same IMRT dose plan was found to be [Formula: see text] (mean  ±  standard deviation) for 39 patients. The 3D Gamma pass rate was [Formula: see text] (2 mm/2%). The novel hybrid model is computationally efficient, generating an sCT in 20 min from standard T2w images for prostate cancer radiation therapy dose planning and DRR generation.

Regression and statistical shape model based substitute CT generation for MRI alone external beam radiation therapy from standard clinical MRI sequences

NASA Astrophysics Data System (ADS)

Ghose, Soumya; Greer, Peter B.; Sun, Jidi; Pichler, Peter; Rivest-Henault, David; Mitra, Jhimli; Richardson, Haylea; Wratten, Chris; Martin, Jarad; Arm, Jameen; Best, Leah; Dowling, Jason A.

2017-11-01

In MR only radiation therapy planning, generation of the tissue specific HU map directly from the MRI would eliminate the need of CT image acquisition and may improve radiation therapy planning. The aim of this work is to generate and validate substitute CT (sCT) scans generated from standard T2 weighted MR pelvic scans in prostate radiation therapy dose planning. A Siemens Skyra 3T MRI scanner with laser bridge, flat couch and pelvic coil mounts was used to scan 39 patients scheduled for external beam radiation therapy for localized prostate cancer. For sCT generation a whole pelvis MRI (1.6 mm 3D isotropic T2w SPACE sequence) was acquired. Patients received a routine planning CT scan. Co-registered whole pelvis CT and T2w MRI pairs were used as training images. Advanced tissue specific non-linear regression models to predict HU for the fat, muscle, bladder and air were created from co-registered CT-MRI image pairs. On a test case T2w MRI, the bones and bladder were automatically segmented using a novel statistical shape and appearance model, while other soft tissues were separated using an Expectation-Maximization based clustering model. The CT bone in the training database that was most ‘similar’ to the segmented bone was then transformed with deformable registration to create the sCT component of the test case T2w MRI bone tissue. Predictions for the bone, air and soft tissue from the separate regression models were successively combined to generate a whole pelvis sCT. The change in monitor units between the sCT-based plans relative to the gold standard CT plan for the same IMRT dose plan was found to be 0.3%+/-0.9% (mean  ±  standard deviation) for 39 patients. The 3D Gamma pass rate was 99.8+/-0.00 (2 mm/2%). The novel hybrid model is computationally efficient, generating an sCT in 20 min from standard T2w images for prostate cancer radiation therapy dose planning and DRR generation.

MUSiC - Model-independent search for deviations from Standard Model predictions in CMS

NASA Astrophysics Data System (ADS)

Pieta, Holger

2010-02-01

We present an approach for a model independent search in CMS. Systematically scanning the data for deviations from the standard model Monte Carlo expectations, such an analysis can help to understand the detector and tune event generators. By minimizing the theoretical bias the analysis is furthermore sensitive to a wide range of models for new physics, including the uncounted number of models not-yet-thought-of. After sorting the events into classes defined by their particle content (leptons, photons, jets and missing transverse energy), a minimally prejudiced scan is performed on a number of distributions. Advanced statistical methods are used to determine the significance of the deviating regions, rigorously taking systematic uncertainties into account. A number of benchmark scenarios, including common models of new physics and possible detector effects, have been used to gauge the power of such a method. )

Robust Mean and Covariance Structure Analysis through Iteratively Reweighted Least Squares.

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Bentler, Peter M.

2000-01-01

Adapts robust schemes to mean and covariance structures, providing an iteratively reweighted least squares approach to robust structural equation modeling. Each case is weighted according to its distance, based on first and second order moments. Test statistics and standard error estimators are given. (SLD)

Are Three Sheets Enough? Using Toilet Paper to Teach Science and Mathematics

ERIC Educational Resources Information Center

Woolverton, Christopher J.; Woolverton, Lyssa N.

2006-01-01

Toilet paper (TP) composition and physical characteristics were used to model scientific investigations that combined several "National Science Education Standards." Experiments with TP permitted the integration of TP history, societal change resulting from invention, mathematics (including geometry and statistics), germ theory, and personal…

NASA standard: Trend analysis techniques

NASA Technical Reports Server (NTRS)

1988-01-01

This Standard presents descriptive and analytical techniques for NASA trend analysis applications. Trend analysis is applicable in all organizational elements of NASA connected with, or supporting, developmental/operational programs. Use of this Standard is not mandatory; however, it should be consulted for any data analysis activity requiring the identification or interpretation of trends. Trend Analysis is neither a precise term nor a circumscribed methodology, but rather connotes, generally, quantitative analysis of time-series data. For NASA activities, the appropriate and applicable techniques include descriptive and graphical statistics, and the fitting or modeling of data by linear, quadratic, and exponential models. Usually, but not always, the data is time-series in nature. Concepts such as autocorrelation and techniques such as Box-Jenkins time-series analysis would only rarely apply and are not included in this Standard. The document presents the basic ideas needed for qualitative and quantitative assessment of trends, together with relevant examples. A list of references provides additional sources of information.

Statistical physics of the symmetric group.

PubMed

Williams, Mobolaji

2017-04-01

Ordered chains (such as chains of amino acids) are ubiquitous in biological cells, and these chains perform specific functions contingent on the sequence of their components. Using the existence and general properties of such sequences as a theoretical motivation, we study the statistical physics of systems whose state space is defined by the possible permutations of an ordered list, i.e., the symmetric group, and whose energy is a function of how certain permutations deviate from some chosen correct ordering. Such a nonfactorizable state space is quite different from the state spaces typically considered in statistical physics systems and consequently has novel behavior in systems with interacting and even noninteracting Hamiltonians. Various parameter choices of a mean-field model reveal the system to contain five different physical regimes defined by two transition temperatures, a triple point, and a quadruple point. Finally, we conclude by discussing how the general analysis can be extended to state spaces with more complex combinatorial properties and to other standard questions of statistical mechanics models.

Statistical physics of the symmetric group

NASA Astrophysics Data System (ADS)

Williams, Mobolaji

2017-04-01

Ordered chains (such as chains of amino acids) are ubiquitous in biological cells, and these chains perform specific functions contingent on the sequence of their components. Using the existence and general properties of such sequences as a theoretical motivation, we study the statistical physics of systems whose state space is defined by the possible permutations of an ordered list, i.e., the symmetric group, and whose energy is a function of how certain permutations deviate from some chosen correct ordering. Such a nonfactorizable state space is quite different from the state spaces typically considered in statistical physics systems and consequently has novel behavior in systems with interacting and even noninteracting Hamiltonians. Various parameter choices of a mean-field model reveal the system to contain five different physical regimes defined by two transition temperatures, a triple point, and a quadruple point. Finally, we conclude by discussing how the general analysis can be extended to state spaces with more complex combinatorial properties and to other standard questions of statistical mechanics models.

Standard deviation analysis of the mastoid fossa temperature differential reading: a potential model for objective chiropractic assessment.

PubMed

Hart, John

2011-03-01

This study describes a model for statistically analyzing follow-up numeric-based chiropractic spinal assessments for an individual patient based on his or her own baseline. Ten mastoid fossa temperature differential readings (MFTD) obtained from a chiropractic patient were used in the study. The first eight readings served as baseline and were compared to post-adjustment readings. One of the two post-adjustment MFTD readings fell outside two standard deviations of the baseline mean and therefore theoretically represents improvement according to pattern analysis theory. This study showed how standard deviation analysis may be used to identify future outliers for an individual patient based on his or her own baseline data. Copyright © 2011 National University of Health Sciences. Published by Elsevier Inc. All rights reserved.

Multicollinearity and Regression Analysis

NASA Astrophysics Data System (ADS)

Daoud, Jamal I.

2017-12-01

In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.

Implementing statistical equating for MRCP(UK) Parts 1 and 2.

PubMed

McManus, I C; Chis, Liliana; Fox, Ray; Waller, Derek; Tang, Peter

2014-09-26

The MRCP(UK) exam, in 2008 and 2010, changed the standard-setting of its Part 1 and Part 2 examinations from a hybrid Angoff/Hofstee method to statistical equating using Item Response Theory, the reference group being UK graduates. The present paper considers the implementation of the change, the question of whether the pass rate increased amongst non-UK candidates, any possible role of Differential Item Functioning (DIF), and changes in examination predictive validity after the change. Analysis of data of MRCP(UK) Part 1 exam from 2003 to 2013 and Part 2 exam from 2005 to 2013. Inspection suggested that Part 1 pass rates were stable after the introduction of statistical equating, but showed greater annual variation probably due to stronger candidates taking the examination earlier. Pass rates seemed to have increased in non-UK graduates after equating was introduced, but was not associated with any changes in DIF after statistical equating. Statistical modelling of the pass rates for non-UK graduates found that pass rates, in both Part 1 and Part 2, were increasing year on year, with the changes probably beginning before the introduction of equating. The predictive validity of Part 1 for Part 2 was higher with statistical equating than with the previous hybrid Angoff/Hofstee method, confirming the utility of IRT-based statistical equating. Statistical equating was successfully introduced into the MRCP(UK) Part 1 and Part 2 written examinations, resulting in higher predictive validity than the previous Angoff/Hofstee standard setting. Concerns about an artefactual increase in pass rates for non-UK candidates after equating were shown not to be well-founded. Most likely the changes resulted from a genuine increase in candidate ability, albeit for reasons which remain unclear, coupled with a cognitive illusion giving the impression of a step-change immediately after equating began. Statistical equating provides a robust standard-setting method, with a better theoretical foundation than judgemental techniques such as Angoff, and is more straightforward and requires far less examiner time to provide a more valid result. The present study provides a detailed case study of introducing statistical equating, and issues which may need to be considered with its introduction.

Dealing with missing standard deviation and mean values in meta-analysis of continuous outcomes: a systematic review.

PubMed

Weir, Christopher J; Butcher, Isabella; Assi, Valentina; Lewis, Stephanie C; Murray, Gordon D; Langhorne, Peter; Brady, Marian C

2018-03-07

Rigorous, informative meta-analyses rely on availability of appropriate summary statistics or individual participant data. For continuous outcomes, especially those with naturally skewed distributions, summary information on the mean or variability often goes unreported. While full reporting of original trial data is the ideal, we sought to identify methods for handling unreported mean or variability summary statistics in meta-analysis. We undertook two systematic literature reviews to identify methodological approaches used to deal with missing mean or variability summary statistics. Five electronic databases were searched, in addition to the Cochrane Colloquium abstract books and the Cochrane Statistics Methods Group mailing list archive. We also conducted cited reference searching and emailed topic experts to identify recent methodological developments. Details recorded included the description of the method, the information required to implement the method, any underlying assumptions and whether the method could be readily applied in standard statistical software. We provided a summary description of the methods identified, illustrating selected methods in example meta-analysis scenarios. For missing standard deviations (SDs), following screening of 503 articles, fifteen methods were identified in addition to those reported in a previous review. These included Bayesian hierarchical modelling at the meta-analysis level; summary statistic level imputation based on observed SD values from other trials in the meta-analysis; a practical approximation based on the range; and algebraic estimation of the SD based on other summary statistics. Following screening of 1124 articles for methods estimating the mean, one approximate Bayesian computation approach and three papers based on alternative summary statistics were identified. Illustrative meta-analyses showed that when replacing a missing SD the approximation using the range minimised loss of precision and generally performed better than omitting trials. When estimating missing means, a formula using the median, lower quartile and upper quartile performed best in preserving the precision of the meta-analysis findings, although in some scenarios, omitting trials gave superior results. Methods based on summary statistics (minimum, maximum, lower quartile, upper quartile, median) reported in the literature facilitate more comprehensive inclusion of randomised controlled trials with missing mean or variability summary statistics within meta-analyses.

Probabilistic registration of an unbiased statistical shape model to ultrasound images of the spine

NASA Astrophysics Data System (ADS)

Rasoulian, Abtin; Rohling, Robert N.; Abolmaesumi, Purang

2012-02-01

The placement of an epidural needle is among the most difficult regional anesthetic techniques. Ultrasound has been proposed to improve success of placement. However, it has not become the standard-of-care because of limitations in the depictions and interpretation of the key anatomical features. We propose to augment the ultrasound images with a registered statistical shape model of the spine to aid interpretation. The model is created with a novel deformable group-wise registration method which utilizes a probabilistic approach to register groups of point sets. The method is compared to a volume-based model building technique and it demonstrates better generalization and compactness. We instantiate and register the shape model to a spine surface probability map extracted from the ultrasound images. Validation is performed on human subjects. The achieved registration accuracy (2-4 mm) is sufficient to guide the choice of puncture site and trajectory of an epidural needle.

Systematic Review and Meta-Analysis of Studies Evaluating Diagnostic Test Accuracy: A Practical Review for Clinical Researchers-Part II. Statistical Methods of Meta-Analysis

PubMed Central

Lee, Juneyoung; Kim, Kyung Won; Choi, Sang Hyun; Huh, Jimi

2015-01-01

Meta-analysis of diagnostic test accuracy studies differs from the usual meta-analysis of therapeutic/interventional studies in that, it is required to simultaneously analyze a pair of two outcome measures such as sensitivity and specificity, instead of a single outcome. Since sensitivity and specificity are generally inversely correlated and could be affected by a threshold effect, more sophisticated statistical methods are required for the meta-analysis of diagnostic test accuracy. Hierarchical models including the bivariate model and the hierarchical summary receiver operating characteristic model are increasingly being accepted as standard methods for meta-analysis of diagnostic test accuracy studies. We provide a conceptual review of statistical methods currently used and recommended for meta-analysis of diagnostic test accuracy studies. This article could serve as a methodological reference for those who perform systematic review and meta-analysis of diagnostic test accuracy studies. PMID:26576107

Evaluation of Satellite and Model Precipitation Products Over Turkey

NASA Astrophysics Data System (ADS)

Yilmaz, M. T.; Amjad, M.

2017-12-01

Satellite-based remote sensing, gauge stations, and models are the three major platforms to acquire precipitation dataset. Among them satellites and models have the advantage of retrieving spatially and temporally continuous and consistent datasets, while the uncertainty estimates of these retrievals are often required for many hydrological studies to understand the source and the magnitude of the uncertainty in hydrological response parameters. In this study, satellite and model precipitation data products are validated over various temporal scales (daily, 3-daily, 7-daily, 10-daily and monthly) using in-situ measured precipitation observations from a network of 733 gauges from all over the Turkey. Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 version 7 and European Center of Medium-Range Weather Forecast (ECMWF) model estimates (daily, 3-daily, 7-daily and 10-daily accumulated forecast) are used in this study. Retrievals are evaluated for their mean and standard deviation and their accuracies are evaluated via bias, root mean square error, error standard deviation and correlation coefficient statistics. Intensity vs frequency analysis and some contingency table statistics like percent correct, probability of detection, false alarm ratio and critical success index are determined using daily time-series. Both ECMWF forecasts and TRMM observations, on average, overestimate the precipitation compared to gauge estimates; wet biases are 10.26 mm/month and 8.65 mm/month, respectively for ECMWF and TRMM. RMSE values of ECMWF forecasts and TRMM estimates are 39.69 mm/month and 41.55 mm/month, respectively. Monthly correlations between Gauges-ECMWF, Gauges-TRMM and ECMWF-TRMM are 0.76, 0.73 and 0.81, respectively. The model and the satellite error statistics are further compared against the gauges error statistics based on inverse distance weighting (IWD) analysis. Both the model and satellite data have less IWD errors (14.72 mm/month and 10.75 mm/month, respectively) compared to gauges IWD error (21.58 mm/month). These results show that, on average, ECMWF forecast data have higher skill than TRMM observations. Overall, both ECMWF forecast data and TRMM observations show good potential for catchment scale hydrological analysis.

A statistical approach for generating synthetic tip stress data from limited CPT soundings

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

Basalams, M.K.

CPT tip stress data obtained from a Uranium mill tailings impoundment are treated as time series. A statistical class of models that was developed to model time series is explored to investigate its applicability in modeling the tip stress series. These models were developed by Box and Jenkins (1970) and are known as Autoregressive Moving Average (ARMA) models. This research demonstrates how to apply the ARMA models to tip stress series. Generation of synthetic tip stress series that preserve the main statistical characteristics of the measured series is also investigated. Multiple regression analysis is used to model the regional variationmore » of the ARMA model parameters as well as the regional variation of the mean and the standard deviation of the measured tip stress series. The reliability of the generated series is investigated from a geotechnical point of view as well as from a statistical point of view. Estimation of the total settlement using the measured and the generated series subjected to the same loading condition are performed. The variation of friction angle with depth of the impoundment materials is also investigated. This research shows that these series can be modeled by the Box and Jenkins ARMA models. A third degree Autoregressive model AR(3) is selected to represent these series. A theoretical double exponential density function is fitted to the AR(3) model residuals. Synthetic tip stress series are generated at nearby locations. The generated series are shown to be reliable in estimating the total settlement and the friction angle variation with depth for this particular site.« less

External Validation of a Case-Mix Adjustment Model for the Standardized Reporting of 30-Day Stroke Mortality Rates in China.

PubMed

Yu, Ping; Pan, Yuesong; Wang, Yongjun; Wang, Xianwei; Liu, Liping; Ji, Ruijun; Meng, Xia; Jing, Jing; Tong, Xu; Guo, Li; Wang, Yilong

2016-01-01

A case-mix adjustment model has been developed and externally validated, demonstrating promise. However, the model has not been thoroughly tested among populations in China. In our study, we evaluated the performance of the model in Chinese patients with acute stroke. The case-mix adjustment model A includes items on age, presence of atrial fibrillation on admission, National Institutes of Health Stroke Severity Scale (NIHSS) score on admission, and stroke type. Model B is similar to Model A but includes only the consciousness component of the NIHSS score. Both model A and B were evaluated to predict 30-day mortality rates in 13,948 patients with acute stroke from the China National Stroke Registry. The discrimination of the models was quantified by c-statistic. Calibration was assessed using Pearson's correlation coefficient. The c-statistic of model A in our external validation cohort was 0.80 (95% confidence interval, 0.79-0.82), and the c-statistic of model B was 0.82 (95% confidence interval, 0.81-0.84). Excellent calibration was reported in the two models with Pearson's correlation coefficient (0.892 for model A, p<0.001; 0.927 for model B, p = 0.008). The case-mix adjustment model could be used to effectively predict 30-day mortality rates in Chinese patients with acute stroke.

Model averaging and muddled multimodel inferences.

PubMed

Cade, Brian S

2015-09-01

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse (Centrocercus urophasianus) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

Fish: A New Computer Program for Friendly Introductory Statistics Help

ERIC Educational Resources Information Center

Brooks, Gordon P.; Raffle, Holly

2005-01-01

All introductory statistics students must master certain basic descriptive statistics, including means, standard deviations and correlations. Students must also gain insight into such complex concepts as the central limit theorem and standard error. This article introduces and describes the Friendly Introductory Statistics Help (FISH) computer…

Statistical inference with quantum measurements: methodologies for nitrogen vacancy centers in diamond

NASA Astrophysics Data System (ADS)

Hincks, Ian; Granade, Christopher; Cory, David G.

2018-01-01

The analysis of photon count data from the standard nitrogen vacancy (NV) measurement process is treated as a statistical inference problem. This has applications toward gaining better and more rigorous error bars for tasks such as parameter estimation (e.g. magnetometry), tomography, and randomized benchmarking. We start by providing a summary of the standard phenomenological model of the NV optical process in terms of Lindblad jump operators. This model is used to derive random variables describing emitted photons during measurement, to which finite visibility, dark counts, and imperfect state preparation are added. NV spin-state measurement is then stated as an abstract statistical inference problem consisting of an underlying biased coin obstructed by three Poisson rates. Relevant frequentist and Bayesian estimators are provided, discussed, and quantitatively compared. We show numerically that the risk of the maximum likelihood estimator is well approximated by the Cramér-Rao bound, for which we provide a simple formula. Of the estimators, we in particular promote the Bayes estimator, owing to its slightly better risk performance, and straightforward error propagation into more complex experiments. This is illustrated on experimental data, where quantum Hamiltonian learning is performed and cross-validated in a fully Bayesian setting, and compared to a more traditional weighted least squares fit.

Imprints of non-standard dark energy and dark matter models on the 21cm intensity map power spectrum

NASA Astrophysics Data System (ADS)

Carucci, Isabella P.; Corasaniti, Pier-Stefano; Viel, Matteo

2017-12-01

We study the imprint of non-standard dark energy (DE) and dark matter (DM) models on the 21cm intensity map power spectra from high-redshift neutral hydrogen (HI) gas. To this purpose we use halo catalogs from N-body simulations of dynamical DE models and DM scenarios which are as successful as the standard Cold Dark Matter model with Cosmological Constant (ΛCDM) at interpreting available cosmological observations. We limit our analysis to halo catalogs at redshift z=1 and 2.3 which are common to all simulations. For each catalog we model the HI distribution by using a simple prescription to associate the HI gas mass to N-body halos. We find that the DE models leave a distinct signature on the HI spectra across a wide range of scales, which correlates with differences in the halo mass function and the onset of the non-linear regime of clustering. In the case of the non-standard DM model significant differences of the HI spectra with respect to the ΛCDM model only arise from the suppressed abundance of low mass halos. These cosmological model dependent features also appear in the 21cm spectra. In particular, we find that future SKA measurements can distinguish the imprints of DE and DM models at high statistical significance.

On statistical inference in time series analysis of the evolution of road safety.

PubMed

Commandeur, Jacques J F; Bijleveld, Frits D; Bergel-Hayat, Ruth; Antoniou, Constantinos; Yannis, George; Papadimitriou, Eleonora

2013-11-01

Data collected for building a road safety observatory usually include observations made sequentially through time. Examples of such data, called time series data, include annual (or monthly) number of road traffic accidents, traffic fatalities or vehicle kilometers driven in a country, as well as the corresponding values of safety performance indicators (e.g., data on speeding, seat belt use, alcohol use, etc.). Some commonly used statistical techniques imply assumptions that are often violated by the special properties of time series data, namely serial dependency among disturbances associated with the observations. The first objective of this paper is to demonstrate the impact of such violations to the applicability of standard methods of statistical inference, which leads to an under or overestimation of the standard error and consequently may produce erroneous inferences. Moreover, having established the adverse consequences of ignoring serial dependency issues, the paper aims to describe rigorous statistical techniques used to overcome them. In particular, appropriate time series analysis techniques of varying complexity are employed to describe the development over time, relating the accident-occurrences to explanatory factors such as exposure measures or safety performance indicators, and forecasting the development into the near future. Traditional regression models (whether they are linear, generalized linear or nonlinear) are shown not to naturally capture the inherent dependencies in time series data. Dedicated time series analysis techniques, such as the ARMA-type and DRAG approaches are discussed next, followed by structural time series models, which are a subclass of state space methods. The paper concludes with general recommendations and practice guidelines for the use of time series models in road safety research. Copyright © 2012 Elsevier Ltd. All rights reserved.

Anatomy of the Higgs fits: A first guide to statistical treatments of the theoretical uncertainties

NASA Astrophysics Data System (ADS)

Fichet, Sylvain; Moreau, Grégory

2016-04-01

The studies of the Higgs boson couplings based on the recent and upcoming LHC data open up a new window on physics beyond the Standard Model. In this paper, we propose a statistical guide to the consistent treatment of the theoretical uncertainties entering the Higgs rate fits. Both the Bayesian and frequentist approaches are systematically analysed in a unified formalism. We present analytical expressions for the marginal likelihoods, useful to implement simultaneously the experimental and theoretical uncertainties. We review the various origins of the theoretical errors (QCD, EFT, PDF, production mode contamination…). All these individual uncertainties are thoroughly combined with the help of moment-based considerations. The theoretical correlations among Higgs detection channels appear to affect the location and size of the best-fit regions in the space of Higgs couplings. We discuss the recurrent question of the shape of the prior distributions for the individual theoretical errors and find that a nearly Gaussian prior arises from the error combinations. We also develop the bias approach, which is an alternative to marginalisation providing more conservative results. The statistical framework to apply the bias principle is introduced and two realisations of the bias are proposed. Finally, depending on the statistical treatment, the Standard Model prediction for the Higgs signal strengths is found to lie within either the 68% or 95% confidence level region obtained from the latest analyses of the 7 and 8 TeV LHC datasets.

Markov switching multinomial logit model: An application to accident-injury severities.

PubMed

Malyshkina, Nataliya V; Mannering, Fred L

2009-07-01

In this study, two-state Markov switching multinomial logit models are proposed for statistical modeling of accident-injury severities. These models assume Markov switching over time between two unobserved states of roadway safety as a means of accounting for potential unobserved heterogeneity. The states are distinct in the sense that in different states accident-severity outcomes are generated by separate multinomial logit processes. To demonstrate the applicability of the approach, two-state Markov switching multinomial logit models are estimated for severity outcomes of accidents occurring on Indiana roads over a four-year time period. Bayesian inference methods and Markov Chain Monte Carlo (MCMC) simulations are used for model estimation. The estimated Markov switching models result in a superior statistical fit relative to the standard (single-state) multinomial logit models for a number of roadway classes and accident types. It is found that the more frequent state of roadway safety is correlated with better weather conditions and that the less frequent state is correlated with adverse weather conditions.

GPU-computing in econophysics and statistical physics

NASA Astrophysics Data System (ADS)

Preis, T.

2011-03-01

A recent trend in computer science and related fields is general purpose computing on graphics processing units (GPUs), which can yield impressive performance. With multiple cores connected by high memory bandwidth, today's GPUs offer resources for non-graphics parallel processing. This article provides a brief introduction into the field of GPU computing and includes examples. In particular computationally expensive analyses employed in financial market context are coded on a graphics card architecture which leads to a significant reduction of computing time. In order to demonstrate the wide range of possible applications, a standard model in statistical physics - the Ising model - is ported to a graphics card architecture as well, resulting in large speedup values.

Statistical, economic and other tools for assessing natural aggregate

USGS Publications Warehouse

Bliss, J.D.; Moyle, P.R.; Bolm, K.S.

2003-01-01

Quantitative aggregate resource assessment provides resource estimates useful for explorationists, land managers and those who make decisions about land allocation, which may have long-term implications concerning cost and the availability of aggregate resources. Aggregate assessment needs to be systematic and consistent, yet flexible enough to allow updating without invalidating other parts of the assessment. Evaluators need to use standard or consistent aggregate classification and statistic distributions or, in other words, models with geological, geotechnical and economic variables or interrelationships between these variables. These models can be used with subjective estimates, if needed, to estimate how much aggregate may be present in a region or country using distributions generated by Monte Carlo computer simulations.

Competing risks models and time-dependent covariates

PubMed Central

Barnett, Adrian; Graves, Nick

2008-01-01

New statistical models for analysing survival data in an intensive care unit context have recently been developed. Two models that offer significant advantages over standard survival analyses are competing risks models and multistate models. Wolkewitz and colleagues used a competing risks model to examine survival times for nosocomial pneumonia and mortality. Their model was able to incorporate time-dependent covariates and so examine how risk factors that changed with time affected the chances of infection or death. We briefly explain how an alternative modelling technique (using logistic regression) can more fully exploit time-dependent covariates for this type of data. PMID:18423067

FAST TRACK COMMUNICATION: Freezing and extreme-value statistics in a random energy model with logarithmically correlated potential

NASA Astrophysics Data System (ADS)

Fyodorov, Yan V.; Bouchaud, Jean-Philippe

2008-09-01

We investigate some implications of the freezing scenario proposed by Carpentier and Le Doussal (CLD) for a random energy model (REM) with logarithmically correlated random potential. We introduce a particular (circular) variant of the model, and show that the integer moments of the partition function in the high-temperature phase are given by the well-known Dyson Coulomb gas integrals. The CLD freezing scenario allows one to use those moments for extracting the distribution of the free energy in both high- and low-temperature phases. In particular, it yields the full distribution of the minimal value in the potential sequence. This provides an explicit new class of extreme-value statistics for strongly correlated variables, manifestly different from the standard Gumbel class.

Vehicle track segmentation using higher order random fields

DOE PAGES

Quach, Tu -Thach

2017-01-09

Here, we present an approach to segment vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times. The approach uses multiscale higher order random field models to capture track statistics, such as curvatures and their parallel nature, that are not currently utilized in existing methods. These statistics are encoded as 3-by-3 patterns at different scales. The model can complete disconnected tracks often caused by sensor noise and various environmental effects. Coupling the model with a simple classifier, our approach is effective at segmenting salient tracks. We improve the F-measure onmore » a standard vehicle track data set to 0.963, up from 0.897 obtained by the current state-of-the-art method.« less

Integrated data management for clinical studies: automatic transformation of data models with semantic annotations for principal investigators, data managers and statisticians.

PubMed

Dugas, Martin; Dugas-Breit, Susanne

2014-01-01

Design, execution and analysis of clinical studies involves several stakeholders with different professional backgrounds. Typically, principle investigators are familiar with standard office tools, data managers apply electronic data capture (EDC) systems and statisticians work with statistics software. Case report forms (CRFs) specify the data model of study subjects, evolve over time and consist of hundreds to thousands of data items per study. To avoid erroneous manual transformation work, a converting tool for different representations of study data models was designed. It can convert between office format, EDC and statistics format. In addition, it supports semantic annotations, which enable precise definitions for data items. A reference implementation is available as open source package ODMconverter at http://cran.r-project.org.

Vehicle track segmentation using higher order random fields

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

Quach, Tu -Thach

Here, we present an approach to segment vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images taken at different times. The approach uses multiscale higher order random field models to capture track statistics, such as curvatures and their parallel nature, that are not currently utilized in existing methods. These statistics are encoded as 3-by-3 patterns at different scales. The model can complete disconnected tracks often caused by sensor noise and various environmental effects. Coupling the model with a simple classifier, our approach is effective at segmenting salient tracks. We improve the F-measure onmore » a standard vehicle track data set to 0.963, up from 0.897 obtained by the current state-of-the-art method.« less

Social Inequality and Labor Force Participation.

ERIC Educational Resources Information Center

King, Jonathan

The labor force participation rates of whites, blacks, and Spanish-Americans, grouped by sex, are explained in a linear regression model fitted with 1970 U. S. Census data on Standard Metropolitan Statistical Area (SMSA). The explanatory variables are: average age, average years of education, vocational training rate, disabled rate, unemployment…

Multi-Parameter Linear Least-Squares Fitting to Poisson Data One Count at a Time

NASA Technical Reports Server (NTRS)

Wheaton, W.; Dunklee, A.; Jacobson, A.; Ling, J.; Mahoney, W.; Radocinski, R.

1993-01-01

A standard problem in gamma-ray astronomy data analysis is the decomposition of a set of observed counts, described by Poisson statistics, according to a given multi-component linear model, with underlying physical count rates or fluxes which are to be estimated from the data.

Standard Operating Procedures for Collecting Data from Local Education Agencies.

ERIC Educational Resources Information Center

McElreath, Nancy R., Ed.

A systematic approach to planning and presenting the data collection activities of a State Department of Education is described. The Information Communication System, a model communication system used by the state of New Jersey, conveys narrative and statistical information relating to a school district's students, teachers, finances, facilities…

Tests of Mediation: Paradoxical Decline in Statistical Power as a Function of Mediator Collinearity

ERIC Educational Resources Information Center

Beasley, T. Mark

2014-01-01

Increasing the correlation between the independent variable and the mediator ("a" coefficient) increases the effect size ("ab") for mediation analysis; however, increasing a by definition increases collinearity in mediation models. As a result, the standard error of product tests increase. The variance inflation caused by…

An entropy-based statistic for genomewide association studies.

PubMed

Zhao, Jinying; Boerwinkle, Eric; Xiong, Momiao

2005-07-01

Efficient genotyping methods and the availability of a large collection of single-nucleotide polymorphisms provide valuable tools for genetic studies of human disease. The standard chi2 statistic for case-control studies, which uses a linear function of allele frequencies, has limited power when the number of marker loci is large. We introduce a novel test statistic for genetic association studies that uses Shannon entropy and a nonlinear function of allele frequencies to amplify the differences in allele and haplotype frequencies to maintain statistical power with large numbers of marker loci. We investigate the relationship between the entropy-based test statistic and the standard chi2 statistic and show that, in most cases, the power of the entropy-based statistic is greater than that of the standard chi2 statistic. The distribution of the entropy-based statistic and the type I error rates are validated using simulation studies. Finally, we apply the new entropy-based test statistic to two real data sets, one for the COMT gene and schizophrenia and one for the MMP-2 gene and esophageal carcinoma, to evaluate the performance of the new method for genetic association studies. The results show that the entropy-based statistic obtained smaller P values than did the standard chi2 statistic.

The joint space-time statistics of macroweather precipitation, space-time statistical factorization and macroweather models.

PubMed

Lovejoy, S; de Lima, M I P

2015-07-01

Over the range of time scales from about 10 days to 30-100 years, in addition to the familiar weather and climate regimes, there is an intermediate "macroweather" regime characterized by negative temporal fluctuation exponents: implying that fluctuations tend to cancel each other out so that averages tend to converge. We show theoretically and numerically that macroweather precipitation can be modeled by a stochastic weather-climate model (the Climate Extended Fractionally Integrated Flux, model, CEFIF) first proposed for macroweather temperatures and we show numerically that a four parameter space-time CEFIF model can approximately reproduce eight or so empirical space-time exponents. In spite of this success, CEFIF is theoretically and numerically difficult to manage. We therefore propose a simplified stochastic model in which the temporal behavior is modeled as a fractional Gaussian noise but the spatial behaviour as a multifractal (climate) cascade: a spatial extension of the recently introduced ScaLIng Macroweather Model, SLIMM. Both the CEFIF and this spatial SLIMM model have a property often implicitly assumed by climatologists that climate statistics can be "homogenized" by normalizing them with the standard deviation of the anomalies. Physically, it means that the spatial macroweather variability corresponds to different climate zones that multiplicatively modulate the local, temporal statistics. This simplified macroweather model provides a framework for macroweather forecasting that exploits the system's long range memory and spatial correlations; for it, the forecasting problem has been solved. We test this factorization property and the model with the help of three centennial, global scale precipitation products that we analyze jointly in space and in time.

77 FR 34044 - National Committee on Vital and Health Statistics: Meeting Standards Subcommittee

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-08

... DEPARTMENT OF HEALTH AND HUMAN SERVICES National Committee on Vital and Health Statistics: Meeting... Health Statistics (NCVHS); Subcommittee on Standards. Time and Date: June 20, 2012, 9 a.m.-5 p.m. EST..., Executive Secretary, NCVHS, National Center for Health Statistics, Centers for Disease Control and...

On the value of incorporating spatial statistics in large-scale geophysical inversions: the SABRe case

NASA Astrophysics Data System (ADS)

Kokkinaki, A.; Sleep, B. E.; Chambers, J. E.; Cirpka, O. A.; Nowak, W.

2010-12-01

Electrical Resistance Tomography (ERT) is a popular method for investigating subsurface heterogeneity. The method relies on measuring electrical potential differences and obtaining, through inverse modeling, the underlying electrical conductivity field, which can be related to hydraulic conductivities. The quality of site characterization strongly depends on the utilized inversion technique. Standard ERT inversion methods, though highly computationally efficient, do not consider spatial correlation of soil properties; as a result, they often underestimate the spatial variability observed in earth materials, thereby producing unrealistic subsurface models. Also, these methods do not quantify the uncertainty of the estimated properties, thus limiting their use in subsequent investigations. Geostatistical inverse methods can be used to overcome both these limitations; however, they are computationally expensive, which has hindered their wide use in practice. In this work, we compare a standard Gauss-Newton smoothness constrained least squares inversion method against the quasi-linear geostatistical approach using the three-dimensional ERT dataset of the SABRe (Source Area Bioremediation) project. The two methods are evaluated for their ability to: a) produce physically realistic electrical conductivity fields that agree with the wide range of data available for the SABRe site while being computationally efficient, and b) provide information on the spatial statistics of other parameters of interest, such as hydraulic conductivity. To explore the trade-off between inversion quality and computational efficiency, we also employ a 2.5-D forward model with corrections for boundary conditions and source singularities. The 2.5-D model accelerates the 3-D geostatistical inversion method. New adjoint equations are developed for the 2.5-D forward model for the efficient calculation of sensitivities. Our work shows that spatial statistics can be incorporated in large-scale ERT inversions to improve the inversion results without making them computationally prohibitive.

The statistical analysis of global climate change studies

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

Hardin, J.W.

1992-01-01

The focus of this work is to contribute to the enhancement of the relationship between climatologists and statisticians. The analysis of global change data has been underway for many years by atmospheric scientists. Much of this analysis includes a heavy reliance on statistics and statistical inference. Some specific climatological analyses are presented and the dependence on statistics is documented before the analysis is undertaken. The first problem presented involves the fluctuation-dissipation theorem and its application to global climate models. This problem has a sound theoretical niche in the literature of both climate modeling and physics, but a statistical analysis inmore » which the data is obtained from the model to show graphically the relationship has not been undertaken. It is under this motivation that the author presents this problem. A second problem concerning the standard errors in estimating global temperatures is purely statistical in nature although very little materials exists for sampling on such a frame. This problem not only has climatological and statistical ramifications, but political ones as well. It is planned to use these results in a further analysis of global warming using actual data collected on the earth. In order to simplify the analysis of these problems, the development of a computer program, MISHA, is presented. This interactive program contains many of the routines, functions, graphics, and map projections needed by the climatologist in order to effectively enter the arena of data visualization.« less

SOCR: Statistics Online Computational Resource

PubMed Central

Dinov, Ivo D.

2011-01-01

The need for hands-on computer laboratory experience in undergraduate and graduate statistics education has been firmly established in the past decade. As a result a number of attempts have been undertaken to develop novel approaches for problem-driven statistical thinking, data analysis and result interpretation. In this paper we describe an integrated educational web-based framework for: interactive distribution modeling, virtual online probability experimentation, statistical data analysis, visualization and integration. Following years of experience in statistical teaching at all college levels using established licensed statistical software packages, like STATA, S-PLUS, R, SPSS, SAS, Systat, etc., we have attempted to engineer a new statistics education environment, the Statistics Online Computational Resource (SOCR). This resource performs many of the standard types of statistical analysis, much like other classical tools. In addition, it is designed in a plug-in object-oriented architecture and is completely platform independent, web-based, interactive, extensible and secure. Over the past 4 years we have tested, fine-tuned and reanalyzed the SOCR framework in many of our undergraduate and graduate probability and statistics courses and have evidence that SOCR resources build student’s intuition and enhance their learning. PMID:21451741

Ferrets as Models for Influenza Virus Transmission Studies and Pandemic Risk Assessments

PubMed Central

Barclay, Wendy; Barr, Ian; Fouchier, Ron A.M.; Matsuyama, Ryota; Nishiura, Hiroshi; Peiris, Malik; Russell, Charles J.; Subbarao, Kanta; Zhu, Huachen

2018-01-01

The ferret transmission model is extensively used to assess the pandemic potential of emerging influenza viruses, yet experimental conditions and reported results vary among laboratories. Such variation can be a critical consideration when contextualizing results from independent risk-assessment studies of novel and emerging influenza viruses. To streamline interpretation of data generated in different laboratories, we provide a consensus on experimental parameters that define risk-assessment experiments of influenza virus transmissibility, including disclosure of variables known or suspected to contribute to experimental variability in this model, and advocate adoption of more standardized practices. We also discuss current limitations of the ferret transmission model and highlight continued refinements and advances to this model ongoing in laboratories. Understanding, disclosing, and standardizing the critical parameters of ferret transmission studies will improve the comparability and reproducibility of pandemic influenza risk assessment and increase the statistical power and, perhaps, accuracy of this model. PMID:29774862

Characterizing Dark Energy Through Supernovae

NASA Astrophysics Data System (ADS)

Davis, Tamara M.; Parkinson, David

Type Ia supernovae are a powerful cosmological probe that gave the first strong evidence that the expansion of the universe is accelerating. Here we provide an overview of how supernovae can go further to reveal information about what is causing the acceleration, be it dark energy or some modification to our laws of gravity. We first review the methods of statistical inference that are commonly used, making a point of separating parameter estimation from model selection. We then summarize the many different approaches used to explain or test the acceleration, including parametric models (like the standard model, ΛCDM), nonparametric models, dark fluid models such as quintessence, and extensions to standard gravity. Finally, we also show how supernova data can be used beyond the Hubble diagram, to give information on gravitational lensing and peculiar velocities that can be used to distinguish between models that predict the same expansion history.

Bayesian modelling of lung function data from multiple-breath washout tests.

PubMed

Mahar, Robert K; Carlin, John B; Ranganathan, Sarath; Ponsonby, Anne-Louise; Vuillermin, Peter; Vukcevic, Damjan

2018-05-30

Paediatric respiratory researchers have widely adopted the multiple-breath washout (MBW) test because it allows assessment of lung function in unsedated infants and is well suited to longitudinal studies of lung development and disease. However, a substantial proportion of MBW tests in infants fail current acceptability criteria. We hypothesised that a model-based approach to analysing the data, in place of traditional simple empirical summaries, would enable more efficient use of these tests. We therefore developed a novel statistical model for infant MBW data and applied it to 1197 tests from 432 individuals from a large birth cohort study. We focus on Bayesian estimation of the lung clearance index, the most commonly used summary of lung function from MBW tests. Our results show that the model provides an excellent fit to the data and shed further light on statistical properties of the standard empirical approach. Furthermore, the modelling approach enables the lung clearance index to be estimated by using tests with different degrees of completeness, something not possible with the standard approach. Our model therefore allows previously unused data to be used rather than discarded, as well as routine use of shorter tests without significant loss of precision. Beyond our specific application, our work illustrates a number of important aspects of Bayesian modelling in practice, such as the importance of hierarchical specifications to account for repeated measurements and the value of model checking via posterior predictive distributions. Copyright © 2018 John Wiley & Sons, Ltd.

A comparison of two experimental design approaches in applying conjoint analysis in patient-centered outcomes research: a randomized trial.

PubMed

Kinter, Elizabeth T; Prior, Thomas J; Carswell, Christopher I; Bridges, John F P

2012-01-01

While the application of conjoint analysis and discrete-choice experiments in health are now widely accepted, a healthy debate exists around competing approaches to experimental design. There remains, however, a paucity of experimental evidence comparing competing design approaches and their impact on the application of these methods in patient-centered outcomes research. Our objectives were to directly compare the choice-model parameters and predictions of an orthogonal and a D-efficient experimental design using a randomized trial (i.e., an experiment on experiments) within an application of conjoint analysis studying patient-centered outcomes among outpatients diagnosed with schizophrenia in Germany. Outpatients diagnosed with schizophrenia were surveyed and randomized to receive choice tasks developed using either an orthogonal or a D-efficient experimental design. The choice tasks elicited judgments from the respondents as to which of two patient profiles (varying across seven outcomes and process attributes) was preferable from their own perspective. The results from the two survey designs were analyzed using the multinomial logit model, and the resulting parameter estimates and their robust standard errors were compared across the two arms of the study (i.e., the orthogonal and D-efficient designs). The predictive performances of the two resulting models were also compared by computing their percentage of survey responses classified correctly, and the potential for variation in scale between the two designs of the experiments was tested statistically and explored graphically. The results of the two models were statistically identical. No difference was found using an overall chi-squared test of equality for the seven parameters (p = 0.69) or via uncorrected pairwise comparisons of the parameter estimates (p-values ranged from 0.30 to 0.98). The D-efficient design resulted in directionally smaller standard errors for six of the seven parameters, of which only two were statistically significant, and no differences were found in the observed D-efficiencies of their standard errors (p = 0.62). The D-efficient design resulted in poorer predictive performance, but this was not significant (p = 0.73); there was some evidence that the parameters of the D-efficient design were biased marginally towards the null. While no statistical difference in scale was detected between the two designs (p = 0.74), the D-efficient design had a higher relative scale (1.06). This could be observed when the parameters were explored graphically, as the D-efficient parameters were lower. Our results indicate that orthogonal and D-efficient experimental designs have produced results that are statistically equivalent. This said, we have identified several qualitative findings that speak to the potential differences in these results that may have been statistically identified in a larger sample. While more comparative studies focused on the statistical efficiency of competing design strategies are needed, a more pressing research problem is to document the impact the experimental design has on respondent efficiency.

Modeling the Test-Retest Statistics of a Localization Experiment in the Full Horizontal Plane.

PubMed

Morsnowski, André; Maune, Steffen

2016-10-01

Two approaches to model the test-retest statistics of a localization experiment basing on Gaussian distribution and on surrogate data are introduced. Their efficiency is investigated using different measures describing directional hearing ability. A localization experiment in the full horizontal plane is a challenging task for hearing impaired patients. In clinical routine, we use this experiment to evaluate the progress of our cochlear implant (CI) recipients. Listening and time effort limit the reproducibility. The localization experiment consists of a 12 loudspeaker circle, placed in an anechoic room, a "camera silens". In darkness, HSM sentences are presented at 65 dB pseudo-erratically from all 12 directions with five repetitions. This experiment is modeled by a set of Gaussian distributions with different standard deviations added to a perfect estimator, as well as by surrogate data. Five repetitions per direction are used to produce surrogate data distributions for the sensation directions. To investigate the statistics, we retrospectively use the data of 33 CI patients with 92 pairs of test-retest-measurements from the same day. The first model does not take inversions into account, (i.e., permutations of the direction from back to front and vice versa are not considered), although they are common for hearing impaired persons particularly in the rear hemisphere. The second model considers these inversions but does not work with all measures. The introduced models successfully describe test-retest statistics of directional hearing. However, since their applications on the investigated measures perform differently no general recommendation can be provided. The presented test-retest statistics enable pair test comparisons for localization experiments.

The estimation of the measurement results with using statistical methods

NASA Astrophysics Data System (ADS)

Velychko, O.; Gordiyenko, T.

2015-02-01

The row of international standards and guides describe various statistical methods that apply for a management, control and improvement of processes with the purpose of realization of analysis of the technical measurement results. The analysis of international standards and guides on statistical methods estimation of the measurement results recommendations for those applications in laboratories is described. For realization of analysis of standards and guides the cause-and-effect Ishikawa diagrams concerting to application of statistical methods for estimation of the measurement results are constructed.

The Effects of Local Economic Conditions on Navy Enlistments.

DTIC Science & Technology

1980-03-18

Standard Metropolitan Statistical Area (SMSA) as the basic economic unit, cross-sectional regression models were constructed for enlistment rate, recruiter...to eligible population suggesting that a cheaper alternative to raising mili- tary wages would be to increase the number of recruiters. Arima (1978...is faced with a number of cri- teria that must be satisfied by an acceptable test variable. As with other variables included in the model , economic

An Econometric Analysis of the Unemployment Insurance System in a Local Urban Labor Market. Final Report for September 1, 1973--September 30, 1974.

ERIC Educational Resources Information Center

Marston, Stephen Tilney

The study derives a model of the unemployment insurance (UI) system and its relationship to the labor market, estimates it with data from the Detroit Standard Metropolitan Statistical Area, and evaluates its potential use to forecast UI benefit amounts, UI insured unemployment, and UI exhaustions. It further uses the model to analyze policy issues…

Class Evolution Tree: A Graphical Tool to Support Decisions on the Number of Classes in Exploratory Categorical Latent Variable Modeling for Rehabilitation Research

ERIC Educational Resources Information Center

Kriston, Levente; Melchior, Hanne; Hergert, Anika; Bergelt, Corinna; Watzke, Birgit; Schulz, Holger; von Wolff, Alessa

2011-01-01

The aim of our study was to develop a graphical tool that can be used in addition to standard statistical criteria to support decisions on the number of classes in explorative categorical latent variable modeling for rehabilitation research. Data from two rehabilitation research projects were used. In the first study, a latent profile analysis was…

2010 Anthropometric Survey of U.S. Marine Corps Personnel: Methods and Summary Statistics

DTIC Science & Technology

2013-06-01

models for the ergonomic design of working environments. Today, the entire production chain for a piece of clothing, beginning with the design and...Corps 382 crewstations and workstations. Digital models are increasingly used in the design process for seated and standing workstations, as well...International Standards for Ergonomic Design : These dimensions are useful for comparing data sets between nations, and are measured according to

A combination of routine blood analytes predicts fitness decrement in elderly endurance athletes.

PubMed

Haslacher, Helmuth; Ratzinger, Franz; Perkmann, Thomas; Batmyagmar, Delgerdalai; Nistler, Sonja; Scherzer, Thomas M; Ponocny-Seliger, Elisabeth; Pilger, Alexander; Gerner, Marlene; Scheichenberger, Vanessa; Kundi, Michael; Endler, Georg; Wagner, Oswald F; Winker, Robert

2017-01-01

Endurance sports are enjoying greater popularity, particularly among new target groups such as the elderly. Predictors of future physical capacities providing a basis for training adaptations are in high demand. We therefore aimed to estimate the future physical performance of elderly marathoners (runners/bicyclists) using a set of easily accessible standard laboratory parameters. To this end, 47 elderly marathon athletes underwent physical examinations including bicycle ergometry and a blood draw at baseline and after a three-year follow-up period. In order to compile a statistical model containing baseline laboratory results allowing prediction of follow-up ergometry performance, the cohort was subgrouped into a model training (n = 25) and a test sample (n = 22). The model containing significant predictors in univariate analysis (alanine aminotransferase, urea, folic acid, myeloperoxidase and total cholesterol) presented with high statistical significance and excellent goodness of fit (R2 = 0.789, ROC-AUC = 0.951±0.050) in the model training sample and was validated in the test sample (ROC-AUC = 0.786±0.098). Our results suggest that standard laboratory parameters could be particularly useful for predicting future physical capacity in elderly marathoners. It hence merits further research whether these conclusions can be translated to other disciplines or age groups.

A combination of routine blood analytes predicts fitness decrement in elderly endurance athletes

PubMed Central

Ratzinger, Franz; Perkmann, Thomas; Batmyagmar, Delgerdalai; Nistler, Sonja; Scherzer, Thomas M.; Ponocny-Seliger, Elisabeth; Pilger, Alexander; Gerner, Marlene; Scheichenberger, Vanessa; Kundi, Michael; Endler, Georg; Wagner, Oswald F.; Winker, Robert

2017-01-01

Endurance sports are enjoying greater popularity, particularly among new target groups such as the elderly. Predictors of future physical capacities providing a basis for training adaptations are in high demand. We therefore aimed to estimate the future physical performance of elderly marathoners (runners/bicyclists) using a set of easily accessible standard laboratory parameters. To this end, 47 elderly marathon athletes underwent physical examinations including bicycle ergometry and a blood draw at baseline and after a three-year follow-up period. In order to compile a statistical model containing baseline laboratory results allowing prediction of follow-up ergometry performance, the cohort was subgrouped into a model training (n = 25) and a test sample (n = 22). The model containing significant predictors in univariate analysis (alanine aminotransferase, urea, folic acid, myeloperoxidase and total cholesterol) presented with high statistical significance and excellent goodness of fit (R2 = 0.789, ROC-AUC = 0.951±0.050) in the model training sample and was validated in the test sample (ROC-AUC = 0.786±0.098). Our results suggest that standard laboratory parameters could be particularly useful for predicting future physical capacity in elderly marathoners. It hence merits further research whether these conclusions can be translated to other disciplines or age groups. PMID:28475643

[Application of a mathematical algorithm for the detection of electroneuromyographic results in the pathogenesis study of facial dyskinesia].

PubMed

Gribova, N P; Iudel'son, Ia B; Golubev, V L; Abramenkova, I V

2003-01-01

To carry out a differential diagnosis of two facial dyskinesia (FD) models--facial hemispasm (FH) and facial paraspasm (FP), a combined program of electroneuromyographic (ENMG) examination has been created, using statistical analyses, including that for objects identification based on hybrid neural network with the application of adaptive fuzzy logic method and standard statistics programs (Wilcoxon, Student statistics). In FH, a lesion of peripheral facial neuromotor apparatus with augmentation of functions of inter-neurons in segmental and upper segmental stem levels predominated. In FP, primary afferent strengthening in mimic muscles was accompanied by increased motor neurons activity and reciprocal augmentation of inter-neurons, inhibiting motor portion of V pair. Mathematical algorithm for ENMG results recognition worked out in the study provides a precise differentiation of two FD models and opens possibilities for differential diagnosis of other facial motor disorders.

The application of the statistical classifying models for signal evaluation of the gas sensors analyzing mold contamination of the building materials

NASA Astrophysics Data System (ADS)

Majerek, Dariusz; Guz, Łukasz; Suchorab, Zbigniew; Łagód, Grzegorz; Sobczuk, Henryk

2017-07-01

Mold that develops on moistened building barriers is a major cause of the Sick Building Syndrome (SBS). Fungal contamination is normally evaluated using standard biological methods which are time-consuming and require a lot of manual labor. Fungi emit Volatile Organic Compounds (VOC) that can be detected in the indoor air using several techniques of detection e.g. chromatography. VOCs can be also detected using gas sensors arrays. All array sensors generate particular voltage signals that ought to be analyzed using properly selected statistical methods of interpretation. This work is focused on the attempt to apply statistical classifying models in evaluation of signals from gas sensors matrix to analyze the air sampled from the headspace of various types of the building materials at different level of contamination but also clean reference materials.

A phylogenetic transform enhances analysis of compositional microbiota data.

PubMed

Silverman, Justin D; Washburne, Alex D; Mukherjee, Sayan; David, Lawrence A

2017-02-15

Surveys of microbial communities (microbiota), typically measured as relative abundance of species, have illustrated the importance of these communities in human health and disease. Yet, statistical artifacts commonly plague the analysis of relative abundance data. Here, we introduce the PhILR transform, which incorporates microbial evolutionary models with the isometric log-ratio transform to allow off-the-shelf statistical tools to be safely applied to microbiota surveys. We demonstrate that analyses of community-level structure can be applied to PhILR transformed data with performance on benchmarks rivaling or surpassing standard tools. Additionally, by decomposing distance in the PhILR transformed space, we identified neighboring clades that may have adapted to distinct human body sites. Decomposing variance revealed that covariation of bacterial clades within human body sites increases with phylogenetic relatedness. Together, these findings illustrate how the PhILR transform combines statistical and phylogenetic models to overcome compositional data challenges and enable evolutionary insights relevant to microbial communities.

The use of analysis of variance procedures in biological studies

USGS Publications Warehouse

Williams, B.K.

1987-01-01

The analysis of variance (ANOVA) is widely used in biological studies, yet there remains considerable confusion among researchers about the interpretation of hypotheses being tested. Ambiguities arise when statistical designs are unbalanced, and in particular when not all combinations of design factors are represented in the data. This paper clarifies the relationship among hypothesis testing, statistical modelling and computing procedures in ANOVA for unbalanced data. A simple two-factor fixed effects design is used to illustrate three common parametrizations for ANOVA models, and some associations among these parametrizations are developed. Biologically meaningful hypotheses for main effects and interactions are given in terms of each parametrization, and procedures for testing the hypotheses are described. The standard statistical computing procedures in ANOVA are given along with their corresponding hypotheses. Throughout the development unbalanced designs are assumed and attention is given to problems that arise with missing cells.

A Statistical Method for Synthesizing Mediation Analyses Using the Product of Coefficient Approach Across Multiple Trials

PubMed Central

Huang, Shi; MacKinnon, David P.; Perrino, Tatiana; Gallo, Carlos; Cruden, Gracelyn; Brown, C Hendricks

2016-01-01

Mediation analysis often requires larger sample sizes than main effect analysis to achieve the same statistical power. Combining results across similar trials may be the only practical option for increasing statistical power for mediation analysis in some situations. In this paper, we propose a method to estimate: 1) marginal means for mediation path a, the relation of the independent variable to the mediator; 2) marginal means for path b, the relation of the mediator to the outcome, across multiple trials; and 3) the between-trial level variance-covariance matrix based on a bivariate normal distribution. We present the statistical theory and an R computer program to combine regression coefficients from multiple trials to estimate a combined mediated effect and confidence interval under a random effects model. Values of coefficients a and b, along with their standard errors from each trial are the input for the method. This marginal likelihood based approach with Monte Carlo confidence intervals provides more accurate inference than the standard meta-analytic approach. We discuss computational issues, apply the method to two real-data examples and make recommendations for the use of the method in different settings. PMID:28239330

Deep space network software cost estimation model

NASA Technical Reports Server (NTRS)

Tausworthe, R. C.

1981-01-01

A parametric software cost estimation model prepared for Deep Space Network (DSN) Data Systems implementation tasks is presented. The resource estimation model incorporates principles and data from a number of existing models. The model calibrates task magnitude and difficulty, development environment, and software technology effects through prompted responses to a set of approximately 50 questions. Parameters in the model are adjusted to fit DSN software life cycle statistics. The estimation model output scales a standard DSN Work Breakdown Structure skeleton, which is then input into a PERT/CPM system, producing a detailed schedule and resource budget for the project being planned.

On prognostic models, artificial intelligence and censored observations.

PubMed

Anand, S S; Hamilton, P W; Hughes, J G; Bell, D A

2001-03-01

The development of prognostic models for assisting medical practitioners with decision making is not a trivial task. Models need to possess a number of desirable characteristics and few, if any, current modelling approaches based on statistical or artificial intelligence can produce models that display all these characteristics. The inability of modelling techniques to provide truly useful models has led to interest in these models being purely academic in nature. This in turn has resulted in only a very small percentage of models that have been developed being deployed in practice. On the other hand, new modelling paradigms are being proposed continuously within the machine learning and statistical community and claims, often based on inadequate evaluation, being made on their superiority over traditional modelling methods. We believe that for new modelling approaches to deliver true net benefits over traditional techniques, an evaluation centric approach to their development is essential. In this paper we present such an evaluation centric approach to developing extensions to the basic k-nearest neighbour (k-NN) paradigm. We use standard statistical techniques to enhance the distance metric used and a framework based on evidence theory to obtain a prediction for the target example from the outcome of the retrieved exemplars. We refer to this new k-NN algorithm as Censored k-NN (Ck-NN). This reflects the enhancements made to k-NN that are aimed at providing a means for handling censored observations within k-NN.

A fuzzy logic-based model for noise control at industrial workplaces.

PubMed

Aluclu, I; Dalgic, A; Toprak, Z F

2008-05-01

Ergonomics is a broad science encompassing the wide variety of working conditions that can affect worker comfort and health, including factors such as lighting, noise, temperature, vibration, workstation design, tool design, machine design, etc. This paper describes noise-human response and a fuzzy logic model developed by comprehensive field studies on noise measurements (including atmospheric parameters) and control measures. The model has two subsystems constructed on noise reduction quantity in dB. The first subsystem of the fuzzy model depending on 549 linguistic rules comprises acoustical features of all materials used in any workplace. Totally 984 patterns were used, 503 patterns for model development and the rest 481 patterns for testing the model. The second subsystem deals with atmospheric parameter interactions with noise and has 52 linguistic rules. Similarly, 94 field patterns were obtained; 68 patterns were used for training stage of the model and the rest 26 patterns for testing the model. These rules were determined by taking into consideration formal standards, experiences of specialists and the measurements patterns. The results of the model were compared with various statistics (correlation coefficients, max-min, standard deviation, average and coefficient of skewness) and error modes (root mean square error and relative error). The correlation coefficients were significantly high, error modes were quite low and the other statistics were very close to the data. This statement indicates the validity of the model. Therefore, the model can be used for noise control in any workplace and helpful to the designer in planning stage of a workplace.

Incorporating Experience Curves in Appliance Standards Analysis

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

Garbesi, Karina; Chan, Peter; Greenblatt, Jeffery

2011-10-31

The technical analyses in support of U.S. energy conservation standards for residential appliances and commercial equipment have typically assumed that manufacturing costs and retail prices remain constant during the projected 30-year analysis period. There is, however, considerable evidence that this assumption does not reflect real market prices. Costs and prices generally fall in relation to cumulative production, a phenomenon known as experience and modeled by a fairly robust empirical experience curve. Using price data from the Bureau of Labor Statistics, and shipment data obtained as part of the standards analysis process, we present U.S. experience curves for room air conditioners,more » clothes dryers, central air conditioners, furnaces, and refrigerators and freezers. These allow us to develop more representative appliance price projections than the assumption-based approach of constant prices. These experience curves were incorporated into recent energy conservation standards for these products. The impact on the national modeling can be significant, often increasing the net present value of potential standard levels in the analysis. In some cases a previously cost-negative potential standard level demonstrates a benefit when incorporating experience. These results imply that past energy conservation standards analyses may have undervalued the economic benefits of potential standard levels.« less

Enumerating Sparse Organisms in Ships’ Ballast Water: Why Counting to 10 Is Not So Easy

PubMed Central

2011-01-01

To reduce ballast water-borne aquatic invasions worldwide, the International Maritime Organization and United States Coast Guard have each proposed discharge standards specifying maximum concentrations of living biota that may be released in ships’ ballast water (BW), but these regulations still lack guidance for standardized type approval and compliance testing of treatment systems. Verifying whether BW meets a discharge standard poses significant challenges. Properly treated BW will contain extremely sparse numbers of live organisms, and robust estimates of rare events require extensive sampling efforts. A balance of analytical rigor and practicality is essential to determine the volume of BW that can be reasonably sampled and processed, yet yield accurate live counts. We applied statistical modeling to a range of sample volumes, plankton concentrations, and regulatory scenarios (i.e., levels of type I and type II errors), and calculated the statistical power of each combination to detect noncompliant discharge concentrations. The model expressly addresses the roles of sampling error, BW volume, and burden of proof on the detection of noncompliant discharges in order to establish a rigorous lower limit of sampling volume. The potential effects of recovery errors (i.e., incomplete recovery and detection of live biota) in relation to sample volume are also discussed. PMID:21434685

Enumerating sparse organisms in ships' ballast water: why counting to 10 is not so easy.

PubMed

Miller, A Whitman; Frazier, Melanie; Smith, George E; Perry, Elgin S; Ruiz, Gregory M; Tamburri, Mario N

2011-04-15

To reduce ballast water-borne aquatic invasions worldwide, the International Maritime Organization and United States Coast Guard have each proposed discharge standards specifying maximum concentrations of living biota that may be released in ships' ballast water (BW), but these regulations still lack guidance for standardized type approval and compliance testing of treatment systems. Verifying whether BW meets a discharge standard poses significant challenges. Properly treated BW will contain extremely sparse numbers of live organisms, and robust estimates of rare events require extensive sampling efforts. A balance of analytical rigor and practicality is essential to determine the volume of BW that can be reasonably sampled and processed, yet yield accurate live counts. We applied statistical modeling to a range of sample volumes, plankton concentrations, and regulatory scenarios (i.e., levels of type I and type II errors), and calculated the statistical power of each combination to detect noncompliant discharge concentrations. The model expressly addresses the roles of sampling error, BW volume, and burden of proof on the detection of noncompliant discharges in order to establish a rigorous lower limit of sampling volume. The potential effects of recovery errors (i.e., incomplete recovery and detection of live biota) in relation to sample volume are also discussed.

Long-term observations minus background monitoring of ground-based brightness temperatures from a microwave radiometer network

NASA Astrophysics Data System (ADS)

De Angelis, Francesco; Cimini, Domenico; Löhnert, Ulrich; Caumont, Olivier; Haefele, Alexander; Pospichal, Bernhard; Martinet, Pauline; Navas-Guzmán, Francisco; Klein-Baltink, Henk; Dupont, Jean-Charles; Hocking, James

2017-10-01

Ground-based microwave radiometers (MWRs) offer the capability to provide continuous, high-temporal-resolution observations of the atmospheric thermodynamic state in the planetary boundary layer (PBL) with low maintenance. This makes MWR an ideal instrument to supplement radiosonde and satellite observations when initializing numerical weather prediction (NWP) models through data assimilation. State-of-the-art data assimilation systems (e.g. variational schemes) require an accurate representation of the differences between model (background) and observations, which are then weighted by their respective errors to provide the best analysis of the true atmospheric state. In this perspective, one source of information is contained in the statistics of the differences between observations and their background counterparts (O-B). Monitoring of O-B statistics is crucial to detect and remove systematic errors coming from the measurements, the observation operator, and/or the NWP model. This work illustrates a 1-year O-B analysis for MWR observations in clear-sky conditions for an European-wide network of six MWRs. Observations include MWR brightness temperatures (TB) measured by the two most common types of MWR instruments. Background profiles are extracted from the French convective-scale model AROME-France before being converted into TB. The observation operator used to map atmospheric profiles into TB is the fast radiative transfer model RTTOV-gb. It is shown that O-B monitoring can effectively detect instrument malfunctions. O-B statistics (bias, standard deviation, and root mean square) for water vapour channels (22.24-30.0 GHz) are quite consistent for all the instrumental sites, decreasing from the 22.24 GHz line centre ( ˜ 2-2.5 K) towards the high-frequency wing ( ˜ 0.8-1.3 K). Statistics for zenith and lower-elevation observations show a similar trend, though values increase with increasing air mass. O-B statistics for temperature channels show different behaviour for relatively transparent (51-53 GHz) and opaque channels (54-58 GHz). Opaque channels show lower uncertainties (< 0.8-0.9 K) and little variation with elevation angle. Transparent channels show larger biases ( ˜ 2-3 K) with relatively low standard deviations ( ˜ 1-1.5 K). The observations minus analysis TB statistics are similar to the O-B statistics, suggesting a possible improvement to be expected by assimilating MWR TB into NWP models. Lastly, the O-B TB differences have been evaluated to verify the normal-distribution hypothesis underlying variational and ensemble Kalman filter-based DA systems. Absolute values of excess kurtosis and skewness are generally within 1 and 0.5, respectively, for all instrumental sites, demonstrating O-B normal distribution for most of the channels and elevations angles.

78 FR 65317 - National Committee on Vital and Health Statistics: Meeting Standards Subcommittee

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-31

... DEPARTMENT OF HEALTH AND HUMAN SERVICES National Committee on Vital and Health Statistics: Meeting... Health Statistics (NCVHS) Subcommittee on Standards. Time and Date: November 12, 2013 8:30 a.m.-5:30 p.m. EST. Place: Centers for Disease Control and Prevention, National Center for Health Statistics, 3311...

78 FR 54470 - National Committee on Vital and Health Statistics: Meeting Standards Subcommittee

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-04

... DEPARTMENT OF HEALTH AND HUMAN SERVICES National Committee on Vital and Health Statistics: Meeting... Health Statistics (NCVHS) Subcommittee on Standards Time and Date: September 18, 2013 8:30 p.m.--5:00 p.m. EDT. Place: Centers for Disease Control and Prevention, National Center for Health Statistics, 3311...

78 FR 942 - National Committee on Vital and Health Statistics: Meeting Standards Subcommittee

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-07

... DEPARTMENT OF HEALTH AND HUMAN SERVICES National Committee on Vital and Health Statistics: Meeting... Health Statistics (NCVHS) Subcommittee on Standards. Time and Date: February 27, 2013 9:30 a.m.-5:00 p.m... electronic claims attachments. The National Committee on Vital Health Statistics is the public advisory body...

78 FR 34100 - National Committee on Vital and Health Statistics: Meeting Standards Subcommittee

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-06

... DEPARTMENT OF HEALTH AND HUMAN SERVICES National Committee on Vital and Health Statistics: Meeting... Health Statistics (NCVHS) Subcommittee on Standards. Time and Date: June 17, 2013 1:00 p.m.-5:00 p.m. e.d..., National Center for Health Statistics, 3311 Toledo Road, Auditorium B & C, Hyattsville, Maryland 20782...

Maps of estimated nitrate and arsenic concentrations in basin-fill aquifers of the southwestern United States

USGS Publications Warehouse

Beisner, Kimberly R.; Anning, David W.; Paul, Angela P.; McKinney, Tim S.; Huntington, Jena M.; Bexfield, Laura M.; Thiros, Susan A.

2012-01-01

Human-health concerns and economic considerations associated with meeting drinking-water standards motivated a study of the vulnerability of basin-fill aquifers to nitrate contamination and arsenic enrichment in the southwestern United States. Statistical models were developed by using the random forest classifier algorithm to predict concentrations of nitrate and arsenic across a model grid representing about 190,600 square miles of basin-fill aquifers in parts of Arizona, California, Colorado, Nevada, New Mexico, and Utah. The statistical models, referred to as classifiers, reflect natural and human-related factors that affect aquifer vulnerability to contamination and relate nitrate and arsenic concentrations to explanatory variables representing local- and basin-scale measures of source and aquifer susceptibility conditions. Geochemical variables were not used in concentration predictions because they were not available for the entire study area. The models were calibrated to assess model accuracy on the basis of measured values.Only 2 percent of the area underlain by basin-fill aquifers in the study area was predicted to equal or exceed the U.S. Environmental Protection Agency drinking-water standard for nitrate as N (10 milligrams per liter), whereas 43 percent of the area was predicted to equal or exceed the standard for arsenic (10 micrograms per liter). Areas predicted to equal or exceed the drinking-water standard for nitrate include basins in central Arizona near Phoenix; the San Joaquin Valley, the Santa Ana Inland, and San Jacinto Basins of California; and the San Luis Valley of Colorado. Much of the area predicted to equal or exceed the drinking-water standard for arsenic is within a belt of basins along the western portion of the Basin and Range Physiographic Province that includes almost all of Nevada and parts of California and Arizona. Predicted nitrate and arsenic concentrations are substantially lower than the drinking-water standards in much of the study area-about 93 percent of the area underlain by basin-fill aquifers was less than one-half the standard for nitrate as N (5.0 milligrams per liter), and 50 percent was less than one-half the standard for arsenic (5.0 micrograms per liter). The predicted concentrations and the improved understanding of the susceptibility and vulnerability of southwestern basin-fill aquifers to nitrate contamination and arsenic enrichment can be used by water managers as a qualitative tool to assess and protect the quality of groundwater resources in the Southwest.

A Comparison between Discrimination Indices and Item-Response Theory Using the Rasch Model in a Clinical Course Written Examination of a Medical School.

PubMed

Park, Jong Cook; Kim, Kwang Sig

2012-03-01

The reliability of test is determined by each items' characteristics. Item analysis is achieved by classical test theory and item response theory. The purpose of the study was to compare the discrimination indices with item response theory using the Rasch model. Thirty-one 4th-year medical school students participated in the clinical course written examination, which included 22 A-type items and 3 R-type items. Point biserial correlation coefficient (C(pbs)) was compared to method of extreme group (D), biserial correlation coefficient (C(bs)), item-total correlation coefficient (C(it)), and corrected item-total correlation coeffcient (C(cit)). Rasch model was applied to estimate item difficulty and examinee's ability and to calculate item fit statistics using joint maximum likelihood. Explanatory power (r2) of Cpbs is decreased in the following order: C(cit) (1.00), C(it) (0.99), C(bs) (0.94), and D (0.45). The ranges of difficulty logit and standard error and ability logit and standard error were -0.82 to 0.80 and 0.37 to 0.76, -3.69 to 3.19 and 0.45 to 1.03, respectively. Item 9 and 23 have outfit > or =1.3. Student 1, 5, 7, 18, 26, 30, and 32 have fit > or =1.3. C(pbs), C(cit), and C(it) are good discrimination parameters. Rasch model can estimate item difficulty parameter and examinee's ability parameter with standard error. The fit statistics can identify bad items and unpredictable examinee's responses.

Trends in modeling Biomedical Complex Systems

PubMed Central

Milanesi, Luciano; Romano, Paolo; Castellani, Gastone; Remondini, Daniel; Liò, Petro

2009-01-01

In this paper we provide an introduction to the techniques for multi-scale complex biological systems, from the single bio-molecule to the cell, combining theoretical modeling, experiments, informatics tools and technologies suitable for biological and biomedical research, which are becoming increasingly multidisciplinary, multidimensional and information-driven. The most important concepts on mathematical modeling methodologies and statistical inference, bioinformatics and standards tools to investigate complex biomedical systems are discussed and the prominent literature useful to both the practitioner and the theoretician are presented. PMID:19828068

On Teaching about the Coefficient of Variation in Introductory Statistics Courses

ERIC Educational Resources Information Center

Trafimow, David

2014-01-01

The standard deviation is related to the mean by virtue of the coefficient of variation. Teachers of statistics courses can make use of that fact to make the standard deviation more comprehensible for statistics students.

On the error statistics of Viterbi decoding and the performance of concatenated codes

NASA Technical Reports Server (NTRS)

Miller, R. L.; Deutsch, L. J.; Butman, S. A.

1981-01-01

Computer simulation results are presented on the performance of convolutional codes of constraint lengths 7 and 10 concatenated with the (255, 223) Reed-Solomon code (a proposed NASA standard). These results indicate that as much as 0.8 dB can be gained by concatenating this Reed-Solomon code with a (10, 1/3) convolutional code, instead of the (7, 1/2) code currently used by the DSN. A mathematical model of Viterbi decoder burst-error statistics is developed and is validated through additional computer simulations.

Numerical Model Sensitivity to Heterogeneous Satellite Derived Vegetation Roughness

NASA Technical Reports Server (NTRS)

Jasinski, Michael; Eastman, Joseph; Borak, Jordan

2011-01-01

The sensitivity of a mesoscale weather prediction model to a 1 km satellite-based vegetation roughness initialization is investigated for a domain within the south central United States. Three different roughness databases are employed: i) a control or standard lookup table roughness that is a function only of land cover type, ii) a spatially heterogeneous roughness database, specific to the domain, that was previously derived using a physically based procedure and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, and iii) a MODIS climatologic roughness database that like (i) is a function only of land cover type, but possesses domain specific mean values from (ii). The model used is the Weather Research and Forecast Model (WRF) coupled to the Community Land Model within the Land Information System (LIS). For each simulation, a statistical comparison is made between modeled results and ground observations within a domain including Oklahoma, Eastern Arkansas, and Northwest Louisiana during a 4-day period within IHOP 2002. Sensitivity analysis compares the impact the three roughness initializations on time-series temperature, precipitation probability of detection (POD), average wind speed, boundary layer height, and turbulent kinetic energy (TKE). Overall, the results indicate that, for the current investigation, replacement of the standard look-up table values with the satellite-derived values statistically improves model performance for most observed variables. Such natural roughness heterogeneity enhances the surface wind speed, PBL height and TKE production up to 10 percent, with a lesser effect over grassland, and greater effect over mixed land cover domains.

A Review of the Statistical and Quantitative Methods Used to Study Alcohol-Attributable Crime.

PubMed

Fitterer, Jessica L; Nelson, Trisalyn A

2015-01-01

Modelling the relationship between alcohol consumption and crime generates new knowledge for crime prevention strategies. Advances in data, particularly data with spatial and temporal attributes, have led to a growing suite of applied methods for modelling. In support of alcohol and crime researchers we synthesized and critiqued existing methods of spatially and quantitatively modelling the effects of alcohol exposure on crime to aid method selection, and identify new opportunities for analysis strategies. We searched the alcohol-crime literature from 1950 to January 2014. Analyses that statistically evaluated or mapped the association between alcohol and crime were included. For modelling purposes, crime data were most often derived from generalized police reports, aggregated to large spatial units such as census tracts or postal codes, and standardized by residential population data. Sixty-eight of the 90 selected studies included geospatial data of which 48 used cross-sectional datasets. Regression was the prominent modelling choice (n = 78) though dependent on data many variations existed. There are opportunities to improve information for alcohol-attributable crime prevention by using alternative population data to standardize crime rates, sourcing crime information from non-traditional platforms (social media), increasing the number of panel studies, and conducting analysis at the local level (neighbourhood, block, or point). Due to the spatio-temporal advances in crime data, we expect a continued uptake of flexible Bayesian hierarchical modelling, a greater inclusion of spatial-temporal point pattern analysis, and shift toward prospective (forecast) modelling over small areas (e.g., blocks).

A Review of the Statistical and Quantitative Methods Used to Study Alcohol-Attributable Crime

PubMed Central

Fitterer, Jessica L.; Nelson, Trisalyn A.

2015-01-01

Modelling the relationship between alcohol consumption and crime generates new knowledge for crime prevention strategies. Advances in data, particularly data with spatial and temporal attributes, have led to a growing suite of applied methods for modelling. In support of alcohol and crime researchers we synthesized and critiqued existing methods of spatially and quantitatively modelling the effects of alcohol exposure on crime to aid method selection, and identify new opportunities for analysis strategies. We searched the alcohol-crime literature from 1950 to January 2014. Analyses that statistically evaluated or mapped the association between alcohol and crime were included. For modelling purposes, crime data were most often derived from generalized police reports, aggregated to large spatial units such as census tracts or postal codes, and standardized by residential population data. Sixty-eight of the 90 selected studies included geospatial data of which 48 used cross-sectional datasets. Regression was the prominent modelling choice (n = 78) though dependent on data many variations existed. There are opportunities to improve information for alcohol-attributable crime prevention by using alternative population data to standardize crime rates, sourcing crime information from non-traditional platforms (social media), increasing the number of panel studies, and conducting analysis at the local level (neighbourhood, block, or point). Due to the spatio-temporal advances in crime data, we expect a continued uptake of flexible Bayesian hierarchical modelling, a greater inclusion of spatial-temporal point pattern analysis, and shift toward prospective (forecast) modelling over small areas (e.g., blocks). PMID:26418016

Statistical methods used in the public health literature and implications for training of public health professionals

PubMed Central

Hayat, Matthew J.; Powell, Amanda; Johnson, Tessa; Cadwell, Betsy L.

2017-01-01

Statistical literacy and knowledge is needed to read and understand the public health literature. The purpose of this study was to quantify basic and advanced statistical methods used in public health research. We randomly sampled 216 published articles from seven top tier general public health journals. Studies were reviewed by two readers and a standardized data collection form completed for each article. Data were analyzed with descriptive statistics and frequency distributions. Results were summarized for statistical methods used in the literature, including descriptive and inferential statistics, modeling, advanced statistical techniques, and statistical software used. Approximately 81.9% of articles reported an observational study design and 93.1% of articles were substantively focused. Descriptive statistics in table or graphical form were reported in more than 95% of the articles, and statistical inference reported in more than 76% of the studies reviewed. These results reveal the types of statistical methods currently used in the public health literature. Although this study did not obtain information on what should be taught, information on statistical methods being used is useful for curriculum development in graduate health sciences education, as well as making informed decisions about continuing education for public health professionals. PMID:28591190

Statistical methods used in the public health literature and implications for training of public health professionals.

PubMed

Hayat, Matthew J; Powell, Amanda; Johnson, Tessa; Cadwell, Betsy L

2017-01-01

Statistical literacy and knowledge is needed to read and understand the public health literature. The purpose of this study was to quantify basic and advanced statistical methods used in public health research. We randomly sampled 216 published articles from seven top tier general public health journals. Studies were reviewed by two readers and a standardized data collection form completed for each article. Data were analyzed with descriptive statistics and frequency distributions. Results were summarized for statistical methods used in the literature, including descriptive and inferential statistics, modeling, advanced statistical techniques, and statistical software used. Approximately 81.9% of articles reported an observational study design and 93.1% of articles were substantively focused. Descriptive statistics in table or graphical form were reported in more than 95% of the articles, and statistical inference reported in more than 76% of the studies reviewed. These results reveal the types of statistical methods currently used in the public health literature. Although this study did not obtain information on what should be taught, information on statistical methods being used is useful for curriculum development in graduate health sciences education, as well as making informed decisions about continuing education for public health professionals.

Prognostic factors in patients with advanced cancer: use of the patient-generated subjective global assessment in survival prediction.

PubMed

Martin, Lisa; Watanabe, Sharon; Fainsinger, Robin; Lau, Francis; Ghosh, Sunita; Quan, Hue; Atkins, Marlis; Fassbender, Konrad; Downing, G Michael; Baracos, Vickie

2010-10-01

To determine whether elements of a standard nutritional screening assessment are independently prognostic of survival in patients with advanced cancer. A prospective nested cohort of patients with metastatic cancer were accrued from different units of a Regional Palliative Care Program. Patients completed a nutritional screen on admission. Data included age, sex, cancer site, height, weight history, dietary intake, 13 nutrition impact symptoms, and patient- and physician-reported performance status (PS). Univariate and multivariate survival analyses were conducted. Concordance statistics (c-statistics) were used to test the predictive accuracy of models based on training and validation sets; a c-statistic of 0.5 indicates the model predicts the outcome as well as chance; perfect prediction has a c-statistic of 1.0. A training set of patients in palliative home care (n = 1,164) was used to identify prognostic variables. Primary disease site, PS, short-term weight change (either gain or loss), dietary intake, and dysphagia predicted survival in multivariate analysis (P < .05). A model including only patients separated by disease site and PS with high c-statistics between predicted and observed responses for survival in the training set (0.90) and validation set (0.88; n = 603). The addition of weight change, dietary intake, and dysphagia did not further improve the c-statistic of the model. The c-statistic was also not altered by substituting physician-rated palliative PS for patient-reported PS. We demonstrate a high probability of concordance between predicted and observed survival for patients in distinct palliative care settings (home care, tertiary inpatient, ambulatory outpatient) based on patient-reported information.

Automated Clinical Assessment from Smart home-based Behavior Data

PubMed Central

Dawadi, Prafulla Nath; Cook, Diane Joyce; Schmitter-Edgecombe, Maureen

2016-01-01

Smart home technologies offer potential benefits for assisting clinicians by automating health monitoring and well-being assessment. In this paper, we examine the actual benefits of smart home-based analysis by monitoring daily behaviour in the home and predicting standard clinical assessment scores of the residents. To accomplish this goal, we propose a Clinical Assessment using Activity Behavior (CAAB) approach to model a smart home resident’s daily behavior and predict the corresponding standard clinical assessment scores. CAAB uses statistical features that describe characteristics of a resident’s daily activity performance to train machine learning algorithms that predict the clinical assessment scores. We evaluate the performance of CAAB utilizing smart home sensor data collected from 18 smart homes over two years using prediction and classification-based experiments. In the prediction-based experiments, we obtain a statistically significant correlation (r = 0.72) between CAAB-predicted and clinician-provided cognitive assessment scores and a statistically significant correlation (r = 0.45) between CAAB-predicted and clinician-provided mobility scores. Similarly, for the classification-based experiments, we find CAAB has a classification accuracy of 72% while classifying cognitive assessment scores and 76% while classifying mobility scores. These prediction and classification results suggest that it is feasible to predict standard clinical scores using smart home sensor data and learning-based data analysis. PMID:26292348

Multi-scale structure and topological anomaly detection via a new network statistic: The onion decomposition.

PubMed

Hébert-Dufresne, Laurent; Grochow, Joshua A; Allard, Antoine

2016-08-18

We introduce a network statistic that measures structural properties at the micro-, meso-, and macroscopic scales, while still being easy to compute and interpretable at a glance. Our statistic, the onion spectrum, is based on the onion decomposition, which refines the k-core decomposition, a standard network fingerprinting method. The onion spectrum is exactly as easy to compute as the k-cores: It is based on the stages at which each vertex gets removed from a graph in the standard algorithm for computing the k-cores. Yet, the onion spectrum reveals much more information about a network, and at multiple scales; for example, it can be used to quantify node heterogeneity, degree correlations, centrality, and tree- or lattice-likeness. Furthermore, unlike the k-core decomposition, the combined degree-onion spectrum immediately gives a clear local picture of the network around each node which allows the detection of interesting subgraphs whose topological structure differs from the global network organization. This local description can also be leveraged to easily generate samples from the ensemble of networks with a given joint degree-onion distribution. We demonstrate the utility of the onion spectrum for understanding both static and dynamic properties on several standard graph models and on many real-world networks.

Closing in on the large-scale CMB power asymmetry

NASA Astrophysics Data System (ADS)

Contreras, D.; Hutchinson, J.; Moss, A.; Scott, D.; Zibin, J. P.

2018-03-01

Measurements of the cosmic microwave background (CMB) temperature anisotropies have revealed a dipolar asymmetry in power at the largest scales, in apparent contradiction with the statistical isotropy of standard cosmological models. The significance of the effect is not very high, and is dependent on a posteriori choices. Nevertheless, a number of models have been proposed that produce a scale-dependent asymmetry. We confront several such models for a physical, position-space modulation with CMB temperature observations. We find that, while some models that maintain the standard isotropic power spectrum are allowed, others, such as those with modulated tensor or uncorrelated isocurvature modes, can be ruled out on the basis of the overproduction of isotropic power. This remains the case even when an extra isocurvature mode fully anticorrelated with the adiabatic perturbations is added to suppress power on large scales.

Search for High-Mass Resonances Decaying to τ ν in p p Collisions at √{s }=13 TeV with the ATLAS Detector

NASA Astrophysics Data System (ADS)

Aaboud, M.; Aad, G.; Abbott, B.; Abdinov, O.; Abeloos, B.; Abidi, S. H.; Abouzeid, O. S.; Abraham, N. L.; Abramowicz, H.; Abreu, H.; Abulaiti, Y.; Acharya, B. S.; Adachi, S.; Adamczyk, L.; Adelman, J.; Adersberger, M.; Adye, T.; Affolder, A. A.; Afik, Y.; Agheorghiesei, C.; Aguilar-Saavedra, J. A.; Ahlen, S. P.; Ahmadov, F.; Aielli, G.; Akatsuka, S.; Åkesson, T. P. A.; Akilli, E.; Akimov, A. V.; Alberghi, G. L.; Albert, J.; Albicocco, P.; Alconada Verzini, M. J.; Alderweireldt, S.; Aleksa, M.; Aleksandrov, I. N.; Alexa, C.; Alexander, G.; Alexopoulos, T.; Alhroob, M.; Ali, B.; Aliev, M.; Alimonti, G.; Alison, J.; Alkire, S. P.; Allaire, C.; Allbrooke, B. M. M.; Allen, B. W.; Allport, P. P.; Aloisio, A.; Alonso, A.; Alonso, F.; Alpigiani, C.; Alshehri, A. A.; Alstaty, M. I.; Alvarez Gonzalez, B.; Álvarez Piqueras, D.; Alviggi, M. G.; Amadio, B. T.; Amaral Coutinho, Y.; Ambroz, L.; Amelung, C.; Amidei, D.; Amor Dos Santos, S. P.; Amoroso, S.; Amrouche, C. S.; Anastopoulos, C.; Ancu, L. S.; Andari, N.; Andeen, T.; Anders, C. F.; Anders, J. K.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Angelidakis, S.; Angelozzi, I.; Angerami, A.; Anisenkov, A. V.; Annovi, A.; Antel, C.; Anthony, M. T.; Antonelli, M.; Antrim, D. J.; Anulli, F.; Aoki, M.; Aperio Bella, L.; Arabidze, G.; Arai, Y.; Araque, J. P.; Araujo Ferraz, V.; Araujo Pereira, R.; Arce, A. T. H.; Ardell, R. E.; Arduh, F. A.; Arguin, J.-F.; Argyropoulos, S.; Armbruster, A. J.; Armitage, L. J.; Arnaez, O.; Arnold, H.; Arratia, M.; Arslan, O.; Artamonov, A.; Artoni, G.; Artz, S.; Asai, S.; Asbah, N.; Ashkenazi, A.; Asimakopoulou, E. M.; Asquith, L.; Assamagan, K.; Astalos, R.; Atkin, R. J.; Atkinson, M.; Atlay, N. B.; Augsten, K.; Avolio, G.; Avramidou, R.; Axen, B.; Ayoub, M. K.; Azuelos, G.; Baas, A. E.; Baca, M. J.; Bachacou, H.; Bachas, K.; Backes, M.; Bagnaia, P.; Bahmani, M.; Bahrasemani, H.; Baines, J. T.; Bajic, M.; Baker, O. K.; Bakker, P. J.; Bakshi Gupta, D.; Baldin, E. M.; Balek, P.; Balli, F.; Balunas, W. K.; Banas, E.; Bandyopadhyay, A.; Banerjee, Sw.; Bannoura, A. A. E.; Barak, L.; Barbe, W. M.; Barberio, E. L.; Barberis, D.; Barbero, M.; Barillari, T.; Barisits, M.-S.; Barkeloo, J. T.; Barklow, T.; Barlow, N.; Barnea, R.; Barnes, S. L.; Barnett, B. M.; Barnett, R. M.; Barnovska-Blenessy, Z.; Baroncelli, A.; Barone, G.; Barr, A. J.; Barranco Navarro, L.; Barreiro, F.; Barreiro Guimarães da Costa, J.; Bartoldus, R.; Barton, A. E.; Bartos, P.; Basalaev, A.; Bassalat, A.; Bates, R. L.; Batista, S. J.; Batley, J. R.; Battaglia, M.; Bauce, M.; Bauer, F.; Bauer, K. T.; Bawa, H. S.; Beacham, J. B.; Beattie, M. D.; Beau, T.; Beauchemin, P. H.; Bechtle, P.; Beck, H. P.; Beck, H. C.; Becker, K.; Becker, M.; Becot, C.; Beddall, A. J.; Beddall, A.; Bednyakov, V. A.; Bedognetti, M.; Bee, C. P.; Beermann, T. A.; Begalli, M.; Begel, M.; Behera, A.; Behr, J. K.; Bell, A. S.; Bella, G.; Bellagamba, L.; Bellerive, A.; Bellomo, M.; Belotskiy, K.; Belyaev, N. L.; Benary, O.; Benchekroun, D.; Bender, M.; Benekos, N.; Benhammou, Y.; Benhar Noccioli, E.; Benitez, J.; Benjamin, D. P.; Benoit, M.; Bensinger, J. R.; Bentvelsen, S.; Beresford, L.; Beretta, M.; Berge, D.; Bergeaas Kuutmann, E.; Berger, N.; Bergsten, L. J.; Beringer, J.; Berlendis, S.; Bernard, N. R.; Bernardi, G.; Bernius, C.; Bernlochner, F. U.; Berry, T.; Berta, P.; Bertella, C.; Bertoli, G.; Bertram, I. A.; Bertsche, C.; Besjes, G. J.; Bessidskaia Bylund, O.; Bessner, M.; Besson, N.; Bethani, A.; Bethke, S.; Betti, A.; Bevan, A. J.; Beyer, J.; Bianchi, R. M.; Biebel, O.; Biedermann, D.; Bielski, R.; Bierwagen, K.; Biesuz, N. V.; Biglietti, M.; Billoud, T. R. V.; Bindi, M.; Bingul, A.; Bini, C.; Biondi, S.; Bisanz, T.; Bittrich, C.; Bjergaard, D. M.; Black, J. E.; Black, K. M.; Blair, R. E.; Blazek, T.; Bloch, I.; Blocker, C.; Blue, A.; Blumenschein, U.; Blunier, Dr.; Bobbink, G. J.; Bobrovnikov, V. S.; Bocchetta, S. S.; Bocci, A.; Bock, C.; Boerner, D.; Bogavac, D.; Bogdanchikov, A. G.; Bohm, C.; Boisvert, V.; Bokan, P.; Bold, T.; Boldyrev, A. S.; Bolz, A. E.; Bomben, M.; Bona, M.; Bonilla, J. S.; Boonekamp, M.; Borisov, A.; Borissov, G.; Bortfeldt, J.; Bortoletto, D.; Bortolotto, V.; Boscherini, D.; Bosman, M.; Bossio Sola, J. D.; Boudreau, J.; Bouhova-Thacker, E. V.; Boumediene, D.; Bourdarios, C.; Boutle, S. K.; Boveia, A.; Boyd, J.; Boyko, I. R.; Bozson, A. J.; Bracinik, J.; Brahimi, N.; Brandt, A.; Brandt, G.; Brandt, O.; Braren, F.; Bratzler, U.; Brau, B.; Brau, J. E.; Breaden Madden, W. D.; Brendlinger, K.; Brennan, A. J.; Brenner, L.; Brenner, R.; Bressler, S.; Briglin, D. L.; Bristow, T. M.; Britton, D.; Britzger, D.; Brock, I.; Brock, R.; Brooijmans, G.; Brooks, T.; Brooks, W. K.; Brost, E.; Broughton, J. H.; Bruckman de Renstrom, P. A.; Bruncko, D.; Bruni, A.; Bruni, G.; Bruni, L. S.; Bruno, S.; Brunt, Bh; Bruschi, M.; Bruscino, N.; Bryant, P.; Bryngemark, L.; Buanes, T.; Buat, Q.; Buchholz, P.; Buckley, A. G.; Budagov, I. A.; Buehrer, F.; Bugge, M. K.; Bulekov, O.; Bullock, D.; Burch, T. J.; Burdin, S.; Burgard, C. D.; Burger, A. M.; Burghgrave, B.; Burka, K.; Burke, S.; Burmeister, I.; Burr, J. T. P.; Büscher, D.; Büscher, V.; Buschmann, E.; Bussey, P.; Butler, J. M.; Buttar, C. M.; Butterworth, J. M.; Butti, P.; Buttinger, W.; Buzatu, A.; Buzykaev, A. R.; Cabras, G.; Cabrera Urbán, S.; Caforio, D.; Cai, H.; Cairo, V. M. M.; Cakir, O.; Calace, N.; Calafiura, P.; Calandri, A.; Calderini, G.; Calfayan, P.; Callea, G.; Caloba, L. P.; Calvente Lopez, S.; Calvet, D.; Calvet, S.; Calvet, T. P.; Calvetti, M.; Camacho Toro, R.; Camarda, S.; Camarri, P.; Cameron, D.; Caminal Armadans, R.; Camincher, C.; Campana, S.; Campanelli, M.; Camplani, A.; Campoverde, A.; Canale, V.; Cano Bret, M.; Cantero, J.; Cao, T.; Cao, Y.; Capeans Garrido, M. D. M.; Caprini, I.; Caprini, M.; Capua, M.; Carbone, R. M.; Cardarelli, R.; Cardillo, F.; Carli, I.; Carli, T.; Carlino, G.; Carlson, B. T.; Carminati, L.; Carney, R. M. D.; Caron, S.; Carquin, E.; Carrá, S.; Carrillo-Montoya, G. D.; Casadei, D.; Casado, M. P.; Casha, A. F.; Casolino, M.; Casper, D. W.; Castelijn, R.; Castillo Gimenez, V.; Castro, N. F.; Catinaccio, A.; Catmore, J. R.; Cattai, A.; Caudron, J.; Cavaliere, V.; Cavallaro, E.; Cavalli, D.; Cavalli-Sforza, M.; Cavasinni, V.; Celebi, E.; Ceradini, F.; Cerda Alberich, L.; Cerqueira, A. S.; Cerri, A.; Cerrito, L.; Cerutti, F.; Cervelli, A.; Cetin, S. A.; Chafaq, A.; Chakraborty, D.; Chan, S. K.; Chan, W. S.; Chan, Y. L.; Chang, P.; Chapman, J. D.; Charlton, D. G.; Chau, C. C.; Chavez Barajas, C. A.; Che, S.; Chegwidden, A.; Chekanov, S.; Chekulaev, S. V.; Chelkov, G. A.; Chelstowska, M. A.; Chen, C.; Chen, C.; Chen, H.; Chen, J.; Chen, J.; Chen, S.; Chen, S.; Chen, X.; Chen, Y.; Chen, Y.-H.; Cheng, H. C.; Cheng, H. J.; Cheplakov, A.; Cheremushkina, E.; Cherkaoui El Moursli, R.; Cheu, E.; Cheung, K.; Chevalier, L.; Chiarella, V.; Chiarelli, G.; Chiodini, G.; Chisholm, A. S.; Chitan, A.; Chiu, I.; Chiu, Y. H.; Chizhov, M. V.; Choi, K.; Chomont, A. R.; Chouridou, S.; Chow, Y. S.; Christodoulou, V.; Chu, M. C.; Chudoba, J.; Chuinard, A. J.; Chwastowski, J. J.; Chytka, L.; Cinca, D.; Cindro, V.; Cioarǎ, I. A.; Ciocio, A.; Cirotto, F.; Citron, Z. H.; Citterio, M.; Clark, A.; Clark, M. R.; Clark, P. J.; Clarke, R. N.; Clement, C.; Coadou, Y.; Cobal, M.; Coccaro, A.; Cochran, J.; Colasurdo, L.; Cole, B.; Colijn, A. P.; Collot, J.; Conde Muiño, P.; Coniavitis, E.; Connell, S. H.; Connelly, I. A.; Constantinescu, S.; Conventi, F.; Cooper-Sarkar, A. M.; Cormier, F.; Cormier, K. J. R.; Corradi, M.; Corrigan, E. E.; Corriveau, F.; Cortes-Gonzalez, A.; Costa, M. J.; Costanzo, D.; Cottin, G.; Cowan, G.; Cox, B. E.; Crane, J.; Cranmer, K.; Crawley, S. J.; Creager, R. 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L.; La Rotonda, L.; La Ruffa, F.; Lacasta, C.; Lacava, F.; Lacey, J.; Lack, D. P. J.; Lacker, H.; Lacour, D.; Ladygin, E.; Lafaye, R.; Laforge, B.; Lai, S.; Lammers, S.; Lampl, W.; Lançon, E.; Landgraf, U.; Landon, M. P. J.; Lanfermann, M. C.; Lang, V. S.; Lange, J. C.; Langenberg, R. J.; Lankford, A. J.; Lanni, F.; Lantzsch, K.; Lanza, A.; Lapertosa, A.; Laplace, S.; Laporte, J. F.; Lari, T.; Lasagni Manghi, F.; Lassnig, M.; Lau, T. S.; Laudrain, A.; Law, A. T.; Laycock, P.; Lazzaroni, M.; Le, B.; Le Dortz, O.; Le Guirriec, E.; Le Quilleuc, E. P.; Leblanc, M.; Lecompte, T.; Ledroit-Guillon, F.; Lee, C. A.; Lee, G. R.; Lee, S. C.; Lee, L.; Lefebvre, B.; Lefebvre, M.; Legger, F.; Leggett, C.; Lehmann Miotto, G.; Leight, W. A.; Leisos, A.; Leite, M. A. L.; Leitner, R.; Lellouch, D.; Lemmer, B.; Leney, K. J. C.; Lenz, T.; Lenzi, B.; Leone, R.; Leone, S.; Leonidopoulos, C.; Lerner, G.; Leroy, C.; Les, R.; Lesage, A. A. J.; Lester, C. G.; Levchenko, M.; Levêque, J.; Levin, D.; Levinson, L. 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M.; Wang, C.; Wang, F.; Wang, H.; Wang, H.; Wang, J.; Wang, J.; Wang, Q.; Wang, R.-J.; Wang, R.; Wang, R.; Wang, S. M.; Wang, T.; Wang, W.; Wang, W.; Wang, Y.; Wang, Z.; Wanotayaroj, C.; Warburton, A.; Ward, C. P.; Wardrope, D. R.; Washbrook, A.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, B. M.; Webb, A. F.; Webb, S.; Weber, C.; Weber, M. S.; Weber, S. M.; Weber, S. A.; Webster, J. S.; Weidberg, A. R.; Weinert, B.; Weingarten, J.; Weirich, M.; Weiser, C.; Wells, P. S.; Wenaus, T.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M. D.; Werner, P.; Wessels, M.; Weston, T. D.; Whalen, K.; Whallon, N. L.; Wharton, A. M.; White, A. S.; White, A.; White, M. J.; White, R.; Whiteson, D.; Whitmore, B. W.; Wickens, F. J.; Wiedenmann, W.; Wielers, M.; Wiglesworth, C.; Wiik-Fuchs, L. A. M.; Wildauer, A.; Wilk, F.; Wilkens, H. G.; Williams, H. H.; Williams, S.; Willis, C.; Willocq, S.; Wilson, J. A.; Wingerter-Seez, I.; Winkels, E.; Winklmeier, F.; Winston, O. J.; Winter, B. T.; Wittgen, M.; Wobisch, M.; Wolf, A.; Wolf, T. M. H.; Wolff, R.; Wolter, M. W.; Wolters, H.; Wong, V. W. S.; Woods, N. L.; Worm, S. D.; Wosiek, B. K.; Wozniak, K. W.; Wraight, K.; Wu, M.; Wu, S. L.; Wu, X.; Wu, Y.; Wyatt, T. R.; Wynne, B. M.; Xella, S.; Xi, Z.; Xia, L.; Xu, D.; Xu, H.; Xu, L.; Xu, T.; Xu, W.; Yabsley, B.; Yacoob, S.; Yajima, K.; Yallup, D. P.; Yamaguchi, D.; Yamaguchi, Y.; Yamamoto, A.; Yamanaka, T.; Yamane, F.; Yamatani, M.; Yamazaki, T.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, H.; Yang, S.; Yang, Y.; Yang, Y.; Yang, Z.; Yao, W.-M.; Yap, Y. C.; Yasu, Y.; Yatsenko, E.; Yau Wong, K. H.; Ye, J.; Ye, S.; Yeletskikh, I.; Yigitbasi, E.; Yildirim, E.; Yorita, K.; Yoshihara, K.; Young, C.; Young, C. J. S.; Yu, J.; Yu, J.; Yue, X.; Yuen, S. P. Y.; Yusuff, I.; Zabinski, B.; Zacharis, G.; Zaidan, R.; Zaitsev, A. M.; Zakharchuk, N.; Zalieckas, J.; Zambito, S.; Zanzi, D.; Zeitnitz, C.; Zemaityte, G.; Zeng, J. C.; Zeng, Q.; Zenin, O.; Ženiš, T.; Zerwas, D.; Zgubič, M.; Zhang, D.; Zhang, D.; Zhang, F.; Zhang, G.; Zhang, H.; Zhang, J.; Zhang, L.; Zhang, L.; Zhang, M.; Zhang, P.; Zhang, R.; Zhang, R.; Zhang, X.; Zhang, Y.; Zhang, Z.; Zhao, X.; Zhao, Y.; Zhao, Z.; Zhemchugov, A.; Zhou, B.; Zhou, C.; Zhou, L.; Zhou, M.; Zhou, M.; Zhou, N.; Zhou, Y.; Zhu, C. G.; Zhu, H.; Zhu, H.; Zhu, J.; Zhu, Y.; Zhuang, X.; Zhukov, K.; Zhulanov, V.; Zibell, A.; Zieminska, D.; Zimine, N. I.; Zimmermann, S.; Zinonos, Z.; Zinser, M.; Ziolkowski, M.; Živković, L.; Zobernig, G.; Zoccoli, A.; Zoch, K.; Zorbas, T. G.; Zou, R.; Zur Nedden, M.; Zwalinski, L.; Atlas Collaboration

2018-04-01

A search for high-mass resonances decaying to τ ν using proton-proton collisions at √{s }=13 TeV produced by the Large Hadron Collider is presented. Only τ -lepton decays with hadrons in the final state are considered. The data were recorded with the ATLAS detector and correspond to an integrated luminosity of 36.1 fb-1 . No statistically significant excess above the standard model expectation is observed; model-independent upper limits are set on the visible τ ν production cross section. Heavy W' bosons with masses less than 3.7 TeV in the sequential standard model and masses less than 2.2-3.8 TeV depending on the coupling in the nonuniversal G (221 ) model are excluded at the 95% credibility level.

Joint Seasonal ARMA Approach for Modeling of Load Forecast Errors in Planning Studies

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

Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.

2014-04-14

To make informed and robust decisions in the probabilistic power system operation and planning process, it is critical to conduct multiple simulations of the generated combinations of wind and load parameters and their forecast errors to handle the variability and uncertainty of these time series. In order for the simulation results to be trustworthy, the simulated series must preserve the salient statistical characteristics of the real series. In this paper, we analyze day-ahead load forecast error data from multiple balancing authority locations and characterize statistical properties such as mean, standard deviation, autocorrelation, correlation between series, time-of-day bias, and time-of-day autocorrelation.more » We then construct and validate a seasonal autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load forecast error series for all BAs.« less

Addressing issues associated with evaluating prediction models for survival endpoints based on the concordance statistic.

PubMed

Wang, Ming; Long, Qi

2016-09-01

Prediction models for disease risk and prognosis play an important role in biomedical research, and evaluating their predictive accuracy in the presence of censored data is of substantial interest. The standard concordance (c) statistic has been extended to provide a summary measure of predictive accuracy for survival models. Motivated by a prostate cancer study, we address several issues associated with evaluating survival prediction models based on c-statistic with a focus on estimators using the technique of inverse probability of censoring weighting (IPCW). Compared to the existing work, we provide complete results on the asymptotic properties of the IPCW estimators under the assumption of coarsening at random (CAR), and propose a sensitivity analysis under the mechanism of noncoarsening at random (NCAR). In addition, we extend the IPCW approach as well as the sensitivity analysis to high-dimensional settings. The predictive accuracy of prediction models for cancer recurrence after prostatectomy is assessed by applying the proposed approaches. We find that the estimated predictive accuracy for the models in consideration is sensitive to NCAR assumption, and thus identify the best predictive model. Finally, we further evaluate the performance of the proposed methods in both settings of low-dimensional and high-dimensional data under CAR and NCAR through simulations. © 2016, The International Biometric Society.

A generalized estimating equations approach for resting-state functional MRI group analysis.

PubMed

D'Angelo, Gina M; Lazar, Nicole A; Eddy, William F; Morris, John C; Sheline, Yvette I

2011-01-01

An Alzheimer's fMRI study has motivated us to evaluate inter-regional correlations between groups. The overall objective is to assess inter-regional correlations at a resting-state with no stimulus or task. We propose using a generalized estimating equation (GEE) transition model and a GEE marginal model to model the within-subject correlation for each region. Residuals calculated from the GEE models are used to correlate brain regions and assess between group differences. The standard pooling approach of group averages of the Fisher-z transformation assuming temporal independence is a typical approach used to compare group correlations. The GEE approaches and standard Fisher-z pooling approach are demonstrated with an Alzheimer's disease (AD) connectivity study in a population of AD subjects and healthy control subjects. We also compare these methods using simulation studies and show that the transition model may have better statistical properties.

Identifying fMRI Model Violations with Lagrange Multiplier Tests

PubMed Central

Cassidy, Ben; Long, Christopher J; Rae, Caroline; Solo, Victor

2013-01-01

The standard modeling framework in Functional Magnetic Resonance Imaging (fMRI) is predicated on assumptions of linearity, time invariance and stationarity. These assumptions are rarely checked because doing so requires specialised software, although failure to do so can lead to bias and mistaken inference. Identifying model violations is an essential but largely neglected step in standard fMRI data analysis. Using Lagrange Multiplier testing methods we have developed simple and efficient procedures for detecting model violations such as non-linearity, non-stationarity and validity of the common Double Gamma specification for hemodynamic response. These procedures are computationally cheap and can easily be added to a conventional analysis. The test statistic is calculated at each voxel and displayed as a spatial anomaly map which shows regions where a model is violated. The methodology is illustrated with a large number of real data examples. PMID:22542665

Application of linear regression analysis in accuracy assessment of rolling force calculations

NASA Astrophysics Data System (ADS)

Poliak, E. I.; Shim, M. K.; Kim, G. S.; Choo, W. Y.

1998-10-01

Efficient operation of the computational models employed in process control systems require periodical assessment of the accuracy of their predictions. Linear regression is proposed as a tool which allows separate systematic and random prediction errors from those related to measurements. A quantitative characteristic of the model predictive ability is introduced in addition to standard statistical tests for model adequacy. Rolling force calculations are considered as an example for the application. However, the outlined approach can be used to assess the performance of any computational model.

Robust Combining of Disparate Classifiers Through Order Statistics

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Ghosh, Joydeep

2001-01-01

Integrating the outputs of multiple classifiers via combiners or meta-learners has led to substantial improvements in several difficult pattern recognition problems. In this article we investigate a family of combiners based on order statistics, for robust handling of situations where there are large discrepancies in performance of individual classifiers. Based on a mathematical modeling of how the decision boundaries are affected by order statistic combiners, we derive expressions for the reductions in error expected when simple output combination methods based on the the median, the maximum and in general, the ith order statistic, are used. Furthermore, we analyze the trim and spread combiners, both based on linear combinations of the ordered classifier outputs, and show that in the presence of uneven classifier performance, they often provide substantial gains over both linear and simple order statistics combiners. Experimental results on both real world data and standard public domain data sets corroborate these findings.

Guidelines for Assessment and Instruction in Statistics Education (GAISE): extending GAISE into nursing education.

PubMed

Hayat, Matthew J

2014-04-01

Statistics coursework is usually a core curriculum requirement for nursing students at all degree levels. The American Association of Colleges of Nursing (AACN) establishes curriculum standards for academic nursing programs. However, the AACN provides little guidance on statistics education and does not offer standardized competency guidelines or recommendations about course content or learning objectives. Published standards may be used in the course development process to clarify course content and learning objectives. This article includes suggestions for implementing and integrating recommendations given in the Guidelines for Assessment and Instruction in Statistics Education (GAISE) report into statistics education for nursing students. Copyright 2014, SLACK Incorporated.

Standard Errors and Confidence Intervals of Norm Statistics for Educational and Psychological Tests.

PubMed

Oosterhuis, Hannah E M; van der Ark, L Andries; Sijtsma, Klaas

2016-11-14

Norm statistics allow for the interpretation of scores on psychological and educational tests, by relating the test score of an individual test taker to the test scores of individuals belonging to the same gender, age, or education groups, et cetera. Given the uncertainty due to sampling error, one would expect researchers to report standard errors for norm statistics. In practice, standard errors are seldom reported; they are either unavailable or derived under strong distributional assumptions that may not be realistic for test scores. We derived standard errors for four norm statistics (standard deviation, percentile ranks, stanine boundaries and Z-scores) under the mild assumption that the test scores are multinomially distributed. A simulation study showed that the standard errors were unbiased and that corresponding Wald-based confidence intervals had good coverage. Finally, we discuss the possibilities for applying the standard errors in practical test use in education and psychology. The procedure is provided via the R function check.norms, which is available in the mokken package.

Improving the Prognostic Ability through Better Use of Standard Clinical Data - The Nottingham Prognostic Index as an Example

PubMed Central

Winzer, Klaus-Jürgen; Buchholz, Anika; Schumacher, Martin; Sauerbrei, Willi

2016-01-01

Background Prognostic factors and prognostic models play a key role in medical research and patient management. The Nottingham Prognostic Index (NPI) is a well-established prognostic classification scheme for patients with breast cancer. In a very simple way, it combines the information from tumor size, lymph node stage and tumor grade. For the resulting index cutpoints are proposed to classify it into three to six groups with different prognosis. As not all prognostic information from the three and other standard factors is used, we will consider improvement of the prognostic ability using suitable analysis approaches. Methods and Findings Reanalyzing overall survival data of 1560 patients from a clinical database by using multivariable fractional polynomials and further modern statistical methods we illustrate suitable multivariable modelling and methods to derive and assess the prognostic ability of an index. Using a REMARK type profile we summarize relevant steps of the analysis. Adding the information from hormonal receptor status and using the full information from the three NPI components, specifically concerning the number of positive lymph nodes, an extended NPI with improved prognostic ability is derived. Conclusions The prognostic ability of even one of the best established prognostic index in medicine can be improved by using suitable statistical methodology to extract the full information from standard clinical data. This extended version of the NPI can serve as a benchmark to assess the added value of new information, ranging from a new single clinical marker to a derived index from omics data. An established benchmark would also help to harmonize the statistical analyses of such studies and protect against the propagation of many false promises concerning the prognostic value of new measurements. Statistical methods used are generally available and can be used for similar analyses in other diseases. PMID:26938061

Intra-patient comparison of reduced-dose model-based iterative reconstruction with standard-dose adaptive statistical iterative reconstruction in the CT diagnosis and follow-up of urolithiasis.

PubMed

Tenant, Sean; Pang, Chun Lap; Dissanayake, Prageeth; Vardhanabhuti, Varut; Stuckey, Colin; Gutteridge, Catherine; Hyde, Christopher; Roobottom, Carl

2017-10-01

To evaluate the accuracy of reduced-dose CT scans reconstructed using a new generation of model-based iterative reconstruction (MBIR) in the imaging of urinary tract stone disease, compared with a standard-dose CT using 30% adaptive statistical iterative reconstruction. This single-institution prospective study recruited 125 patients presenting either with acute renal colic or for follow-up of known urinary tract stones. They underwent two immediately consecutive scans, one at standard dose settings and one at the lowest dose (highest noise index) the scanner would allow. The reduced-dose scans were reconstructed using both ASIR 30% and MBIR algorithms and reviewed independently by two radiologists. Objective and subjective image quality measures as well as diagnostic data were obtained. The reduced-dose MBIR scan was 100% concordant with the reference standard for the assessment of ureteric stones. It was extremely accurate at identifying calculi of 3 mm and above. The algorithm allowed a dose reduction of 58% without any loss of scan quality. A reduced-dose CT scan using MBIR is accurate in acute imaging for renal colic symptoms and for urolithiasis follow-up and allows a significant reduction in dose. • MBIR allows reduced CT dose with similar diagnostic accuracy • MBIR outperforms ASIR when used for the reconstruction of reduced-dose scans • MBIR can be used to accurately assess stones 3 mm and above.

Development and external validation of a risk-prediction model to predict 5-year overall survival in advanced larynx cancer.

PubMed

Petersen, Japke F; Stuiver, Martijn M; Timmermans, Adriana J; Chen, Amy; Zhang, Hongzhen; O'Neill, James P; Deady, Sandra; Vander Poorten, Vincent; Meulemans, Jeroen; Wennerberg, Johan; Skroder, Carl; Day, Andrew T; Koch, Wayne; van den Brekel, Michiel W M

2018-05-01

TNM-classification inadequately estimates patient-specific overall survival (OS). We aimed to improve this by developing a risk-prediction model for patients with advanced larynx cancer. Cohort study. We developed a risk prediction model to estimate the 5-year OS rate based on a cohort of 3,442 patients with T3T4N0N+M0 larynx cancer. The model was internally validated using bootstrapping samples and externally validated on patient data from five external centers (n = 770). The main outcome was performance of the model as tested by discrimination, calibration, and the ability to distinguish risk groups based on tertiles from the derivation dataset. The model performance was compared to a model based on T and N classification only. We included age, gender, T and N classification, and subsite as prognostic variables in the standard model. After external validation, the standard model had a significantly better fit than a model based on T and N classification alone (C statistic, 0.59 vs. 0.55, P < .001). The model was able to distinguish well among three risk groups based on tertiles of the risk score. Adding treatment modality to the model did not decrease the predictive power. As a post hoc analysis, we tested the added value of comorbidity as scored by American Society of Anesthesiologists score in a subsample, which increased the C statistic to 0.68. A risk prediction model for patients with advanced larynx cancer, consisting of readily available clinical variables, gives more accurate estimations of the estimated 5-year survival rate when compared to a model based on T and N classification alone. 2c. Laryngoscope, 128:1140-1145, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

Are V1 Simple Cells Optimized for Visual Occlusions? A Comparative Study

PubMed Central

Bornschein, Jörg; Henniges, Marc; Lücke, Jörg

2013-01-01

Simple cells in primary visual cortex were famously found to respond to low-level image components such as edges. Sparse coding and independent component analysis (ICA) emerged as the standard computational models for simple cell coding because they linked their receptive fields to the statistics of visual stimuli. However, a salient feature of image statistics, occlusions of image components, is not considered by these models. Here we ask if occlusions have an effect on the predicted shapes of simple cell receptive fields. We use a comparative approach to answer this question and investigate two models for simple cells: a standard linear model and an occlusive model. For both models we simultaneously estimate optimal receptive fields, sparsity and stimulus noise. The two models are identical except for their component superposition assumption. We find the image encoding and receptive fields predicted by the models to differ significantly. While both models predict many Gabor-like fields, the occlusive model predicts a much sparser encoding and high percentages of ‘globular’ receptive fields. This relatively new center-surround type of simple cell response is observed since reverse correlation is used in experimental studies. While high percentages of ‘globular’ fields can be obtained using specific choices of sparsity and overcompleteness in linear sparse coding, no or only low proportions are reported in the vast majority of studies on linear models (including all ICA models). Likewise, for the here investigated linear model and optimal sparsity, only low proportions of ‘globular’ fields are observed. In comparison, the occlusive model robustly infers high proportions and can match the experimentally observed high proportions of ‘globular’ fields well. Our computational study, therefore, suggests that ‘globular’ fields may be evidence for an optimal encoding of visual occlusions in primary visual cortex. PMID:23754938

The Reliability of Setting Grade Boundaries Using Comparative Judgement

ERIC Educational Resources Information Center

Benton, Tom; Elliott, Gill

2016-01-01

In recent years the use of expert judgement to set and maintain examination standards has been increasingly criticised in favour of approaches based on statistical modelling. This paper reviews existing research on this controversy and attempts to unify the evidence within a framework where expertise is utilised in the form of comparative…

A Comparison of Imputation Methods for Bayesian Factor Analysis Models

ERIC Educational Resources Information Center

Merkle, Edgar C.

2011-01-01

Imputation methods are popular for the handling of missing data in psychology. The methods generally consist of predicting missing data based on observed data, yielding a complete data set that is amiable to standard statistical analyses. In the context of Bayesian factor analysis, this article compares imputation under an unrestricted…

Standard and Robust Methods in Regression Imputation

ERIC Educational Resources Information Center

Moraveji, Behjat; Jafarian, Koorosh

2014-01-01

The aim of this paper is to provide an introduction of new imputation algorithms for estimating missing values from official statistics in larger data sets of data pre-processing, or outliers. The goal is to propose a new algorithm called IRMI (iterative robust model-based imputation). This algorithm is able to deal with all challenges like…

Normalization Ridge Regression in Practice II: The Estimation of Multiple Feedback Linkages.

ERIC Educational Resources Information Center

Bulcock, J. W.

The use of the two-stage least squares (2 SLS) procedure for estimating nonrecursive social science models is often impractical when multiple feedback linkages are required. This is because 2 SLS is extremely sensitive to multicollinearity. The standard statistical solution to the multicollinearity problem is a biased, variance reduced procedure…

Representation of microstructural features and magnetic anisotropy of electrical steels in an energy-based vector hysteresis model

NASA Astrophysics Data System (ADS)

Jacques, Kevin; Steentjes, Simon; Henrotte, François; Geuzaine, Christophe; Hameyer, Kay

2018-04-01

This paper demonstrates how the statistical distribution of pinning fields in a ferromagnetic material can be identified systematically from standard magnetic measurements, Epstein frame or Single Sheet Tester (SST). The correlation between the pinning field distribution and microstructural parameters of the material is then analyzed.

Quantitative evaluation of ASiR image quality: an adaptive statistical iterative reconstruction technique

NASA Astrophysics Data System (ADS)

Van de Casteele, Elke; Parizel, Paul; Sijbers, Jan

2012-03-01

Adaptive statistical iterative reconstruction (ASiR) is a new reconstruction algorithm used in the field of medical X-ray imaging. This new reconstruction method combines the idealized system representation, as we know it from the standard Filtered Back Projection (FBP) algorithm, and the strength of iterative reconstruction by including a noise model in the reconstruction scheme. It studies how noise propagates through the reconstruction steps, feeds this model back into the loop and iteratively reduces noise in the reconstructed image without affecting spatial resolution. In this paper the effect of ASiR on the contrast to noise ratio is studied using the low contrast module of the Catphan phantom. The experiments were done on a GE LightSpeed VCT system at different voltages and currents. The results show reduced noise and increased contrast for the ASiR reconstructions compared to the standard FBP method. For the same contrast to noise ratio the images from ASiR can be obtained using 60% less current, leading to a reduction in dose of the same amount.

The role of presumed probability density functions in the simulation of nonpremixed turbulent combustion

NASA Astrophysics Data System (ADS)

Coclite, A.; Pascazio, G.; De Palma, P.; Cutrone, L.

2016-07-01

Flamelet-Progress-Variable (FPV) combustion models allow the evaluation of all thermochemical quantities in a reacting flow by computing only the mixture fraction Z and a progress variable C. When using such a method to predict turbulent combustion in conjunction with a turbulence model, a probability density function (PDF) is required to evaluate statistical averages (e. g., Favre averages) of chemical quantities. The choice of the PDF is a compromise between computational costs and accuracy level. The aim of this paper is to investigate the influence of the PDF choice and its modeling aspects to predict turbulent combustion. Three different models are considered: the standard one, based on the choice of a β-distribution for Z and a Dirac-distribution for C; a model employing a β-distribution for both Z and C; and the third model obtained using a β-distribution for Z and the statistically most likely distribution (SMLD) for C. The standard model, although widely used, does not take into account the interaction between turbulence and chemical kinetics as well as the dependence of the progress variable not only on its mean but also on its variance. The SMLD approach establishes a systematic framework to incorporate informations from an arbitrary number of moments, thus providing an improvement over conventionally employed presumed PDF closure models. The rational behind the choice of the three PDFs is described in some details and the prediction capability of the corresponding models is tested vs. well-known test cases, namely, the Sandia flames, and H2-air supersonic combustion.

The fourth radiation transfer model intercomparison (RAMI-IV): Proficiency testing of canopy reflectance models with ISO-13528

NASA Astrophysics Data System (ADS)

Widlowski, J.-L.; Pinty, B.; Lopatka, M.; Atzberger, C.; Buzica, D.; Chelle, M.; Disney, M.; Gastellu-Etchegorry, J.-P.; Gerboles, M.; Gobron, N.; Grau, E.; Huang, H.; Kallel, A.; Kobayashi, H.; Lewis, P. E.; Qin, W.; Schlerf, M.; Stuckens, J.; Xie, D.

2013-07-01

The radiation transfer model intercomparison (RAMI) activity aims at assessing the reliability of physics-based radiative transfer (RT) models under controlled experimental conditions. RAMI focuses on computer simulation models that mimic the interactions of radiation with plant canopies. These models are increasingly used in the development of satellite retrieval algorithms for terrestrial essential climate variables (ECVs). Rather than applying ad hoc performance metrics, RAMI-IV makes use of existing ISO standards to enhance the rigor of its protocols evaluating the quality of RT models. ISO-13528 was developed "to determine the performance of individual laboratories for specific tests or measurements." More specifically, it aims to guarantee that measurement results fall within specified tolerance criteria from a known reference. Of particular interest to RAMI is that ISO-13528 provides guidelines for comparisons where the true value of the target quantity is unknown. In those cases, "truth" must be replaced by a reliable "conventional reference value" to enable absolute performance tests. This contribution will show, for the first time, how the ISO-13528 standard developed by the chemical and physical measurement communities can be applied to proficiency testing of computer simulation models. Step by step, the pre-screening of data, the identification of reference solutions, and the choice of proficiency statistics will be discussed and illustrated with simulation results from the RAMI-IV "abstract canopy" scenarios. Detailed performance statistics of the participating RT models will be provided and the role of the accuracy of the reference solutions as well as the choice of the tolerance criteria will be highlighted.

An SAS Macro for Implementing the Modified Bollen-Stine Bootstrap for Missing Data: Implementing the Bootstrap Using Existing Structural Equation Modeling Software

ERIC Educational Resources Information Center

Enders, Craig K.

2005-01-01

The Bollen-Stine bootstrap can be used to correct for standard error and fit statistic bias that occurs in structural equation modeling (SEM) applications due to nonnormal data. The purpose of this article is to demonstrate the use of a custom SAS macro program that can be used to implement the Bollen-Stine bootstrap with existing SEM software.…

Time Series Model Identification by Estimating Information, Memory, and Quantiles.

DTIC Science & Technology

1983-07-01

Standards, Sect. D, 68D, 937-951. Parzen, Emanuel (1969) "Multiple time series modeling" Multivariate Analysis - II, edited by P. Krishnaiah , Academic... Krishnaiah , North Holland: Amsterdam, 283-295. Parzen, Emanuel (1979) "Forecasting and Whitening Filter Estimation" TIMS Studies in the Management...principle. Applications of Statistics, P. R. Krishnaiah , ed. North Holland: Amsterdam, 27-41. Box, G. E. P. and Jenkins, G. M. (1970) Time Series Analysis

Battery Calendar Life Estimator Manual Modeling and Simulation

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

Jon P. Christophersen; Ira Bloom; Ed Thomas

2012-10-01

The Battery Life Estimator (BLE) Manual has been prepared to assist developers in their efforts to estimate the calendar life of advanced batteries for automotive applications. Testing requirements and procedures are defined by the various manuals previously published under the United States Advanced Battery Consortium (USABC). The purpose of this manual is to describe and standardize a method for estimating calendar life based on statistical models and degradation data acquired from typical USABC battery testing.

Battery Life Estimator Manual Linear Modeling and Simulation

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

Jon P. Christophersen; Ira Bloom; Ed Thomas

2009-08-01

The Battery Life Estimator (BLE) Manual has been prepared to assist developers in their efforts to estimate the calendar life of advanced batteries for automotive applications. Testing requirements and procedures are defined by the various manuals previously published under the United States Advanced Battery Consortium (USABC). The purpose of this manual is to describe and standardize a method for estimating calendar life based on statistical models and degradation data acquired from typical USABC battery testing.

Time Exceedances for High Intensity Solar Proton Fluxes

NASA Technical Reports Server (NTRS)

Xapsos, Michael A.; Stauffer, Craig A.; Jordan, Thomas M.; Adam, James H., Jr.; Dietrich, William F.

2011-01-01

A model is presented for times during a space mission that specified solar proton flux levels are exceeded. This includes both total time and continuous time periods during missions. Results for the solar maximum and solar minimum phases of the solar cycle are presented and compared for a broad range of proton energies and shielding levels. This type of approach is more amenable to reliability analysis for spacecraft systems and instrumentation than standard statistical models.

Cosmological Constraints from Fourier Phase Statistics

NASA Astrophysics Data System (ADS)

Ali, Kamran; Obreschkow, Danail; Howlett, Cullan; Bonvin, Camille; Llinares, Claudio; Oliveira Franco, Felipe; Power, Chris

2018-06-01

Most statistical inference from cosmic large-scale structure relies on two-point statistics, i.e. on the galaxy-galaxy correlation function (2PCF) or the power spectrum. These statistics capture the full information encoded in the Fourier amplitudes of the galaxy density field but do not describe the Fourier phases of the field. Here, we quantify the information contained in the line correlation function (LCF), a three-point Fourier phase correlation function. Using cosmological simulations, we estimate the Fisher information (at redshift z = 0) of the 2PCF, LCF and their combination, regarding the cosmological parameters of the standard ΛCDM model, as well as a Warm Dark Matter (WDM) model and the f(R) and Symmetron modified gravity models. The galaxy bias is accounted for at the level of a linear bias. The relative information of the 2PCF and the LCF depends on the survey volume, sampling density (shot noise) and the bias uncertainty. For a volume of 1h^{-3}Gpc^3, sampled with points of mean density \\bar{n} = 2× 10^{-3} h3 Mpc^{-3} and a bias uncertainty of 13%, the LCF improves the parameter constraints by about 20% in the ΛCDM cosmology and potentially even more in alternative models. Finally, since a linear bias only affects the Fourier amplitudes (2PCF), but not the phases (LCF), the combination of the 2PCF and the LCF can be used to break the degeneracy between the linear bias and σ8, present in 2-point statistics.

External Validation of a Case-Mix Adjustment Model for the Standardized Reporting of 30-Day Stroke Mortality Rates in China

PubMed Central

Yu, Ping; Pan, Yuesong; Wang, Yongjun; Wang, Xianwei; Liu, Liping; Ji, Ruijun; Meng, Xia; Jing, Jing; Tong, Xu; Guo, Li; Wang, Yilong

2016-01-01

Background and Purpose A case-mix adjustment model has been developed and externally validated, demonstrating promise. However, the model has not been thoroughly tested among populations in China. In our study, we evaluated the performance of the model in Chinese patients with acute stroke. Methods The case-mix adjustment model A includes items on age, presence of atrial fibrillation on admission, National Institutes of Health Stroke Severity Scale (NIHSS) score on admission, and stroke type. Model B is similar to Model A but includes only the consciousness component of the NIHSS score. Both model A and B were evaluated to predict 30-day mortality rates in 13,948 patients with acute stroke from the China National Stroke Registry. The discrimination of the models was quantified by c-statistic. Calibration was assessed using Pearson’s correlation coefficient. Results The c-statistic of model A in our external validation cohort was 0.80 (95% confidence interval, 0.79–0.82), and the c-statistic of model B was 0.82 (95% confidence interval, 0.81–0.84). Excellent calibration was reported in the two models with Pearson’s correlation coefficient (0.892 for model A, p<0.001; 0.927 for model B, p = 0.008). Conclusions The case-mix adjustment model could be used to effectively predict 30-day mortality rates in Chinese patients with acute stroke. PMID:27846282

Simplified estimation of age-specific reference intervals for skewed data.

PubMed

Wright, E M; Royston, P

1997-12-30

Age-specific reference intervals are commonly used in medical screening and clinical practice, where interest lies in the detection of extreme values. Many different statistical approaches have been published on this topic. The advantages of a parametric method are that they necessarily produce smooth centile curves, the entire density is estimated and an explicit formula is available for the centiles. The method proposed here is a simplified version of a recent approach proposed by Royston and Wright. Basic transformations of the data and multiple regression techniques are combined to model the mean, standard deviation and skewness. Using these simple tools, which are implemented in almost all statistical computer packages, age-specific reference intervals may be obtained. The scope of the method is illustrated by fitting models to several real data sets and assessing each model using goodness-of-fit techniques.

Nonlinear estimation of parameters in biphasic Arrhenius plots.

PubMed

Puterman, M L; Hrboticky, N; Innis, S M

1988-05-01

This paper presents a formal procedure for the statistical analysis of data on the thermotropic behavior of membrane-bound enzymes generated using the Arrhenius equation and compares the analysis to several alternatives. Data is modeled by a bent hyperbola. Nonlinear regression is used to obtain estimates and standard errors of the intersection of line segments, defined as the transition temperature, and slopes, defined as energies of activation of the enzyme reaction. The methodology allows formal tests of the adequacy of a biphasic model rather than either a single straight line or a curvilinear model. Examples on data concerning the thermotropic behavior of pig brain synaptosomal acetylcholinesterase are given. The data support the biphasic temperature dependence of this enzyme. The methodology represents a formal procedure for statistical validation of any biphasic data and allows for calculation of all line parameters with estimates of precision.

Nutrient-enriched formula versus standard term formula for preterm infants following hospital discharge.

PubMed

Henderson, G; Fahey, T; McGuire, W

2007-10-17

Preterm infants are often growth-restricted at hospital discharge. Feeding infants after hospital discharge with nutrient-enriched formula rather than standard term formula might facilitate "catch-up" growth and improve development. To determine the effect of feeding nutrient-enriched formula compared with standard term formula on growth and development for preterm infants following hospital discharge. The standard search strategy of the Cochrane Neonatal Review Group were used. This included searches of the Cochrane Central Register of Controlled Trials (CENTRAL, The Cochrane Library, Issue 2, 2007), MEDLINE (1966 - May 2007), EMBASE (1980 - May 2007), CINAHL (1982 - May 2007), conference proceedings, and previous reviews. Randomised or quasi-randomised controlled trials that compared the effect of feeding preterm infants following hospital discharge with nutrient-enriched formula compared with standard term formula. Data was extracted using the standard methods of the Cochrane Neonatal Review Group, with separate evaluation of trial quality and data extraction by two authors, and synthesis of data using weighted mean difference and a fixed effects model for meta-analysis. Seven trials were found that were eligible for inclusion. These recruited a total of 631 infants and were generally of good methodological quality. The trials found little evidence that feeding with nutrient-enriched formula milk affected growth and development. Because of differences in the way individual trials measured and presented outcomes, data synthesis was limited. Growth data from two trials found that, at six months post-term, infants fed with nutrient-enriched formula had statistically significantly lower weights [weighted mean difference: -601 (95% confidence interval -1028, -174) grams], lengths [-18.8 (-30.0, -7.6) millimetres], and head circumferences [-10.2 ( -18.0, -2.4) millimetres], than infants fed standard term formula. At 12 to 18 months post-term, meta-analyses of data from three trials did not find any statistically significant differences in growth parameters. However, examination of these meta-analyses demonstrated statistical heterogeneity. Meta-analyses of data from two trials did not reveal a statistically significant difference in Bayley Mental Development or Psychomotor Development Indices. There are not yet any data on growth or development through later childhood. The available data do not provide strong evidence that feeding preterm infants following hospital discharge with nutrient-enriched formula compared with standard term formula affects growth rates or development up to 18 months post-term.

Sampling methods to the statistical control of the production of blood components.

PubMed

Pereira, Paulo; Seghatchian, Jerard; Caldeira, Beatriz; Santos, Paula; Castro, Rosa; Fernandes, Teresa; Xavier, Sandra; de Sousa, Gracinda; de Almeida E Sousa, João Paulo

2017-12-01

The control of blood components specifications is a requirement generalized in Europe by the European Commission Directives and in the US by the AABB standards. The use of a statistical process control methodology is recommended in the related literature, including the EDQM guideline. The control reliability is dependent of the sampling. However, a correct sampling methodology seems not to be systematically applied. Commonly, the sampling is intended to comply uniquely with the 1% specification to the produced blood components. Nevertheless, on a purely statistical viewpoint, this model could be argued not to be related to a consistent sampling technique. This could be a severe limitation to detect abnormal patterns and to assure that the production has a non-significant probability of producing nonconforming components. This article discusses what is happening in blood establishments. Three statistical methodologies are proposed: simple random sampling, sampling based on the proportion of a finite population, and sampling based on the inspection level. The empirical results demonstrate that these models are practicable in blood establishments contributing to the robustness of sampling and related statistical process control decisions for the purpose they are suggested for. Copyright © 2017 Elsevier Ltd. All rights reserved.

A test-bed modeling study for wave resource assessment

NASA Astrophysics Data System (ADS)

Yang, Z.; Neary, V. S.; Wang, T.; Gunawan, B.; Dallman, A.

2016-02-01

Hindcasts from phase-averaged wave models are commonly used to estimate standard statistics used in wave energy resource assessments. However, the research community and wave energy converter industry is lacking a well-documented and consistent modeling approach for conducting these resource assessments at different phases of WEC project development, and at different spatial scales, e.g., from small-scale pilot study to large-scale commercial deployment. Therefore, it is necessary to evaluate current wave model codes, as well as limitations and knowledge gaps for predicting sea states, in order to establish best wave modeling practices, and to identify future research needs to improve wave prediction for resource assessment. This paper presents the first phase of an on-going modeling study to address these concerns. The modeling study is being conducted at a test-bed site off the Central Oregon Coast using two of the most widely-used third-generation wave models - WaveWatchIII and SWAN. A nested-grid modeling approach, with domain dimension ranging from global to regional scales, was used to provide wave spectral boundary condition to a local scale model domain, which has a spatial dimension around 60km by 60km and a grid resolution of 250m - 300m. Model results simulated by WaveWatchIII and SWAN in a structured-grid framework are compared to NOAA wave buoy data for the six wave parameters, including omnidirectional wave power, significant wave height, energy period, spectral width, direction of maximum directionally resolved wave power, and directionality coefficient. Model performance and computational efficiency are evaluated, and the best practices for wave resource assessments are discussed, based on a set of standard error statistics and model run times.

An information-theoretic approach to the modeling and analysis of whole-genome bisulfite sequencing data.

PubMed

Jenkinson, Garrett; Abante, Jordi; Feinberg, Andrew P; Goutsias, John

2018-03-07

DNA methylation is a stable form of epigenetic memory used by cells to control gene expression. Whole genome bisulfite sequencing (WGBS) has emerged as a gold-standard experimental technique for studying DNA methylation by producing high resolution genome-wide methylation profiles. Statistical modeling and analysis is employed to computationally extract and quantify information from these profiles in an effort to identify regions of the genome that demonstrate crucial or aberrant epigenetic behavior. However, the performance of most currently available methods for methylation analysis is hampered by their inability to directly account for statistical dependencies between neighboring methylation sites, thus ignoring significant information available in WGBS reads. We present a powerful information-theoretic approach for genome-wide modeling and analysis of WGBS data based on the 1D Ising model of statistical physics. This approach takes into account correlations in methylation by utilizing a joint probability model that encapsulates all information available in WGBS methylation reads and produces accurate results even when applied on single WGBS samples with low coverage. Using the Shannon entropy, our approach provides a rigorous quantification of methylation stochasticity in individual WGBS samples genome-wide. Furthermore, it utilizes the Jensen-Shannon distance to evaluate differences in methylation distributions between a test and a reference sample. Differential performance assessment using simulated and real human lung normal/cancer data demonstrate a clear superiority of our approach over DSS, a recently proposed method for WGBS data analysis. Critically, these results demonstrate that marginal methods become statistically invalid when correlations are present in the data. This contribution demonstrates clear benefits and the necessity of modeling joint probability distributions of methylation using the 1D Ising model of statistical physics and of quantifying methylation stochasticity using concepts from information theory. By employing this methodology, substantial improvement of DNA methylation analysis can be achieved by effectively taking into account the massive amount of statistical information available in WGBS data, which is largely ignored by existing methods.

A statistical approach to quasi-extinction forecasting.

PubMed

Holmes, Elizabeth Eli; Sabo, John L; Viscido, Steven Vincent; Fagan, William Fredric

2007-12-01

Forecasting population decline to a certain critical threshold (the quasi-extinction risk) is one of the central objectives of population viability analysis (PVA), and such predictions figure prominently in the decisions of major conservation organizations. In this paper, we argue that accurate forecasting of a population's quasi-extinction risk does not necessarily require knowledge of the underlying biological mechanisms. Because of the stochastic and multiplicative nature of population growth, the ensemble behaviour of population trajectories converges to common statistical forms across a wide variety of stochastic population processes. This paper provides a theoretical basis for this argument. We show that the quasi-extinction surfaces of a variety of complex stochastic population processes (including age-structured, density-dependent and spatially structured populations) can be modelled by a simple stochastic approximation: the stochastic exponential growth process overlaid with Gaussian errors. Using simulated and real data, we show that this model can be estimated with 20-30 years of data and can provide relatively unbiased quasi-extinction risk with confidence intervals considerably smaller than (0,1). This was found to be true even for simulated data derived from some of the noisiest population processes (density-dependent feedback, species interactions and strong age-structure cycling). A key advantage of statistical models is that their parameters and the uncertainty of those parameters can be estimated from time series data using standard statistical methods. In contrast for most species of conservation concern, biologically realistic models must often be specified rather than estimated because of the limited data available for all the various parameters. Biologically realistic models will always have a prominent place in PVA for evaluating specific management options which affect a single segment of a population, a single demographic rate, or different geographic areas. However, for forecasting quasi-extinction risk, statistical models that are based on the convergent statistical properties of population processes offer many advantages over biologically realistic models.

The joint space-time statistics of macroweather precipitation, space-time statistical factorization and macroweather models

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

Lovejoy, S., E-mail: lovejoy@physics.mcgill.ca; Lima, M. I. P. de; Department of Civil Engineering, University of Coimbra, 3030-788 Coimbra

2015-07-15

Over the range of time scales from about 10 days to 30–100 years, in addition to the familiar weather and climate regimes, there is an intermediate “macroweather” regime characterized by negative temporal fluctuation exponents: implying that fluctuations tend to cancel each other out so that averages tend to converge. We show theoretically and numerically that macroweather precipitation can be modeled by a stochastic weather-climate model (the Climate Extended Fractionally Integrated Flux, model, CEFIF) first proposed for macroweather temperatures and we show numerically that a four parameter space-time CEFIF model can approximately reproduce eight or so empirical space-time exponents. In spitemore » of this success, CEFIF is theoretically and numerically difficult to manage. We therefore propose a simplified stochastic model in which the temporal behavior is modeled as a fractional Gaussian noise but the spatial behaviour as a multifractal (climate) cascade: a spatial extension of the recently introduced ScaLIng Macroweather Model, SLIMM. Both the CEFIF and this spatial SLIMM model have a property often implicitly assumed by climatologists that climate statistics can be “homogenized” by normalizing them with the standard deviation of the anomalies. Physically, it means that the spatial macroweather variability corresponds to different climate zones that multiplicatively modulate the local, temporal statistics. This simplified macroweather model provides a framework for macroweather forecasting that exploits the system's long range memory and spatial correlations; for it, the forecasting problem has been solved. We test this factorization property and the model with the help of three centennial, global scale precipitation products that we analyze jointly in space and in time.« less

Zero-state Markov switching count-data models: an empirical assessment.

PubMed

Malyshkina, Nataliya V; Mannering, Fred L

2010-01-01

In this study, a two-state Markov switching count-data model is proposed as an alternative to zero-inflated models to account for the preponderance of zeros sometimes observed in transportation count data, such as the number of accidents occurring on a roadway segment over some period of time. For this accident-frequency case, zero-inflated models assume the existence of two states: one of the states is a zero-accident count state, which has accident probabilities that are so low that they cannot be statistically distinguished from zero, and the other state is a normal-count state, in which counts can be non-negative integers that are generated by some counting process, for example, a Poisson or negative binomial. While zero-inflated models have come under some criticism with regard to accident-frequency applications - one fact is undeniable - in many applications they provide a statistically superior fit to the data. The Markov switching approach we propose seeks to overcome some of the criticism associated with the zero-accident state of the zero-inflated model by allowing individual roadway segments to switch between zero and normal-count states over time. An important advantage of this Markov switching approach is that it allows for the direct statistical estimation of the specific roadway-segment state (i.e., zero-accident or normal-count state) whereas traditional zero-inflated models do not. To demonstrate the applicability of this approach, a two-state Markov switching negative binomial model (estimated with Bayesian inference) and standard zero-inflated negative binomial models are estimated using five-year accident frequencies on Indiana interstate highway segments. It is shown that the Markov switching model is a viable alternative and results in a superior statistical fit relative to the zero-inflated models.

Statistical Mechanical Foundation for the Two-State Transition in Protein Folding of Small Globular Proteins

NASA Astrophysics Data System (ADS)

Iguchi, Kazumoto

We discuss the statistical mechanical foundation for the two-state transition in the protein folding of small globular proteins. In the standard arguments of protein folding, the statistical search for the ground state is carried out from astronomically many conformations in the configuration space. This leads us to the famous Levinthal's paradox. To resolve the paradox, Gō first postulated that the two-state transition - all-or-none type transition - is very crucial for the protein folding of small globular proteins and used the Gō's lattice model to show the two-state transition nature. Recently, there have been accumulated many experimental results that support the two-state transition for small globular proteins. Stimulated by such recent experiments, Zwanzig has introduced a minimal statistical mechanical model that exhibits the two-state transition. Also, Finkelstein and coworkers have discussed the solution of the paradox by considering the sequential folding of a small globular protein. On the other hand, recently Iguchi have introduced a toy model of protein folding using the Rubik's magic snake model, in which all folded structures are exactly known and mathematically represented in terms of the four types of conformations: cis-, trans-, left and right gauche-configurations between the unit polyhedrons. In this paper, we study the relationship between the Gō's two-state transition, the Zwanzig's statistical mechanics model and the Finkelsteinapos;s sequential folding model by applying them to the Rubik's magic snake models. We show that the foundation of the Gō's two-state transition model relies on the search within the equienergy surface that is labeled by the contact order of the hydrophobic condensation. This idea reproduces the Zwanzig's statistical model as a special case, realizes the Finkelstein's sequential folding model and fits together to understand the nature of the two-state transition of a small globular protein by calculating the physical quantities such as the free energy, the contact order and the specific heat. We point out the similarity between the liquid-gas transition in statistical mechanics and the two-state transition of protein folding. We also study morphology of the Rubik's magic snake models to give a prototype model for understanding the differences between α-helices proteins and β-sheets proteins.

Model-based Iterative Reconstruction: Effect on Patient Radiation Dose and Image Quality in Pediatric Body CT

PubMed Central

Dillman, Jonathan R.; Goodsitt, Mitchell M.; Christodoulou, Emmanuel G.; Keshavarzi, Nahid; Strouse, Peter J.

2014-01-01

Purpose To retrospectively compare image quality and radiation dose between a reduced-dose computed tomographic (CT) protocol that uses model-based iterative reconstruction (MBIR) and a standard-dose CT protocol that uses 30% adaptive statistical iterative reconstruction (ASIR) with filtered back projection. Materials and Methods Institutional review board approval was obtained. Clinical CT images of the chest, abdomen, and pelvis obtained with a reduced-dose protocol were identified. Images were reconstructed with two algorithms: MBIR and 100% ASIR. All subjects had undergone standard-dose CT within the prior year, and the images were reconstructed with 30% ASIR. Reduced- and standard-dose images were evaluated objectively and subjectively. Reduced-dose images were evaluated for lesion detectability. Spatial resolution was assessed in a phantom. Radiation dose was estimated by using volumetric CT dose index (CTDIvol) and calculated size-specific dose estimates (SSDE). A combination of descriptive statistics, analysis of variance, and t tests was used for statistical analysis. Results In the 25 patients who underwent the reduced-dose protocol, mean decrease in CTDIvol was 46% (range, 19%–65%) and mean decrease in SSDE was 44% (range, 19%–64%). Reduced-dose MBIR images had less noise (P > .004). Spatial resolution was superior for reduced-dose MBIR images. Reduced-dose MBIR images were equivalent to standard-dose images for lungs and soft tissues (P > .05) but were inferior for bones (P = .004). Reduced-dose 100% ASIR images were inferior for soft tissues (P < .002), lungs (P < .001), and bones (P < .001). By using the same reduced-dose acquisition, lesion detectability was better (38% [32 of 84 rated lesions]) or the same (62% [52 of 84 rated lesions]) with MBIR as compared with 100% ASIR. Conclusion CT performed with a reduced-dose protocol and MBIR is feasible in the pediatric population, and it maintains diagnostic quality. © RSNA, 2013 Online supplemental material is available for this article. PMID:24091359

Summary Statistics for Homemade ?Play Dough? -- Data Acquired at LLNL

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

Kallman, J S; Morales, K E; Whipple, R E

Using x-ray computerized tomography (CT), we have characterized the x-ray linear attenuation coefficients (LAC) of a homemade Play Dough{trademark}-like material, designated as PDA. Table 1 gives the first-order statistics for each of four CT measurements, estimated with a Gaussian kernel density estimator (KDE) analysis. The mean values of the LAC range from a high of about 2700 LMHU{sub D} 100kVp to a low of about 1200 LMHUD at 300kVp. The standard deviation of each measurement is around 10% to 15% of the mean. The entropy covers the range from 6.0 to 7.4. Ordinarily, we would model the LAC of themore » material and compare the modeled values to the measured values. In this case, however, we did not have the detailed chemical composition of the material and therefore did not model the LAC. Using a method recently proposed by Lawrence Livermore National Laboratory (LLNL), we estimate the value of the effective atomic number, Z{sub eff}, to be near 10. LLNL prepared about 50mL of the homemade 'Play Dough' in a polypropylene vial and firmly compressed it immediately prior to the x-ray measurements. We used the computer program IMGREC to reconstruct the CT images. The values of the key parameters used in the data capture and image reconstruction are given in this report. Additional details may be found in the experimental SOP and a separate document. To characterize the statistical distribution of LAC values in each CT image, we first isolated an 80% central-core segment of volume elements ('voxels') lying completely within the specimen, away from the walls of the polypropylene vial. All of the voxels within this central core, including those comprised of voids and inclusions, are included in the statistics. We then calculated the mean value, standard deviation and entropy for (a) the four image segments and for (b) their digital gradient images. (A digital gradient image of a given image was obtained by taking the absolute value of the difference between the initial image and that same image offset by one voxel horizontally, parallel to the rows of the x-ray detector array.) The statistics of the initial image of LAC values are called 'first order statistics;' those of the gradient image, 'second order statistics.'« less

Stochastic univariate and multivariate time series analysis of PM2.5 and PM10 air pollution: A comparative case study for Plovdiv and Asenovgrad, Bulgaria

NASA Astrophysics Data System (ADS)

Gocheva-Ilieva, S.; Stoimenova, M.; Ivanov, A.; Voynikova, D.; Iliev, I.

2016-10-01

Fine particulate matter PM2.5 and PM10 air pollutants are a serious problem in many urban areas affecting both the health of the population and the environment as a whole. The availability of large data arrays for the levels of these pollutants makes it possible to perform statistical analysis, to obtain relevant information, and to find patterns within the data. Research in this field is particularly topical for a number of Bulgarian cities, European country, where in recent years regulatory air pollution health limits are constantly being exceeded. This paper examines average daily data for air pollution with PM2.5 and PM10, collected by 3 monitoring stations in the cities of Plovdiv and Asenovgrad between 2011 and 2016. The goal is to find and analyze actual relationships in data time series, to build adequate mathematical models, and to develop short-term forecasts. Modeling is carried out by stochastic univariate and multivariate time series analysis, based on Box-Jenkins methodology. The best models are selected following initial transformation of the data and using a set of standard and robust statistical criteria. The Mathematica and SPSS software were used to perform calculations. This examination showed measured concentrations of PM2.5 and PM10 in the region of Plovdiv and Asenovgrad regularly exceed permissible European and national health and safety thresholds. We obtained adequate stochastic models with high statistical fit with the data and good quality forecasting when compared against actual measurements. The mathematical approach applied provides an independent alternative to standard official monitoring and control means for air pollution in urban areas.

Characterizing and Addressing the Need for Statistical Adjustment of Global Climate Model Data

NASA Astrophysics Data System (ADS)

White, K. D.; Baker, B.; Mueller, C.; Villarini, G.; Foley, P.; Friedman, D.

2017-12-01

As part of its mission to research and measure the effects of the changing climate, the U. S. Army Corps of Engineers (USACE) regularly uses the World Climate Research Programme's Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model dataset. However, these data are generated at a global level and are not fine-tuned for specific watersheds. This often causes CMIP5 output to vary from locally observed patterns in the climate. Several downscaling methods have been developed to increase the resolution of the CMIP5 data and decrease systemic differences to support decision-makers as they evaluate results at the watershed scale. Evaluating preliminary comparisons of observed and projected flow frequency curves over the US revealed a simple framework for water resources decision makers to plan and design water resources management measures under changing conditions using standard tools. Using this framework as a basis, USACE has begun to explore to use of statistical adjustment to alter global climate model data to better match the locally observed patterns while preserving the general structure and behavior of the model data. When paired with careful measurement and hypothesis testing, statistical adjustment can be particularly effective at navigating the compromise between the locally observed patterns and the global climate model structures for decision makers.

Stochastic Individual-Based Modeling of Bacterial Growth and Division Using Flow Cytometry.

PubMed

García, Míriam R; Vázquez, José A; Teixeira, Isabel G; Alonso, Antonio A

2017-01-01

A realistic description of the variability in bacterial growth and division is critical to produce reliable predictions of safety risks along the food chain. Individual-based modeling of bacteria provides the theoretical framework to deal with this variability, but it requires information about the individual behavior of bacteria inside populations. In this work, we overcome this problem by estimating the individual behavior of bacteria from population statistics obtained with flow cytometry. For this objective, a stochastic individual-based modeling framework is defined based on standard assumptions during division and exponential growth. The unknown single-cell parameters required for running the individual-based modeling simulations, such as cell size growth rate, are estimated from the flow cytometry data. Instead of using directly the individual-based model, we make use of a modified Fokker-Plank equation. This only equation simulates the population statistics in function of the unknown single-cell parameters. We test the validity of the approach by modeling the growth and division of Pediococcus acidilactici within the exponential phase. Estimations reveal the statistics of cell growth and division using only data from flow cytometry at a given time. From the relationship between the mother and daughter volumes, we also predict that P. acidilactici divide into two successive parallel planes.

Accurate Modeling of Galaxy Clustering on Small Scales: Testing the Standard ΛCDM + Halo Model

NASA Astrophysics Data System (ADS)

Sinha, Manodeep; Berlind, Andreas A.; McBride, Cameron; Scoccimarro, Roman

2015-01-01

The large-scale distribution of galaxies can be explained fairly simply by assuming (i) a cosmological model, which determines the dark matter halo distribution, and (ii) a simple connection between galaxies and the halos they inhabit. This conceptually simple framework, called the halo model, has been remarkably successful at reproducing the clustering of galaxies on all scales, as observed in various galaxy redshift surveys. However, none of these previous studies have carefully modeled the systematics and thus truly tested the halo model in a statistically rigorous sense. We present a new accurate and fully numerical halo model framework and test it against clustering measurements from two luminosity samples of galaxies drawn from the SDSS DR7. We show that the simple ΛCDM cosmology + halo model is not able to simultaneously reproduce the galaxy projected correlation function and the group multiplicity function. In particular, the more luminous sample shows significant tension with theory. We discuss the implications of our findings and how this work paves the way for constraining galaxy formation by accurate simultaneous modeling of multiple galaxy clustering statistics.

Predicted nitrate and arsenic concentrations in basin-fill aquifers of the Southwestern United States

USGS Publications Warehouse

Anning, David W.; Paul, Angela P.; McKinney, Tim S.; Huntington, Jena M.; Bexfield, Laura M.; Thiros, Susan A.

2012-01-01

The National Water-Quality Assessment (NAWQA) Program of the U.S. Geological Survey (USGS) is conducting a regional analysis of water quality in the principal aquifer systems across the United States. The Southwest Principal Aquifers (SWPA) study is building a better understanding of the susceptibility and vulnerability of basin-fill aquifers in the region to groundwater contamination by synthesizing baseline knowledge of groundwater-quality conditions in 16 basins previously studied by the NAWQA Program. The improved understanding of aquifer susceptibility and vulnerability to contamination is assisting in the development of tools that water managers can use to assess and protect the quality of groundwater resources.Human-health concerns and economic considerations associated with meeting drinking-water standards motivated a study of the vulnerability of basin-fill aquifers to nitrate con­tamination and arsenic enrichment in the southwestern United States. Statistical models were developed by using the random forest classifier algorithm to predict concentrations of nitrate and arsenic across a model grid that represents about 190,600 square miles of basin-fill aquifers in parts of Arizona, California, Colorado, Nevada, New Mexico, and Utah. The statistical models, referred to as classifiers, reflect natural and human-related factors that affect aquifer vulnerability to contamina­tion and relate nitrate and arsenic concentrations to explana­tory variables representing local- and basin-scale measures of source, aquifer susceptibility, and geochemical conditions. The classifiers were unbiased and fit the observed data well, and misclassifications were primarily due to statistical sampling error in the training datasets.The classifiers were designed to predict concentrations to be in one of six classes for nitrate, and one of seven classes for arsenic. Each classification scheme allowed for identification of areas with concentrations that were equal to or exceeding the U.S. Environmental Protection Agency drinking-water standard. Whereas 2.4 percent of the area underlain by basin-fill aquifers in the study area was predicted to equal or exceed this standard for nitrate (10 milligrams per liter as N; mg/L), 42.7 percent was predicted to equal or exceed the standard for arsenic (10 micrograms per liter; μg/L). Areas predicted to equal or exceed the drinking-water standard for nitrate include basins in central Arizona near Phoenix; the San Joaquin, Inland, and San Jacinto basins of California; and the San Luis Valley of Colorado. Much of the area predicted to equal or exceed the drinking-water standard for arsenic is within a belt of basins along the western portion of the Basin and Range Physiographic Province in Nevada, California, and Arizona. Predicted nitrate and arsenic concentrations are substantially lower than the drinking-water standards in much of the study area—about 93.0 percent of the area underlain by basin-fill aquifers was less than one-half the standard for nitrate (5.0 mg/L), and 50.2 percent was less than one-half the standard for arsenic (5.0 μg/L).

Quantitative determination of amorphous cyclosporine in crystalline cyclosporine samples by Fourier transform infrared spectroscopy.

PubMed

Bertacche, Vittorio; Pini, Elena; Stradi, Riccardo; Stratta, Fabio

2006-01-01

The purpose of this study is the development of a quantification method to detect the amount of amorphous cyclosporine using Fourier transform infrared (FTIR) spectroscopy. The mixing of different percentages of crystalline cyclosporine with amorphous cyclosporine was used to obtain a set of standards, composed of cyclosporine samples characterized by different percentages of amorphous cyclosporine. Using a wavelength range of 450-4,000 cm(-1), FTIR spectra were obtained from samples in potassium bromide pellets and then a partial least squares (PLS) model was exploited to correlate the features of the FTIR spectra with the percentage of amorphous cyclosporine in the samples. This model gave a standard error of estimate (SEE) of 0.3562, with an r value of 0.9971 and a standard error of prediction (SEP) of 0.4168, which derives from the cross validation function used to check the precision of the model. Statistical values reveal the applicability of the method to the quantitative determination of amorphous cyclosporine in crystalline cyclosporine samples.

Large-angle correlations in the cosmic microwave background

NASA Astrophysics Data System (ADS)

Efstathiou, George; Ma, Yin-Zhe; Hanson, Duncan

2010-10-01

It has been argued recently by Copi et al. 2009 that the lack of large angular correlations of the CMB temperature field provides strong evidence against the standard, statistically isotropic, inflationary Lambda cold dark matter (ΛCDM) cosmology. We compare various estimators of the temperature correlation function showing how they depend on assumptions of statistical isotropy and how they perform on the Wilkinson Microwave Anisotropy Probe (WMAP) 5-yr Internal Linear Combination (ILC) maps with and without a sky cut. We show that the low multipole harmonics that determine the large-scale features of the temperature correlation function can be reconstructed accurately from the data that lie outside the sky cuts. The reconstructions are only weakly dependent on the assumed statistical properties of the temperature field. The temperature correlation functions computed from these reconstructions are in good agreement with those computed from the ILC map over the whole sky. We conclude that the large-scale angular correlation function for our realization of the sky is well determined. A Bayesian analysis of the large-scale correlations is presented, which shows that the data cannot exclude the standard ΛCDM model. We discuss the differences between our results and those of Copi et al. Either there exists a violation of statistical isotropy as claimed by Copi et al., or these authors have overestimated the significance of the discrepancy because of a posteriori choices of estimator, statistic and sky cut.

Manipulating measurement scales in medical statistical analysis and data mining: A review of methodologies

PubMed Central

Marateb, Hamid Reza; Mansourian, Marjan; Adibi, Peyman; Farina, Dario

2014-01-01

Background: selecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. Different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. We have presented two ordinal–variables clustering examples, as more challenging variable in analysis, using Wisconsin Breast Cancer Data (WBCD). Ordinal-to-Interval scale conversion example: a breast cancer database of nine 10-level ordinal variables for 683 patients was analyzed by two ordinal-scale clustering methods. The performance of the clustering methods was assessed by comparison with the gold standard groups of malignant and benign cases that had been identified by clinical tests. Results: the sensitivity and accuracy of the two clustering methods were 98% and 96%, respectively. Their specificity was comparable. Conclusion: by using appropriate clustering algorithm based on the measurement scale of the variables in the study, high performance is granted. Moreover, descriptive and inferential statistics in addition to modeling approach must be selected based on the scale of the variables. PMID:24672565

Statistical models for estimating daily streamflow in Michigan

USGS Publications Warehouse

Holtschlag, D.J.; Salehi, Habib

1992-01-01

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

Long-term changes (1980-2003) in total ozone time series over Northern Hemisphere midlatitudes

NASA Astrophysics Data System (ADS)

Białek, Małgorzata

2006-03-01

Long-term changes in total ozone time series for Arosa, Belsk, Boulder and Sapporo stations are examined. For each station we analyze time series of the following statistical characteristics of the distribution of daily ozone data: seasonal mean, standard deviation, maximum and minimum of total daily ozone values for all seasons. The iterative statistical model is proposed to estimate trends and long-term changes in the statistical distribution of the daily total ozone data. The trends are calculated for the period 1980-2003. We observe lessening of negative trends in the seasonal means as compared to those calculated by WMO for 1980-2000. We discuss a possibility of a change of the distribution shape of ozone daily data using the Kolmogorov-Smirnov test and comparing trend values in the seasonal mean, standard deviation, maximum and minimum time series for the selected stations and seasons. The distribution shift toward lower values without a change in the distribution shape is suggested with the following exceptions: the spreading of the distribution toward lower values for Belsk during winter and no decisive result for Sapporo and Boulder in summer.

Meta-analysis for the comparison of two diagnostic tests to a common gold standard: A generalized linear mixed model approach.

PubMed

Hoyer, Annika; Kuss, Oliver

2018-05-01

Meta-analysis of diagnostic studies is still a rapidly developing area of biostatistical research. Especially, there is an increasing interest in methods to compare different diagnostic tests to a common gold standard. Restricting to the case of two diagnostic tests, in these meta-analyses the parameters of interest are the differences of sensitivities and specificities (with their corresponding confidence intervals) between the two diagnostic tests while accounting for the various associations across single studies and between the two tests. We propose statistical models with a quadrivariate response (where sensitivity of test 1, specificity of test 1, sensitivity of test 2, and specificity of test 2 are the four responses) as a sensible approach to this task. Using a quadrivariate generalized linear mixed model naturally generalizes the common standard bivariate model of meta-analysis for a single diagnostic test. If information on several thresholds of the tests is available, the quadrivariate model can be further generalized to yield a comparison of full receiver operating characteristic (ROC) curves. We illustrate our model by an example where two screening methods for the diagnosis of type 2 diabetes are compared.

Approaches to setting organism-based ballast water discharge standards

USGS Publications Warehouse

Lee, Henry; Reusser, Deborah A.; Frazier, Melanie

2013-01-01

As a vector by which foreign species invade coastal and freshwater waterbodies, ballast water discharge from ships is recognized as a major environmental threat. The International Maritime Organization (IMO) drafted an international treaty establishing ballast water discharge standards based on the number of viable organisms per volume of ballast discharge for different organism size classes. Concerns that the IMO standards are not sufficiently protective have initiated several state and national efforts in the United States to develop more stringent standards. We evaluated seven approaches to establishing discharge standards for the >50-μm size class: (1) expert opinion/management consensus, (2) zero detectable living organisms, (3) natural invasion rates, (4) reaction–diffusion models, (5) population viability analysis (PVA) models, (6) per capita invasion probabilities (PCIP), and (7) experimental studies. Because of the difficulty in synthesizing scientific knowledge in an unbiased and transparent fashion, we recommend the use of quantitative models instead of expert opinion. The actual organism concentration associated with a “zero detectable organisms” standard is defined by the statistical rigor of its monitoring program; thus it is not clear whether such a standard is as stringent as other standards. For several reasons, the natural invasion rate, reaction–diffusion, and experimental approaches are not considered suitable for generating discharge standards. PVA models can be used to predict the likelihood of establishment of introduced species but are limited by a lack of population vital rates for species characteristic of ballast water discharges. Until such rates become available, PVA models are better suited to evaluate relative efficiency of proposed standards rather than predicting probabilities of invasion. The PCIP approach, which is based on historical invasion rates at a regional scale, appears to circumvent many of the indicated problems, although it may underestimate invasions by asexual and parthenogenic species. Further research is needed to better define propagule dose–responses, densities at which Allee effects occur, approaches to predicting the likelihood of invasion from multi-species introductions, and generation of formal comparisons of approaches using standardized scenarios.

Evaluation of liquefaction potential of soil based on standard penetration test using multi-gene genetic programming model

NASA Astrophysics Data System (ADS)

Muduli, Pradyut; Das, Sarat

2014-06-01

This paper discusses the evaluation of liquefaction potential of soil based on standard penetration test (SPT) dataset using evolutionary artificial intelligence technique, multi-gene genetic programming (MGGP). The liquefaction classification accuracy (94.19%) of the developed liquefaction index (LI) model is found to be better than that of available artificial neural network (ANN) model (88.37%) and at par with the available support vector machine (SVM) model (94.19%) on the basis of the testing data. Further, an empirical equation is presented using MGGP to approximate the unknown limit state function representing the cyclic resistance ratio (CRR) of soil based on developed LI model. Using an independent database of 227 cases, the overall rates of successful prediction of occurrence of liquefaction and non-liquefaction are found to be 87, 86, and 84% by the developed MGGP based model, available ANN and the statistical models, respectively, on the basis of calculated factor of safety (F s) against the liquefaction occurrence.

Student achievement in science and mathematics on campuses that have implemented the CSCOPE curriculum model

NASA Astrophysics Data System (ADS)

Wilson, Emily R.

The purpose of this study was to determine whether differences in student achievement exist between school campuses which followed a specific standards-based curriculum model (CSCOPE) and school campuses which followed a non-CSCOPE or traditional curriculum model. One-hundred and sixty CSCOPE curriculum campuses and 160 non-CSCOPE curriculum campuses were used in the study. Achievement data were collected on students in the fifth, eighth, and eleventh grades using the campuses percentage passing on the Texas Assessment of Knowledge and Skills (TAKS) for both science and mathematics. The TAKS is the state-mandated assessment system used to comply with federal testing guidelines. Data for the 2007-2008 school year were used for the elementary level while data from 2006-2007 and 2007-2008 were used for junior high (middle school) and high school levels. Data were analyzed by overall class as well as aggregated by ethnic classifications. Descriptive statistics were used to summarize achievement results and t-tests were utilized to analyze achievement differences between the two curriculum models. Overall fifth grade students in CSCOPE schools outperformed (p < .05) non-CSCOPE counterparts in science and mathematics. Also, fifth grade Hispanic students using CSCOPE curriculum scored higher (p < .05) than those in traditional curricula. Eighth grade students in CSCOPE schools performed better (p < .05) in science than students in non-CSCOPE schools. Finally, eighth grade Hispanic and White subgroups using CSCOPE curriculum outperformed ( p < .05) their ethnic counterparts using traditional curriculum models. The only statistically significant finding at the eleventh grade level was the African-American subgroup in science, but this subgroup had too small of a sample to infer the findings to the population. Thus, the results would tend to support use of the standardized curriculum model (CSCOPE) at lower levels whereas achievement in high school may not be differentially affected by the standardized model.

Observation of t t ¯ H Production

NASA Astrophysics Data System (ADS)

Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Ambrogi, F.; Asilar, E.; Bergauer, T.; Brandstetter, J.; Dragicevic, M.; Erö, J.; Escalante Del Valle, A.; Flechl, M.; Frühwirth, R.; Ghete, V. M.; Hrubec, J.; Jeitler, M.; Krammer, N.; Krätschmer, I.; Liko, D.; Madlener, T.; Mikulec, I.; Rad, N.; Rohringer, H.; Schieck, J.; Schöfbeck, R.; Spanring, M.; Spitzbart, D.; Taurok, A.; Waltenberger, W.; Wittmann, J.; Wulz, C.-E.; Zarucki, M.; Chekhovsky, V.; Mossolov, V.; Suarez Gonzalez, J.; De Wolf, E. A.; Di Croce, D.; Janssen, X.; Lauwers, J.; Pieters, M.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Abu Zeid, S.; Blekman, F.; D'Hondt, J.; De Bruyn, I.; De Clercq, J.; Deroover, K.; Flouris, G.; Lontkovskyi, D.; Lowette, S.; Marchesini, I.; Moortgat, S.; Moreels, L.; Python, Q.; Skovpen, K.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Parijs, I.; Beghin, D.; Bilin, B.; Brun, H.; Clerbaux, B.; De Lentdecker, G.; Delannoy, H.; Dorney, B.; Fasanella, G.; Favart, L.; Goldouzian, R.; Grebenyuk, A.; Kalsi, A. K.; Lenzi, T.; Luetic, J.; Postiau, N.; Starling, E.; Thomas, L.; Vander Velde, C.; Vanlaer, P.; Vannerom, D.; Wang, Q.; Cornelis, T.; Dobur, D.; Fagot, A.; Gul, M.; Khvastunov, I.; Poyraz, D.; Roskas, C.; Trocino, D.; Tytgat, M.; Verbeke, W.; Vermassen, B.; Vit, M.; Zaganidis, N.; Bakhshiansohi, H.; Bondu, O.; Brochet, S.; Bruno, G.; Caputo, C.; David, P.; Delaere, C.; Delcourt, M.; Francois, B.; Giammanco, A.; Krintiras, G.; Lemaitre, V.; Magitteri, A.; Mertens, A.; Musich, M.; Piotrzkowski, K.; Saggio, A.; Vidal Marono, M.; Wertz, S.; Zobec, J.; Alves, F. L.; Alves, G. A.; Brito, L.; Correa Martins Junior, M.; Correia Silva, G.; Hensel, C.; Moraes, A.; Pol, M. E.; Rebello Teles, P.; Belchior Batista Das Chagas, E.; Carvalho, W.; Chinellato, J.; Coelho, E.; Da Costa, E. M.; Da Silveira, G. G.; De Jesus Damiao, D.; De Oliveira Martins, C.; Fonseca De Souza, S.; Malbouisson, H.; Matos Figueiredo, D.; Melo De Almeida, M.; Mora Herrera, C.; Mundim, L.; Nogima, H.; Prado Da Silva, W. L.; Sanchez Rosas, L. J.; Santoro, A.; Sznajder, A.; Thiel, M.; Tonelli Manganote, E. J.; Torres Da Silva De Araujo, F.; Vilela Pereira, A.; Ahuja, S.; Bernardes, C. A.; Calligaris, L.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Mercadante, P. G.; Novaes, S. F.; Padula, Sandra S.; Romero Abad, D.; Aleksandrov, A.; Hadjiiska, R.; Iaydjiev, P.; Marinov, A.; Misheva, M.; Rodozov, M.; Shopova, M.; Sultanov, G.; Dimitrov, A.; Litov, L.; Pavlov, B.; Petkov, P.; Fang, W.; Gao, X.; Yuan, L.; Ahmad, M.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Chen, Y.; Jiang, C. H.; Leggat, D.; Liao, H.; Liu, Z.; Romeo, F.; Shaheen, S. M.; Spiezia, A.; Tao, J.; Wang, C.; Wang, Z.; Yazgan, E.; Zhang, H.; Zhao, J.; Ban, Y.; Chen, G.; Levin, A.; Li, J.; Li, L.; Li, Q.; Mao, Y.; Qian, S. J.; Wang, D.; Xu, Z.; Wang, Y.; Avila, C.; Cabrera, A.; Carrillo Montoya, C. A.; Chaparro Sierra, L. F.; Florez, C.; González Hernández, C. F.; Segura Delgado, M. A.; Courbon, B.; Godinovic, N.; Lelas, D.; Puljak, I.; Sculac, T.; Antunovic, Z.; Kovac, M.; Brigljevic, V.; Ferencek, D.; Kadija, K.; Mesic, B.; Starodumov, A.; Susa, T.; Ather, M. W.; Attikis, A.; Kolosova, M.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Rykaczewski, H.; Finger, M.; Finger, M.; Ayala, E.; Carrera Jarrin, E.; Abdalla, H.; Abdelalim, A. A.; Mohamed, A.; Bhowmik, S.; Carvalho Antunes De Oliveira, A.; Dewanjee, R. K.; Ehataht, K.; Kadastik, M.; Raidal, M.; Veelken, C.; Eerola, P.; Kirschenmann, H.; Pekkanen, J.; Voutilainen, M.; Havukainen, J.; Heikkilä, J. K.; Järvinen, T.; Karimäki, V.; Kinnunen, R.; Lampén, T.; Lassila-Perini, K.; Laurila, S.; Lehti, S.; Lindén, T.; Luukka, P.; Mäenpää, T.; Siikonen, H.; Tuominen, E.; Tuominiemi, J.; Tuuva, T.; Besancon, M.; Couderc, F.; Dejardin, M.; Denegri, D.; Faure, J. L.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; Hamel de Monchenault, G.; Jarry, P.; Leloup, C.; Locci, E.; Malcles, J.; Negro, G.; Rander, J.; Rosowsky, A.; Sahin, M. Ö.; Titov, M.; Abdulsalam, A.; Amendola, C.; Antropov, I.; Beaudette, F.; Busson, P.; Charlot, C.; Granier de Cassagnac, R.; Kucher, I.; Lobanov, A.; Martin Blanco, J.; Nguyen, M.; Ochando, C.; Ortona, G.; Pigard, P.; Salerno, R.; Sauvan, J. B.; Sirois, Y.; Stahl Leiton, A. G.; Zabi, A.; Zghiche, A.; Agram, J.-L.; Andrea, J.; Bloch, D.; Brom, J.-M.; Chabert, E. C.; Cherepanov, V.; Collard, C.; Conte, E.; Fontaine, J.-C.; Gelé, D.; Goerlach, U.; Jansová, M.; Le Bihan, A.-C.; Tonon, N.; Van Hove, P.; Gadrat, S.; Beauceron, S.; Bernet, C.; Boudoul, G.; Chanon, N.; Chierici, R.; Contardo, D.; Depasse, P.; El Mamouni, H.; Fay, J.; Finco, L.; Gascon, S.; Gouzevitch, M.; Grenier, G.; Ille, B.; Lagarde, F.; Laktineh, I. B.; Lattaud, H.; Lethuillier, M.; Mirabito, L.; Pequegnot, A. L.; Perries, S.; Popov, A.; Sordini, V.; Vander Donckt, M.; Viret, S.; Zhang, S.; Khvedelidze, A.; Tsamalaidze, Z.; Autermann, C.; Feld, L.; Kiesel, M. K.; Klein, K.; Lipinski, M.; Preuten, M.; Rauch, M. P.; Schomakers, C.; Schulz, J.; Teroerde, M.; Wittmer, B.; Zhukov, V.; Albert, A.; Duchardt, D.; Endres, M.; Erdmann, M.; Esch, T.; Fischer, R.; Ghosh, S.; Güth, A.; Hebbeker, T.; Heidemann, C.; Hoepfner, K.; Keller, H.; Knutzen, S.; Mastrolorenzo, L.; Merschmeyer, M.; Meyer, A.; Millet, P.; Mukherjee, S.; Pook, T.; Radziej, M.; Rath, Y.; Reithler, H.; Rieger, M.; Scheuch, F.; Schmidt, A.; Teyssier, D.; Flügge, G.; Hlushchenko, O.; Kress, T.; Künsken, A.; Müller, T.; Nehrkorn, A.; Nowack, A.; Pistone, C.; Pooth, O.; Roy, D.; Sert, H.; Stahl, A.; Aldaya Martin, M.; Arndt, T.; Asawatangtrakuldee, C.; Babounikau, I.; Beernaert, K.; Behnke, O.; Behrens, U.; Bermúdez Martínez, A.; Bertsche, D.; Bin Anuar, A. A.; Borras, K.; Botta, V.; Campbell, A.; Connor, P.; Contreras-Campana, C.; Costanza, F.; Danilov, V.; De Wit, A.; Defranchis, M. M.; Diez Pardos, C.; Domínguez Damiani, D.; Eckerlin, G.; Eichhorn, T.; Elwood, A.; Eren, E.; Gallo, E.; Geiser, A.; Grados Luyando, J. M.; Grohsjean, A.; Gunnellini, P.; Guthoff, M.; Haranko, M.; Harb, A.; Hauk, J.; Jung, H.; Kasemann, M.; Keaveney, J.; Kleinwort, C.; Knolle, J.; Krücker, D.; Lange, W.; Lelek, A.; Lenz, T.; Lipka, K.; Lohmann, W.; Mankel, R.; Melzer-Pellmann, I.-A.; Meyer, A. B.; Meyer, M.; Missiroli, M.; Mittag, G.; Mnich, J.; Myronenko, V.; Pflitsch, S. K.; Pitzl, D.; Raspereza, A.; Saibel, A.; Savitskyi, M.; Saxena, P.; Schütze, P.; Schwanenberger, C.; Shevchenko, R.; Singh, A.; Tholen, H.; Turkot, O.; Vagnerini, A.; Van Onsem, G. P.; Walsh, R.; Wen, Y.; Wichmann, K.; Wissing, C.; Zenaiev, O.; Aggleton, R.; Bein, S.; Benato, L.; Benecke, A.; Blobel, V.; Centis Vignali, M.; Dreyer, T.; Garutti, E.; Gonzalez, D.; Haller, J.; Hinzmann, A.; Karavdina, A.; Kasieczka, G.; Klanner, R.; Kogler, R.; Kovalchuk, N.; Kurz, S.; Kutzner, V.; Lange, J.; Marconi, D.; Multhaup, J.; Niedziela, M.; Nowatschin, D.; Perieanu, A.; Reimers, A.; Rieger, O.; Scharf, C.; Schleper, P.; Schumann, S.; Schwandt, J.; Sonneveld, J.; Stadie, H.; Steinbrück, G.; Stober, F. M.; Stöver, M.; Troendle, D.; Vanhoefer, A.; Vormwald, B.; Akbiyik, M.; Barth, C.; Baselga, M.; Baur, S.; Butz, E.; Caspart, R.; Chwalek, T.; Colombo, F.; De Boer, W.; Dierlamm, A.; El Morabit, K.; Faltermann, N.; Freund, B.; Giffels, M.; Harrendorf, M. A.; Hartmann, F.; Heindl, S. M.; Husemann, U.; Kassel, F.; Katkov, I.; Keicher, P.; Kudella, S.; Mildner, H.; Mitra, S.; Mozer, M. U.; Müller, Th.; Plagge, M.; Quast, G.; Rabbertz, K.; Schröder, M.; Shvetsov, I.; Sieber, G.; Simonis, H. J.; Ulrich, R.; Wayand, S.; Waßmer, M.; Weber, M.; Weiler, T.; Williamson, S.; Wöhrmann, C.; Wolf, R.; Anagnostou, G.; Daskalakis, G.; Geralis, T.; Kyriakis, A.; Loukas, D.; Paspalaki, G.; Topsis-Giotis, I.; Karathanasis, G.; Kesisoglou, S.; Kontaxakis, P.; Panagiotou, A.; Papavergou, I.; Saoulidou, N.; Tziaferi, E.; Vellidis, K.; Kousouris, K.; Papakrivopoulos, I.; Tsipolitis, G.; Evangelou, I.; Foudas, C.; Gianneios, P.; Katsoulis, P.; Kokkas, P.; Mallios, S.; Manthos, N.; Papadopoulos, I.; Paradas, E.; Strologas, J.; Triantis, F. A.; Tsitsonis, D.; Bartók, M.; Csanad, M.; Filipovic, N.; Major, P.; Nagy, M. I.; Pasztor, G.; Surányi, O.; Veres, G. I.; Bencze, G.; Hajdu, C.; Horvath, D.; Hunyadi, Á.; Sikler, F.; Veszpremi, V.; Vesztergombi, G.; Vámi, T. Á.; Beni, N.; Czellar, S.; Karancsi, J.; Makovec, A.; Molnar, J.; Szillasi, Z.; Raics, P.; Trocsanyi, Z. L.; Ujvari, B.; Choudhury, S.; Komaragiri, J. R.; Tiwari, P. C.; Bahinipati, S.; Kar, C.; Mal, P.; Mandal, K.; Nayak, A.; Sahoo, D. K.; Swain, S. K.; Bansal, S.; Beri, S. B.; Bhatnagar, V.; Chauhan, S.; Chawla, R.; Dhingra, N.; Gupta, R.; Kaur, A.; Kaur, A.; Kaur, M.; Kaur, S.; Kumar, R.; Kumari, P.; Lohan, M.; Mehta, A.; Sandeep, K.; Sharma, S.; Singh, J. B.; Walia, G.; Kumar, Ashok; Shah, Aashaq; Bhardwaj, A.; Choudhary, B. C.; Garg, R. B.; Gola, M.; Keshri, S.; Malhotra, S.; Naimuddin, M.; Priyanka, P.; Ranjan, K.; Sharma, R.; Bhardwaj, R.; Bharti, M.; Bhattacharya, R.; Bhattacharya, S.; Bhawandeep, U.; Bhowmik, D.; Dey, S.; Dutt, S.; Dutta, S.; Ghosh, S.; Mondal, K.; Nandan, S.; Purohit, A.; Rout, P. K.; Roy, A.; Roy Chowdhury, S.; Saha, G.; Sarkar, S.; Sharan, M.; Singh, B.; Thakur, S.; Behera, P. K.; Chudasama, R.; Dutta, D.; Jha, V.; Kumar, V.; Netrakanti, P. K.; Pant, L. M.; Shukla, P.; Kumar Verma, Ravindra; Aziz, T.; Bhat, M. A.; Dugad, S.; Mohanty, G. B.; Sur, N.; Sutar, B.; Banerjee, S.; Bhattacharya, S.; Chatterjee, S.; Das, P.; Guchait, M.; Jain, Sa.; Karmakar, S.; Kumar, S.; Maity, M.; Majumder, G.; Mazumdar, K.; Sahoo, N.; Sarkar, T.; Chauhan, S.; Dube, S.; Hegde, V.; Kapoor, A.; Kothekar, K.; Pandey, S.; Rane, A.; Sharma, S.; Chenarani, S.; Eskandari Tadavani, E.; Etesami, S. M.; Khakzad, M.; Mohammadi Najafabadi, M.; Naseri, M.; Rezaei Hosseinabadi, F.; Safarzadeh, B.; Zeinali, M.; Felcini, M.; Grunewald, M.; Abbrescia, M.; Calabria, C.; Colaleo, A.; Creanza, D.; Cristella, L.; De Filippis, N.; De Palma, M.; Di Florio, A.; Errico, F.; Fiore, L.; Gelmi, A.; Iaselli, G.; Ince, M.; Lezki, S.; Maggi, G.; Maggi, M.; Miniello, G.; My, S.; Nuzzo, S.; Pompili, A.; Pugliese, G.; Radogna, R.; Ranieri, A.; Selvaggi, G.; Sharma, A.; Silvestris, L.; Venditti, R.; Verwilligen, P.; Zito, G.; Abbiendi, G.; Battilana, C.; Bonacorsi, D.; Borgonovi, L.; Braibant-Giacomelli, S.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Chhibra, S. S.; Ciocca, C.; Codispoti, G.; Cuffiani, M.; Dallavalle, G. M.; Fabbri, F.; Fanfani, A.; Giacomelli, P.; Grandi, C.; Guiducci, L.; Iemmi, F.; Marcellini, S.; Masetti, G.; Montanari, A.; Navarria, F. L.; Perrotta, A.; Primavera, F.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.; Tosi, N.; Albergo, S.; Di Mattia, A.; Potenza, R.; Tricomi, A.; Tuve, C.; Barbagli, G.; Chatterjee, K.; Ciulli, V.; Civinini, C.; D'Alessandro, R.; Focardi, E.; Latino, G.; Lenzi, P.; Meschini, M.; Paoletti, S.; Russo, L.; Sguazzoni, G.; Strom, D.; Viliani, L.; Benussi, L.; Bianco, S.; Fabbri, F.; Piccolo, D.; Ferro, F.; Ravera, F.; Robutti, E.; Tosi, S.; Benaglia, A.; Beschi, A.; Brianza, L.; Brivio, F.; Ciriolo, V.; Di Guida, S.; Dinardo, M. E.; Fiorendi, S.; Gennai, S.; Ghezzi, A.; Govoni, P.; Malberti, M.; Malvezzi, S.; Massironi, A.; Menasce, D.; Moroni, L.; Paganoni, M.; Pedrini, D.; Ragazzi, S.; Tabarelli de Fatis, T.; Zuolo, D.; Buontempo, S.; Cavallo, N.; Di Crescenzo, A.; Fabozzi, F.; Fienga, F.; Galati, G.; Iorio, A. O. M.; Khan, W. A.; Lista, L.; Meola, S.; Paolucci, P.; Sciacca, C.; Voevodina, E.; Azzi, P.; Bacchetta, N.; Bisello, D.; Boletti, A.; Bragagnolo, A.; Carlin, R.; Checchia, P.; Dall'Osso, M.; De Castro Manzano, P.; Dorigo, T.; Fanzago, F.; Gasparini, U.; Gozzelino, A.; Hoh, S. Y.; Lacaprara, S.; Lujan, P.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Pozzobon, N.; Ronchese, P.; Rossin, R.; Simonetto, F.; Tiko, A.; Torassa, E.; Zanetti, M.; Zotto, P.; Zumerle, G.; Braghieri, A.; Magnani, A.; Montagna, P.; Ratti, S. P.; Re, V.; Ressegotti, M.; Riccardi, C.; Salvini, P.; Vai, I.; Vitulo, P.; Alunni Solestizi, L.; Biasini, M.; Bilei, G. M.; Cecchi, C.; Ciangottini, D.; Fanò, L.; Lariccia, P.; Leonardi, R.; Manoni, E.; Mantovani, G.; Mariani, V.; Menichelli, M.; Rossi, A.; Santocchia, A.; Spiga, D.; Androsov, K.; Azzurri, P.; Bagliesi, G.; Bianchini, L.; Boccali, T.; Borrello, L.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Fedi, G.; Fiori, F.; Giannini, L.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Manca, E.; Mandorli, G.; Messineo, A.; Palla, F.; Rizzi, A.; Spagnolo, P.; Tenchini, R.; Tonelli, G.; Venturi, A.; Verdini, P. 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G.; Heideman, J.; Riley, G.; Spanier, S.; Thapa, K.; Bouhali, O.; Celik, A.; Dalchenko, M.; De Mattia, M.; Delgado, A.; Dildick, S.; Eusebi, R.; Gilmore, J.; Huang, T.; Kamon, T.; Luo, S.; Mueller, R.; Patel, R.; Perloff, A.; Perniè, L.; Rathjens, D.; Safonov, A.; Akchurin, N.; Damgov, J.; De Guio, F.; Dudero, P. R.; Kunori, S.; Lamichhane, K.; Lee, S. W.; Mengke, T.; Muthumuni, S.; Peltola, T.; Undleeb, S.; Volobouev, I.; Wang, Z.; Greene, S.; Gurrola, A.; Janjam, R.; Johns, W.; Maguire, C.; Melo, A.; Ni, H.; Padeken, K.; Ruiz Alvarez, J. D.; Sheldon, P.; Tuo, S.; Velkovska, J.; Verweij, M.; Xu, Q.; Arenton, M. W.; Barria, P.; Cox, B.; Hirosky, R.; Joyce, M.; Ledovskoy, A.; Li, H.; Neu, C.; Sinthuprasith, T.; Wang, Y.; Wolfe, E.; Xia, F.; Harr, R.; Karchin, P. E.; Poudyal, N.; Sturdy, J.; Thapa, P.; Zaleski, S.; Brodski, M.; Buchanan, J.; Caillol, C.; Carlsmith, D.; Dasu, S.; Dodd, L.; Gomber, B.; Grothe, M.; Herndon, M.; Hervé, A.; Hussain, U.; Klabbers, P.; Lanaro, A.; Long, K.; Loveless, R.; Ruggles, T.; Savin, A.; Smith, N.; Smith, W. H.; Woods, N.; CMS Collaboration

2018-06-01

The observation of Higgs boson production in association with a top quark-antiquark pair is reported, based on a combined analysis of proton-proton collision data at center-of-mass energies of √{s }=7 , 8, and 13 TeV, corresponding to integrated luminosities of up to 5.1, 19.7, and 35.9 fb-1, respectively. The data were collected with the CMS detector at the CERN LHC. The results of statistically independent searches for Higgs bosons produced in conjunction with a top quark-antiquark pair and decaying to pairs of W bosons, Z bosons, photons, τ leptons, or bottom quark jets are combined to maximize sensitivity. An excess of events is observed, with a significance of 5.2 standard deviations, over the expectation from the background-only hypothesis. The corresponding expected significance from the standard model for a Higgs boson mass of 125.09 GeV is 4.2 standard deviations. The combined best fit signal strength normalized to the standard model prediction is 1.26-0.26+0.31 .

Observation of tt[over ¯]H Production.

PubMed

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Jessop, C; Karmgard, D J; Kellams, N; Lannon, K; Li, W; Loukas, N; Marinelli, N; Meng, F; Mueller, C; Musienko, Y; Planer, M; Reinsvold, A; Ruchti, R; Siddireddy, P; Smith, G; Taroni, S; Wayne, M; Wightman, A; Wolf, M; Woodard, A; Alimena, J; Antonelli, L; Bylsma, B; Durkin, L S; Flowers, S; Francis, B; Hart, A; Hill, C; Ji, W; Lefeld, A; Ling, T Y; Luo, W; Winer, B L; Wulsin, H W; Cooperstein, S; Elmer, P; Hardenbrook, J; Higginbotham, S; Kalogeropoulos, A; Lange, D; Lucchini, M T; Luo, J; Marlow, D; Mei, K; Ojalvo, I; Olsen, J; Palmer, C; Piroué, P; Salfeld-Nebgen, J; Stickland, D; Tully, C; Malik, S; Norberg, S; Barker, A; Barnes, V E; Gutay, L; Jones, M; Jung, A W; Khatiwada, A; Mahakud, B; Miller, D H; Neumeister, N; Peng, C C; Qiu, H; Schulte, J F; Sun, J; Wang, F; Xiao, R; Xie, W; Cheng, T; Dolen, J; Parashar, N; Chen, Z; Ecklund, K M; Freed, S; Geurts, F J M; Kilpatrick, M; Li, W; Michlin, B; Padley, B P; Roberts, J; Rorie, J; Shi, W; Tu, Z; Zabel, J; Zhang, A; Bodek, A; de Barbaro, P; Demina, R; Duh, Y T; Dulemba, J L; Fallon, C; Ferbel, T; Galanti, M; Garcia-Bellido, A; Han, J; Hindrichs, O; Khukhunaishvili, A; Lo, K H; Tan, P; Taus, R; Verzetti, M; Agapitos, A; Chou, J P; Gershtein, Y; Gómez Espinosa, T A; Halkiadakis, E; Heindl, M; Hughes, E; Kaplan, S; Kunnawalkam Elayavalli, R; Kyriacou, S; Lath, A; Montalvo, R; Nash, K; Osherson, M; Saka, H; Salur, S; Schnetzer, S; Sheffield, D; Somalwar, S; Stone, R; Thomas, S; Thomassen, P; Walker, M; Delannoy, A G; Heideman, J; Riley, G; Spanier, S; Thapa, K; Bouhali, O; Celik, A; Dalchenko, M; De Mattia, M; Delgado, A; Dildick, S; Eusebi, R; Gilmore, J; Huang, T; Kamon, T; Luo, S; Mueller, R; Patel, R; Perloff, A; Perniè, L; Rathjens, D; Safonov, A; Akchurin, N; Damgov, J; De Guio, F; Dudero, P R; Kunori, S; Lamichhane, K; Lee, S W; Mengke, T; Muthumuni, S; Peltola, T; Undleeb, S; Volobouev, I; Wang, Z; Greene, S; Gurrola, A; Janjam, R; Johns, W; Maguire, C; Melo, A; Ni, H; Padeken, K; Ruiz Alvarez, J D; Sheldon, P; Tuo, S; Velkovska, J; Verweij, M; Xu, Q; Arenton, M W; Barria, P; Cox, B; Hirosky, R; Joyce, M; Ledovskoy, A; Li, H; Neu, C; Sinthuprasith, T; Wang, Y; Wolfe, E; Xia, F; Harr, R; Karchin, P E; Poudyal, N; Sturdy, J; Thapa, P; Zaleski, S; Brodski, M; Buchanan, J; Caillol, C; Carlsmith, D; Dasu, S; Dodd, L; Gomber, B; Grothe, M; Herndon, M; Hervé, A; Hussain, U; Klabbers, P; Lanaro, A; Long, K; Loveless, R; Ruggles, T; Savin, A; Smith, N; Smith, W H; Woods, N

2018-06-08

The observation of Higgs boson production in association with a top quark-antiquark pair is reported, based on a combined analysis of proton-proton collision data at center-of-mass energies of sqrt[s]=7, 8, and 13 TeV, corresponding to integrated luminosities of up to 5.1, 19.7, and 35.9  fb^{-1}, respectively. The data were collected with the CMS detector at the CERN LHC. The results of statistically independent searches for Higgs bosons produced in conjunction with a top quark-antiquark pair and decaying to pairs of W bosons, Z bosons, photons, τ leptons, or bottom quark jets are combined to maximize sensitivity. An excess of events is observed, with a significance of 5.2 standard deviations, over the expectation from the background-only hypothesis. The corresponding expected significance from the standard model for a Higgs boson mass of 125.09 GeV is 4.2 standard deviations. The combined best fit signal strength normalized to the standard model prediction is 1.26_{-0.26}^{+0.31}.

Generation of future potential scenarios in an Alpine Catchment by applying bias-correction techniques, delta-change approaches and stochastic Weather Generators at different spatial scale. Analysis of their influence on basic and drought statistics.

NASA Astrophysics Data System (ADS)

Collados-Lara, Antonio-Juan; Pulido-Velazquez, David; Pardo-Iguzquiza, Eulogio

2017-04-01

Assessing impacts of potential future climate change scenarios in precipitation and temperature is essential to design adaptive strategies in water resources systems. The objective of this work is to analyze the possibilities of different statistical downscaling methods to generate future potential scenarios in an Alpine Catchment from historical data and the available climate models simulations performed in the frame of the CORDEX EU project. The initial information employed to define these downscaling approaches are the historical climatic data (taken from the Spain02 project for the period 1971-2000 with a spatial resolution of 12.5 Km) and the future series provided by climatic models in the horizon period 2071-2100 . We have used information coming from nine climate model simulations (obtained from five different Regional climate models (RCM) nested to four different Global Climate Models (GCM)) from the European CORDEX project. In our application we have focused on the Representative Concentration Pathways (RCP) 8.5 emissions scenario, which is the most unfavorable scenario considered in the fifth Assessment Report (AR5) by the Intergovernmental Panel on Climate Change (IPCC). For each RCM we have generated future climate series for the period 2071-2100 by applying two different approaches, bias correction and delta change, and five different transformation techniques (first moment correction, first and second moment correction, regression functions, quantile mapping using distribution derived transformation and quantile mapping using empirical quantiles) for both of them. Ensembles of the obtained series were proposed to obtain more representative potential future climate scenarios to be employed to study potential impacts. In this work we propose a non-equifeaseble combination of the future series giving more weight to those coming from models (delta change approaches) or combination of models and techniques that provides better approximation to the basic and drought statistic of the historical data. A multi-objective analysis using basic statistics (mean, standard deviation and asymmetry coefficient) and droughts statistics (duration, magnitude and intensity) has been performed to identify which models are better in terms of goodness of fit to reproduce the historical series. The drought statistics have been obtained from the Standard Precipitation index (SPI) series using the Theory of Runs. This analysis allows discriminate the best RCM and the best combination of model and correction technique in the bias-correction method. We have also analyzed the possibilities of using different Stochastic Weather Generators to approximate the basic and droughts statistics of the historical series. These analyses have been performed in our case study in a lumped and in a distributed way in order to assess its sensibility to the spatial scale. The statistic of the future temperature series obtained with different ensemble options are quite homogeneous, but the precipitation shows a higher sensibility to the adopted method and spatial scale. The global increment in the mean temperature values are 31.79 %, 31.79 %, 31.03 % and 31.74 % for the distributed bias-correction, distributed delta-change, lumped bias-correction and lumped delta-change ensembles respectively and in the precipitation they are -25.48 %, -28.49 %, -26.42 % and -27.35% respectively. Acknowledgments: This research work has been partially supported by the GESINHIMPADAPT project (CGL2013-48424-C2-2-R) with Spanish MINECO funds. We would also like to thank Spain02 and CORDEX projects for the data provided for this study and the R package qmap.

SPReM: Sparse Projection Regression Model For High-dimensional Linear Regression *

PubMed Central

Sun, Qiang; Zhu, Hongtu; Liu, Yufeng; Ibrahim, Joseph G.

2014-01-01

The aim of this paper is to develop a sparse projection regression modeling (SPReM) framework to perform multivariate regression modeling with a large number of responses and a multivariate covariate of interest. We propose two novel heritability ratios to simultaneously perform dimension reduction, response selection, estimation, and testing, while explicitly accounting for correlations among multivariate responses. Our SPReM is devised to specifically address the low statistical power issue of many standard statistical approaches, such as the Hotelling’s T2 test statistic or a mass univariate analysis, for high-dimensional data. We formulate the estimation problem of SPREM as a novel sparse unit rank projection (SURP) problem and propose a fast optimization algorithm for SURP. Furthermore, we extend SURP to the sparse multi-rank projection (SMURP) by adopting a sequential SURP approximation. Theoretically, we have systematically investigated the convergence properties of SURP and the convergence rate of SURP estimates. Our simulation results and real data analysis have shown that SPReM out-performs other state-of-the-art methods. PMID:26527844

Statistical shape model-based reconstruction of a scaled, patient-specific surface model of the pelvis from a single standard AP x-ray radiograph

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

Zheng Guoyan

2010-04-15

Purpose: The aim of this article is to investigate the feasibility of using a statistical shape model (SSM)-based reconstruction technique to derive a scaled, patient-specific surface model of the pelvis from a single standard anteroposterior (AP) x-ray radiograph and the feasibility of estimating the scale of the reconstructed surface model by performing a surface-based 3D/3D matching. Methods: Data sets of 14 pelvises (one plastic bone, 12 cadavers, and one patient) were used to validate the single-image based reconstruction technique. This reconstruction technique is based on a hybrid 2D/3D deformable registration process combining a landmark-to-ray registration with a SSM-based 2D/3D reconstruction.more » The landmark-to-ray registration was used to find an initial scale and an initial rigid transformation between the x-ray image and the SSM. The estimated scale and rigid transformation were used to initialize the SSM-based 2D/3D reconstruction. The optimal reconstruction was then achieved in three stages by iteratively matching the projections of the apparent contours extracted from a 3D model derived from the SSM to the image contours extracted from the x-ray radiograph: Iterative affine registration, statistical instantiation, and iterative regularized shape deformation. The image contours are first detected by using a semiautomatic segmentation tool based on the Livewire algorithm and then approximated by a set of sparse dominant points that are adaptively sampled from the detected contours. The unknown scales of the reconstructed models were estimated by performing a surface-based 3D/3D matching between the reconstructed models and the associated ground truth models that were derived from a CT-based reconstruction method. Such a matching also allowed for computing the errors between the reconstructed models and the associated ground truth models. Results: The technique could reconstruct the surface models of all 14 pelvises directly from the landmark-based initialization. Depending on the surface-based matching techniques, the reconstruction errors were slightly different. When a surface-based iterative affine registration was used, an average reconstruction error of 1.6 mm was observed. This error was increased to 1.9 mm, when a surface-based iterative scaled rigid registration was used. Conclusions: It is feasible to reconstruct a scaled, patient-specific surface model of the pelvis from single standard AP x-ray radiograph using the present approach. The unknown scale of the reconstructed model can be estimated by performing a surface-based 3D/3D matching.« less

Interactions between different EEG frequency bands and their effect on alpha-fMRI correlations.

PubMed

de Munck, J C; Gonçalves, S I; Mammoliti, R; Heethaar, R M; Lopes da Silva, F H

2009-08-01

In EEG/fMRI correlation studies it is common to consider the fMRI BOLD as filtered version of the EEG alpha power. Here the question is addressed whether other EEG frequency components may affect the correlation between alpha and BOLD. This was done comparing the statistical parametric maps (SPMs) of three different filter models wherein either the free or the standard hemodynamic response functions (HRF) were used in combination with the full spectral bandwidth of the EEG. EEG and fMRI were co-registered in a 30 min resting state condition in 15 healthy young subjects. Power variations in the delta, theta, alpha, beta and gamma bands were extracted from the EEG and used as regressors in a general linear model. Statistical parametric maps (SPMs) were computed using three different filter models, wherein either the free or the standard hemodynamic response functions (HRF) were used in combination with the full spectral bandwidth of the EEG. Results show that the SPMs of different EEG frequency bands, when significant, are very similar to that of the alpha rhythm. This is true in particular for the beta band, despite the fact that the alpha harmonics were discarded. It is shown that inclusion of EEG frequency bands as confounder in the fMRI-alpha correlation model has a large effect on the resulting SPM, in particular when for each frequency band the HRF is extracted from the data. We conclude that power fluctuations of different EEG frequency bands are mutually highly correlated, and that a multi frequency model is required to extract the SPM of the frequency of interest from EEG/fMRI data. When no constraints are put on the shapes of the HRFs of the nuisance frequencies, the correlation model looses so much statistical power that no correlations can be detected.

Data Processing System (DPS) software with experimental design, statistical analysis and data mining developed for use in entomological research.

PubMed

Tang, Qi-Yi; Zhang, Chuan-Xi

2013-04-01

A comprehensive but simple-to-use software package called DPS (Data Processing System) has been developed to execute a range of standard numerical analyses and operations used in experimental design, statistics and data mining. This program runs on standard Windows computers. Many of the functions are specific to entomological and other biological research and are not found in standard statistical software. This paper presents applications of DPS to experimental design, statistical analysis and data mining in entomology. © 2012 The Authors Insect Science © 2012 Institute of Zoology, Chinese Academy of Sciences.

A Statistical Framework for Protein Quantitation in Bottom-Up MS-Based Proteomics

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

Karpievitch, Yuliya; Stanley, Jeffrey R.; Taverner, Thomas

2009-08-15

Motivation: Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level. Results: We present a statistical model that carefully accounts for informative missingness in peak intensities and allows unbiased, model-based, protein-level estimation and inference. The model is applicable to both label-based and label-free quantitation experiments. We also provide automated, model-based, algorithms for filtering of proteins and peptides as well as imputation of missing values. Two LC/MS datasets are used to illustrate themore » methods. In simulation studies, our methods are shown to achieve substantially more discoveries than standard alternatives. Availability: The software has been made available in the opensource proteomics platform DAnTE (http://omics.pnl.gov/software/). Contact: adabney@stat.tamu.edu Supplementary information: Supplementary data are available at Bioinformatics online.« less

Additive Genetic Variability and the Bayesian Alphabet

PubMed Central

Gianola, Daniel; de los Campos, Gustavo; Hill, William G.; Manfredi, Eduardo; Fernando, Rohan

2009-01-01

The use of all available molecular markers in statistical models for prediction of quantitative traits has led to what could be termed a genomic-assisted selection paradigm in animal and plant breeding. This article provides a critical review of some theoretical and statistical concepts in the context of genomic-assisted genetic evaluation of animals and crops. First, relationships between the (Bayesian) variance of marker effects in some regression models and additive genetic variance are examined under standard assumptions. Second, the connection between marker genotypes and resemblance between relatives is explored, and linkages between a marker-based model and the infinitesimal model are reviewed. Third, issues associated with the use of Bayesian models for marker-assisted selection, with a focus on the role of the priors, are examined from a theoretical angle. The sensitivity of a Bayesian specification that has been proposed (called “Bayes A”) with respect to priors is illustrated with a simulation. Methods that can solve potential shortcomings of some of these Bayesian regression procedures are discussed briefly. PMID:19620397

Statistical analysis of cascading failures in power grids

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

Chertkov, Michael; Pfitzner, Rene; Turitsyn, Konstantin

2010-12-01

We introduce a new microscopic model of cascading failures in transmission power grids. This model accounts for automatic response of the grid to load fluctuations that take place on the scale of minutes, when optimum power flow adjustments and load shedding controls are unavailable. We describe extreme events, caused by load fluctuations, which cause cascading failures of loads, generators and lines. Our model is quasi-static in the causal, discrete time and sequential resolution of individual failures. The model, in its simplest realization based on the Directed Current description of the power flow problem, is tested on three standard IEEE systemsmore » consisting of 30, 39 and 118 buses. Our statistical analysis suggests a straightforward classification of cascading and islanding phases in terms of the ratios between average number of removed loads, generators and links. The analysis also demonstrates sensitivity to variations in line capacities. Future research challenges in modeling and control of cascading outages over real-world power networks are discussed.« less

Combining forecast weights: Why and how?

NASA Astrophysics Data System (ADS)

Yin, Yip Chee; Kok-Haur, Ng; Hock-Eam, Lim

2012-09-01

This paper proposes a procedure called forecast weight averaging which is a specific combination of forecast weights obtained from different methods of constructing forecast weights for the purpose of improving the accuracy of pseudo out of sample forecasting. It is found that under certain specified conditions, forecast weight averaging can lower the mean squared forecast error obtained from model averaging. In addition, we show that in a linear and homoskedastic environment, this superior predictive ability of forecast weight averaging holds true irrespective whether the coefficients are tested by t statistic or z statistic provided the significant level is within the 10% range. By theoretical proofs and simulation study, we have shown that model averaging like, variance model averaging, simple model averaging and standard error model averaging, each produces mean squared forecast error larger than that of forecast weight averaging. Finally, this result also holds true marginally when applied to business and economic empirical data sets, Gross Domestic Product (GDP growth rate), Consumer Price Index (CPI) and Average Lending Rate (ALR) of Malaysia.

A marked correlation function for constraining modified gravity models

NASA Astrophysics Data System (ADS)

White, Martin

2016-11-01

Future large scale structure surveys will provide increasingly tight constraints on our cosmological model. These surveys will report results on the distance scale and growth rate of perturbations through measurements of Baryon Acoustic Oscillations and Redshift-Space Distortions. It is interesting to ask: what further analyses should become routine, so as to test as-yet-unknown models of cosmic acceleration? Models which aim to explain the accelerated expansion rate of the Universe by modifications to General Relativity often invoke screening mechanisms which can imprint a non-standard density dependence on their predictions. This suggests density-dependent clustering as a `generic' constraint. This paper argues that a density-marked correlation function provides a density-dependent statistic which is easy to compute and report and requires minimal additional infrastructure beyond what is routinely available to such survey analyses. We give one realization of this idea and study it using low order perturbation theory. We encourage groups developing modified gravity theories to see whether such statistics provide discriminatory power for their models.

Research participant compensation: A matter of statistical inference as well as ethics.

PubMed

Swanson, David M; Betensky, Rebecca A

2015-11-01

The ethics of compensation of research subjects for participation in clinical trials has been debated for years. One ethical issue of concern is variation among subjects in the level of compensation for identical treatments. Surprisingly, the impact of variation on the statistical inferences made from trial results has not been examined. We seek to identify how variation in compensation may influence any existing dependent censoring in clinical trials, thereby also influencing inference about the survival curve, hazard ratio, or other measures of treatment efficacy. In simulation studies, we consider a model for how compensation structure may influence the censoring model. Under existing dependent censoring, we estimate survival curves under different compensation structures and observe how these structures induce variability in the estimates. We show through this model that if the compensation structure affects the censoring model and dependent censoring is present, then variation in that structure induces variation in the estimates and affects the accuracy of estimation and inference on treatment efficacy. From the perspectives of both ethics and statistical inference, standardization and transparency in the compensation of participants in clinical trials are warranted. Copyright © 2015 Elsevier Inc. All rights reserved.

Multivariate model of female black bear habitat use for a Geographic Information System

USGS Publications Warehouse

Clark, Joseph D.; Dunn, James E.; Smith, Kimberly G.

1993-01-01

Simple univariate statistical techniques may not adequately assess the multidimensional nature of habitats used by wildlife. Thus, we developed a multivariate method to model habitat-use potential using a set of female black bear (Ursus americanus) radio locations and habitat data consisting of forest cover type, elevation, slope, aspect, distance to roads, distance to streams, and forest cover type diversity score in the Ozark Mountains of Arkansas. The model is based on the Mahalanobis distance statistic coupled with Geographic Information System (GIS) technology. That statistic is a measure of dissimilarity and represents a standardized squared distance between a set of sample variates and an ideal based on the mean of variates associated with animal observations. Calculations were made with the GIS to produce a map containing Mahalanobis distance values within each cell on a 60- × 60-m grid. The model identified areas of high habitat use potential that could not otherwise be identified by independent perusal of any single map layer. This technique avoids many pitfalls that commonly affect typical multivariate analyses of habitat use and is a useful tool for habitat manipulation or mitigation to favor terrestrial vertebrates that use habitats on a landscape scale.

Fluctuations in air pollution give risk warning signals of asthma hospitalization

NASA Astrophysics Data System (ADS)

Hsieh, Nan-Hung; Liao, Chung-Min

2013-08-01

Recent studies have implicated that air pollution has been associated with asthma exacerbations. However, the key link between specific air pollutant and the consequent impact on asthma has not been shown. The purpose of this study was to quantify the fluctuations in air pollution time-series dynamics to correlate the relationships between statistical indicators and age-specific asthma hospital admissions. An indicators-based regression model was developed to predict the time-trend of asthma hospital admissions in Taiwan in the period 1998-2010. Five major pollutants such as particulate matters with aerodynamic diameter less than 10 μm (PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) were included. We used Spearman's rank correlation to detect the relationships between time-series based statistical indicators of standard deviation, coefficient of variation, skewness, and kurtosis and monthly asthma hospitalization. We further used the indicators-guided Poisson regression model to test and predict the impact of target air pollutants on asthma incidence. Here we showed that standard deviation of PM10 data was the most correlated indicators for asthma hospitalization for all age groups, particularly for elderly. The skewness of O3 data gives the highest correlation to adult asthmatics. The proposed regression model shows a better predictability in annual asthma hospitalization trends for pediatrics. Our results suggest that a set of statistical indicators inferred from time-series information of major air pollutants can provide advance risk warning signals in complex air pollution-asthma systems and aid in asthma management that depends heavily on monitoring the dynamics of asthma incidence and environmental stimuli.

Search for a dark photon in e(+)e(-) collisions at BABAR.

PubMed

Lees, J P; Poireau, V; Tisserand, V; Grauges, E; Palano, A; Eigen, G; Stugu, B; Brown, D N; Feng, M; Kerth, L T; Kolomensky, Yu G; Lee, M J; Lynch, G; Koch, H; Schroeder, T; Hearty, C; Mattison, T S; McKenna, J A; So, R Y; Khan, A; Blinov, V E; Buzykaev, A R; Druzhinin, V P; Golubev, V B; Kravchenko, E A; Onuchin, A P; Serednyakov, S I; Skovpen, Yu I; Solodov, E P; Todyshev, K Yu; Lankford, A J; Mandelkern, M; Dey, B; Gary, J W; Long, O; Campagnari, C; Franco Sevilla, M; Hong, T M; Kovalskyi, D; Richman, J D; West, C A; Eisner, A M; Lockman, W S; Panduro Vazquez, W; Schumm, B A; Seiden, A; Chao, D S; Cheng, C H; Echenard, B; Flood, K T; Hitlin, D G; Miyashita, T S; Ongmongkolkul, P; Porter, F C; Andreassen, R; Huard, Z; Meadows, B T; Pushpawela, B G; Sokoloff, M D; Sun, L; Bloom, P C; Ford, W T; Gaz, A; Smith, J G; Wagner, S R; Ayad, R; Toki, W H; Spaan, B; Bernard, D; Verderi, M; Playfer, S; Bettoni, D; Bozzi, C; Calabrese, R; Cibinetto, G; Fioravanti, E; Garzia, I; Luppi, E; Piemontese, L; Santoro, V; Calcaterra, A; de Sangro, R; Finocchiaro, G; Martellotti, S; Patteri, P; Peruzzi, I M; Piccolo, M; Rama, M; Zallo, A; Contri, R; Lo Vetere, M; Monge, M R; Passaggio, S; Patrignani, C; Robutti, E; Bhuyan, B; Prasad, V; Adametz, A; Uwer, U; Lacker, H M; Dauncey, P D; Mallik, U; Chen, C; Cochran, J; Prell, S; Ahmed, H; Gritsan, A V; Arnaud, N; Davier, M; Derkach, D; Grosdidier, G; Le Diberder, F; Lutz, A M; Malaescu, B; Roudeau, P; Stocchi, A; Wormser, G; Lange, D J; Wright, D M; Coleman, J P; Fry, J R; Gabathuler, E; Hutchcroft, D E; Payne, D J; Touramanis, C; Bevan, A J; Di Lodovico, F; Sacco, R; Cowan, G; Bougher, J; Brown, D N; Davis, C L; Denig, A G; Fritsch, M; Gradl, W; Griessinger, K; Hafner, A; Schubert, K R; Barlow, R J; Lafferty, G D; Cenci, R; Hamilton, B; Jawahery, A; Roberts, D A; Cowan, R; Sciolla, G; Cheaib, R; Patel, P M; Robertson, S H; Neri, N; Palombo, F; Cremaldi, L; Godang, R; Sonnek, P; Summers, D J; Simard, M; Taras, P; De Nardo, G; Onorato, G; Sciacca, C; Martinelli, M; Raven, G; Jessop, C P; LoSecco, J M; Honscheid, K; Kass, R; Feltresi, E; Margoni, M; Morandin, M; Posocco, M; Rotondo, M; Simi, G; Simonetto, F; Stroili, R; Akar, S; Ben-Haim, E; Bomben, M; Bonneaud, G R; Briand, H; Calderini, G; Chauveau, J; Leruste, Ph; Marchiori, G; Ocariz, J; Biasini, M; Manoni, E; Pacetti, S; Rossi, A; Angelini, C; Batignani, G; Bettarini, S; Carpinelli, M; Casarosa, G; Cervelli, A; Chrzaszcz, M; Forti, F; Giorgi, M A; Lusiani, A; Oberhof, B; Paoloni, E; Perez, A; Rizzo, G; Walsh, J J; Lopes Pegna, D; Olsen, J; Smith, A J S; Faccini, R; Ferrarotto, F; Ferroni, F; Gaspero, M; Li Gioi, L; Pilloni, A; Piredda, G; Bünger, C; Dittrich, S; Grünberg, O; Hartmann, T; Hess, M; Leddig, T; Voß, C; Waldi, R; Adye, T; Olaiya, E O; Wilson, F F; Emery, S; Vasseur, G; Anulli, F; Aston, D; Bard, D J; Cartaro, C; Convery, M R; Dorfan, J; Dubois-Felsmann, G P; Dunwoodie, W; Ebert, M; Field, R C; Fulsom, B G; Graham, M T; Hast, C; Innes, W R; Kim, P; Leith, D W G S; Lewis, P; Lindemann, D; Luitz, S; Luth, V; Lynch, H L; MacFarlane, D B; Muller, D R; Neal, H; Perl, M; Pulliam, T; Ratcliff, B N; Roodman, A; Salnikov, A A; Schindler, R H; Snyder, A; Su, D; Sullivan, M K; Va'vra, J; Wisniewski, W J; Wulsin, H W; Purohit, M V; White, R M; Wilson, J R; Randle-Conde, A; Sekula, S J; Bellis, M; Burchat, P R; Puccio, E M T; Alam, M S; Ernst, J A; Gorodeisky, R; Guttman, N; Peimer, D R; Soffer, A; Spanier, S M; Ritchie, J L; Ruland, A M; Schwitters, R F; Wray, B C; Izen, J M; Lou, X C; Bianchi, F; De Mori, F; Filippi, A; Gamba, D; Lanceri, L; Vitale, L; Martinez-Vidal, F; Oyanguren, A; Villanueva-Perez, P; Albert, J; Banerjee, Sw; Beaulieu, A; Bernlochner, F U; Choi, H H F; King, G J; Kowalewski, R; Lewczuk, M J; Lueck, T; Nugent, I M; Roney, J M; Sobie, R J; Tasneem, N; Gershon, T J; Harrison, P F; Latham, T E; Band, H R; Dasu, S; Pan, Y; Prepost, R; Wu, S L

2014-11-14

Dark sectors charged under a new Abelian interaction have recently received much attention in the context of dark matter models. These models introduce a light new mediator, the so-called dark photon (A^{'}), connecting the dark sector to the standard model. We present a search for a dark photon in the reaction e^{+}e^{-}→γA^{'}, A^{'}→e^{+}e^{-}, μ^{+}μ^{-} using 514  fb^{-1} of data collected with the BABAR detector. We observe no statistically significant deviations from the standard model predictions, and we set 90% confidence level upper limits on the mixing strength between the photon and dark photon at the level of 10^{-4}-10^{-3} for dark photon masses in the range 0.02-10.2  GeV. We further constrain the range of the parameter space favored by interpretations of the discrepancy between the calculated and measured anomalous magnetic moment of the muon.

Search for High-Mass Resonances Decaying to τν in pp Collisions at sqrt[s]=13  TeV with the ATLAS Detector.

PubMed

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Theveneaux-Pelzer, T; Thiele, F; Thomas, J P; Thompson, P D; Thompson, A S; Thomsen, L A; Thomson, E; Tian, Y; Ticse Torres, R E; Tikhomirov, V O; Tikhonov, Yu A; Timoshenko, S; Tipton, P; Tisserant, S; Todome, K; Todorova-Nova, S; Todt, S; Tojo, J; Tokár, S; Tokushuku, K; Tolley, E; Tomoto, M; Tompkins, L; Toms, K; Tong, B; Tornambe, P; Torrence, E; Torres, H; Torró Pastor, E; Tosciri, C; Toth, J; Touchard, F; Tovey, D R; Treado, C J; Trefzger, T; Tresoldi, F; Tricoli, A; Trigger, I M; Trincaz-Duvoid, S; Tripiana, M F; Trischuk, W; Trocmé, B; Trofymov, A; Troncon, C; Trovatelli, M; Trovato, F; Truong, L; Trzebinski, M; Trzupek, A; Tsai, F; Tsang, K W; Tseng, J C-L; Tsiareshka, P V; Tsirintanis, N; Tsiskaridze, S; Tsiskaridze, V; Tskhadadze, E G; Tsukerman, I I; Tsulaia, V; Tsuno, S; Tsybychev, D; Tu, Y; Tudorache, A; Tudorache, V; Tulbure, T T; Tuna, A N; Turchikhin, S; Turgeman, D; Turk Cakir, I; Turra, R; Tuts, P M; Ucchielli, G; Ueda, I; Ughetto, M; Ukegawa, F; Unal, G; Undrus, A; Unel, G; Ungaro, F C; Unno, Y; Uno, K; Urban, J; Urquijo, P; Urrejola, P; Usai, G; Usui, J; Vacavant, L; Vacek, V; Vachon, B; Vadla, K O H; Vaidya, A; Valderanis, C; Valdes Santurio, E; Valente, M; Valentinetti, S; Valero, A; Valéry, L; Vallance, R A; Vallier, A; Valls Ferrer, J A; Van Daalen, T R; Van Den Wollenberg, W; van der Graaf, H; van Gemmeren, P; Van Nieuwkoop, J; van Vulpen, I; van Woerden, M C; Vanadia, M; Vandelli, W; Vaniachine, A; Vankov, P; Vari, R; Varnes, E W; Varni, C; Varol, T; Varouchas, D; Vartapetian, A; Varvell, K E; Vasquez, J G; Vasquez, G A; Vazeille, F; Vazquez Furelos, D; Vazquez Schroeder, T; Veatch, J; Vecchio, V; Veloce, L M; Veloso, F; Veneziano, S; Ventura, A; Venturi, M; Venturi, N; Vercesi, V; Verducci, M; Vergis, C; Verkerke, W; Vermeulen, A T; Vermeulen, J C; Vetterli, M C; Viaux Maira, N; Viazlo, O; Vichou, I; Vickey, T; Vickey Boeriu, O E; Viehhauser, G H A; Viel, S; Vigani, L; Villa, M; Villaplana Perez, M; Vilucchi, E; Vincter, M G; Vinogradov, V B; Vishwakarma, A; Vittori, C; Vivarelli, I; Vlachos, S; Vogel, M; Vokac, P; Volpi, G; von Buddenbrock, S E; von Toerne, E; Vorobel, V; Vorobev, K; Vos, M; Vossebeld, J H; Vranjes, N; Vranjes Milosavljevic, M; Vrba, V; Vreeswijk, M; Vuillermet, R; Vukotic, I; Wagner, P; Wagner, W; Wagner-Kuhr, J; Wahlberg, H; Wahrmund, S; Wakamiya, K; Walder, J; Walker, R; Walkowiak, W; Wallangen, V; Wang, A M; Wang, C; Wang, F; Wang, H; Wang, H; Wang, J; Wang, J; Wang, Q; Wang, R-J; Wang, R; Wang, R; Wang, S M; Wang, T; Wang, W; Wang, W; Wang, Y; Wang, Z; Wanotayaroj, C; Warburton, A; Ward, C P; Wardrope, D R; Washbrook, A; Watkins, P M; Watson, A T; Watson, M F; Watts, G; Watts, S; Waugh, B M; Webb, A F; Webb, S; Weber, C; Weber, M S; Weber, S M; Weber, S A; Webster, J S; Weidberg, A R; Weinert, B; Weingarten, J; Weirich, M; Weiser, C; Wells, P S; Wenaus, T; Wengler, T; Wenig, S; Wermes, N; Werner, M D; Werner, P; Wessels, M; Weston, T D; Whalen, K; Whallon, N L; Wharton, A M; White, A S; White, A; White, M J; White, R; Whiteson, D; Whitmore, B W; Wickens, F J; Wiedenmann, W; Wielers, M; Wiglesworth, C; Wiik-Fuchs, L A M; Wildauer, A; Wilk, F; Wilkens, H G; Williams, H H; Williams, S; Willis, C; Willocq, S; Wilson, J A; Wingerter-Seez, I; Winkels, E; Winklmeier, F; Winston, O J; Winter, B T; Wittgen, M; Wobisch, M; Wolf, A; Wolf, T M H; Wolff, R; Wolter, M W; Wolters, H; Wong, V W S; Woods, N L; Worm, S D; Wosiek, B K; Wozniak, K W; Wraight, K; Wu, M; Wu, S L; Wu, X; Wu, Y; Wyatt, T R; Wynne, B M; Xella, S; Xi, Z; Xia, L; Xu, D; Xu, H; Xu, L; Xu, T; Xu, W; Yabsley, B; Yacoob, S; Yajima, K; Yallup, D P; Yamaguchi, D; Yamaguchi, Y; Yamamoto, A; Yamanaka, T; Yamane, F; Yamatani, M; Yamazaki, T; Yamazaki, Y; Yan, Z; Yang, H; Yang, H; Yang, S; Yang, Y; Yang, Y; Yang, Z; Yao, W-M; Yap, Y C; Yasu, Y; Yatsenko, E; Yau Wong, K H; Ye, J; Ye, S; Yeletskikh, I; Yigitbasi, E; Yildirim, E; Yorita, K; Yoshihara, K; Young, C; Young, C J S; Yu, J; Yu, J; Yue, X; Yuen, S P Y; Yusuff, I; Zabinski, B; Zacharis, G; Zaidan, R; Zaitsev, A M; Zakharchuk, N; Zalieckas, J; Zambito, S; Zanzi, D; Zeitnitz, C; Zemaityte, G; Zeng, J C; Zeng, Q; Zenin, O; Ženiš, T; Zerwas, D; Zgubič, M; Zhang, D; Zhang, D; Zhang, F; Zhang, G; Zhang, H; Zhang, J; Zhang, L; Zhang, L; Zhang, M; Zhang, P; Zhang, R; Zhang, R; Zhang, X; Zhang, Y; Zhang, Z; Zhao, X; Zhao, Y; Zhao, Z; Zhemchugov, A; Zhou, B; Zhou, C; Zhou, L; Zhou, M; Zhou, M; Zhou, N; Zhou, Y; Zhu, C G; Zhu, H; Zhu, H; Zhu, J; Zhu, Y; Zhuang, X; Zhukov, K; Zhulanov, V; Zibell, A; Zieminska, D; Zimine, N I; Zimmermann, S; Zinonos, Z; Zinser, M; Ziolkowski, M; Živković, L; Zobernig, G; Zoccoli, A; Zoch, K; Zorbas, T G; Zou, R; Zur Nedden, M; Zwalinski, L

2018-04-20

A search for high-mass resonances decaying to τν using proton-proton collisions at sqrt[s]=13  TeV produced by the Large Hadron Collider is presented. Only τ-lepton decays with hadrons in the final state are considered. The data were recorded with the ATLAS detector and correspond to an integrated luminosity of 36.1  fb^{-1}. No statistically significant excess above the standard model expectation is observed; model-independent upper limits are set on the visible τν production cross section. Heavy W^{'} bosons with masses less than 3.7 TeV in the sequential standard model and masses less than 2.2-3.8 TeV depending on the coupling in the nonuniversal G(221) model are excluded at the 95% credibility level.

Search for High-Mass Resonances Decaying to τ ν in p p Collisions at s = 13 TeV with the ATLAS Detector

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

Aaboud, M.; Aad, G.; Abbott, B.

Here, a search for high-mass resonances decaying to τν using proton-proton collisions atmore » $$\\sqrt{s}$$ = 13 TeV produced by the Large Hadron Collider is presented. Only τ-lepton decays with hadrons in the final state are considered. The data were recorded with the ATLAS detector and correspond to an integrated luminosity of 36.1fb -1. No statistically significant excess above the standard model expectation is observed; model-independent upper limits are set on the visible τν production cross section. Heavy W' bosons with masses less than 3.7 TeV in the sequential standard model and masses less than 2.2–3.8 TeV depending on the coupling in the nonuniversal G(221) model are excluded at the 95% credibility level.« less

Search for High-Mass Resonances Decaying to τ ν in p p Collisions at s = 13 TeV with the ATLAS Detector

DOE PAGES

Aaboud, M.; Aad, G.; Abbott, B.; ...

2018-04-20

Here, a search for high-mass resonances decaying to τν using proton-proton collisions atmore » $$\\sqrt{s}$$ = 13 TeV produced by the Large Hadron Collider is presented. Only τ-lepton decays with hadrons in the final state are considered. The data were recorded with the ATLAS detector and correspond to an integrated luminosity of 36.1fb -1. No statistically significant excess above the standard model expectation is observed; model-independent upper limits are set on the visible τν production cross section. Heavy W' bosons with masses less than 3.7 TeV in the sequential standard model and masses less than 2.2–3.8 TeV depending on the coupling in the nonuniversal G(221) model are excluded at the 95% credibility level.« less

Recent updates in developing a statistical pseudo-dynamic source-modeling framework to capture the variability of earthquake rupture scenarios

NASA Astrophysics Data System (ADS)

Song, Seok Goo; Kwak, Sangmin; Lee, Kyungbook; Park, Donghee

2017-04-01

It is a critical element to predict the intensity and variability of strong ground motions in seismic hazard assessment. The characteristics and variability of earthquake rupture process may be a dominant factor in determining the intensity and variability of near-source strong ground motions. Song et al. (2014) demonstrated that the variability of earthquake rupture scenarios could be effectively quantified in the framework of 1-point and 2-point statistics of earthquake source parameters, constrained by rupture dynamics and past events. The developed pseudo-dynamic source modeling schemes were also validated against the recorded ground motion data of past events and empirical ground motion prediction equations (GMPEs) at the broadband platform (BBP) developed by the Southern California Earthquake Center (SCEC). Recently we improved the computational efficiency of the developed pseudo-dynamic source-modeling scheme by adopting the nonparametric co-regionalization algorithm, introduced and applied in geostatistics initially. We also investigated the effect of earthquake rupture process on near-source ground motion characteristics in the framework of 1-point and 2-point statistics, particularly focusing on the forward directivity region. Finally we will discuss whether the pseudo-dynamic source modeling can reproduce the variability (standard deviation) of empirical GMPEs and the efficiency of 1-point and 2-point statistics to address the variability of ground motions.

BRIDG: a domain information model for translational and clinical protocol-driven research.

PubMed

Becnel, Lauren B; Hastak, Smita; Ver Hoef, Wendy; Milius, Robert P; Slack, MaryAnn; Wold, Diane; Glickman, Michael L; Brodsky, Boris; Jaffe, Charles; Kush, Rebecca; Helton, Edward

2017-09-01

It is critical to integrate and analyze data from biological, translational, and clinical studies with data from health systems; however, electronic artifacts are stored in thousands of disparate systems that are often unable to readily exchange data. To facilitate meaningful data exchange, a model that presents a common understanding of biomedical research concepts and their relationships with health care semantics is required. The Biomedical Research Integrated Domain Group (BRIDG) domain information model fulfills this need. Software systems created from BRIDG have shared meaning "baked in," enabling interoperability among disparate systems. For nearly 10 years, the Clinical Data Standards Interchange Consortium, the National Cancer Institute, the US Food and Drug Administration, and Health Level 7 International have been key stakeholders in developing BRIDG. BRIDG is an open-source Unified Modeling Language-class model developed through use cases and harmonization with other models. With its 4+ releases, BRIDG includes clinical and now translational research concepts in its Common, Protocol Representation, Study Conduct, Adverse Events, Regulatory, Statistical Analysis, Experiment, Biospecimen, and Molecular Biology subdomains. The model is a Clinical Data Standards Interchange Consortium, Health Level 7 International, and International Standards Organization standard that has been utilized in national and international standards-based software development projects. It will continue to mature and evolve in the areas of clinical imaging, pathology, ontology, and vocabulary support. BRIDG 4.1.1 and prior releases are freely available at https://bridgmodel.nci.nih.gov . © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Applications of the DOE/NASA wind turbine engineering information system

NASA Technical Reports Server (NTRS)

Neustadter, H. E.; Spera, D. A.

1981-01-01

A statistical analysis of data obtained from the Technology and Engineering Information Systems was made. The systems analyzed consist of the following elements: (1) sensors which measure critical parameters (e.g., wind speed and direction, output power, blade loads and component vibrations); (2) remote multiplexing units (RMUs) on each wind turbine which frequency-modulate, multiplex and transmit sensor outputs; (3) on-site instrumentation to record, process and display the sensor output; and (4) statistical analysis of data. Two examples of the capabilities of these systems are presented. The first illustrates the standardized format for application of statistical analysis to each directly measured parameter. The second shows the use of a model to estimate the variability of the rotor thrust loading, which is a derived parameter.

Statistical mechanics of two-dimensional shuffled foams: prediction of the correlation between geometry and topology.

PubMed

Durand, Marc; Käfer, Jos; Quilliet, Catherine; Cox, Simon; Talebi, Shirin Ataei; Graner, François

2011-10-14

We propose an analytical model for the statistical mechanics of shuffled two-dimensional foams with moderate bubble size polydispersity. It predicts without any adjustable parameters the correlations between the number of sides n of the bubbles (topology) and their areas A (geometry) observed in experiments and numerical simulations of shuffled foams. Detailed statistics show that in shuffled cellular patterns n correlates better with √A (as claimed by Desch and Feltham) than with A (as claimed by Lewis and widely assumed in the literature). At the level of the whole foam, standard deviations Δn and ΔA are in proportion. Possible applications include correlations of the detailed distributions of n and A, three-dimensional foams, and biological tissues.

Tests of Mediation: Paradoxical Decline in Statistical Power as a Function of Mediator Collinearity

PubMed Central

Beasley, T. Mark

2013-01-01

Increasing the correlation between the independent variable and the mediator (a coefficient) increases the effect size (ab) for mediation analysis; however, increasing a by definition increases collinearity in mediation models. As a result, the standard error of product tests increase. The variance inflation due to increases in a at some point outweighs the increase of the effect size (ab) and results in a loss of statistical power. This phenomenon also occurs with nonparametric bootstrapping approaches because the variance of the bootstrap distribution of ab approximates the variance expected from normal theory. Both variances increase dramatically when a exceeds the b coefficient, thus explaining the power decline with increases in a. Implications for statistical analysis and applied researchers are discussed. PMID:24954952

Conservative Tests under Satisficing Models of Publication Bias.

PubMed

McCrary, Justin; Christensen, Garret; Fanelli, Daniele

2016-01-01

Publication bias leads consumers of research to observe a selected sample of statistical estimates calculated by producers of research. We calculate critical values for statistical significance that could help to adjust after the fact for the distortions created by this selection effect, assuming that the only source of publication bias is file drawer bias. These adjusted critical values are easy to calculate and differ from unadjusted critical values by approximately 50%-rather than rejecting a null hypothesis when the t-ratio exceeds 2, the analysis suggests rejecting a null hypothesis when the t-ratio exceeds 3. Samples of published social science research indicate that on average, across research fields, approximately 30% of published t-statistics fall between the standard and adjusted cutoffs.

Conservative Tests under Satisficing Models of Publication Bias

PubMed Central

McCrary, Justin; Christensen, Garret; Fanelli, Daniele

2016-01-01

Publication bias leads consumers of research to observe a selected sample of statistical estimates calculated by producers of research. We calculate critical values for statistical significance that could help to adjust after the fact for the distortions created by this selection effect, assuming that the only source of publication bias is file drawer bias. These adjusted critical values are easy to calculate and differ from unadjusted critical values by approximately 50%—rather than rejecting a null hypothesis when the t-ratio exceeds 2, the analysis suggests rejecting a null hypothesis when the t-ratio exceeds 3. Samples of published social science research indicate that on average, across research fields, approximately 30% of published t-statistics fall between the standard and adjusted cutoffs. PMID:26901834

The correlation between relatives on the supposition of genomic imprinting.

PubMed Central

Spencer, Hamish G

2002-01-01

Standard genetic analyses assume that reciprocal heterozygotes are, on average, phenotypically identical. If a locus is subject to genomic imprinting, however, this assumption does not hold. We incorporate imprinting into the standard quantitative-genetic model for two alleles at a single locus, deriving expressions for the additive and dominance components of genetic variance, as well as measures of resemblance among relatives. We show that, in contrast to the case with Mendelian expression, the additive and dominance deviations are correlated. In principle, this correlation allows imprinting to be detected solely on the basis of different measures of familial resemblances, but in practice, the standard error of the estimate is likely to be too large for a test to have much statistical power. The effects of genomic imprinting will need to be incorporated into quantitative-genetic models of many traits, for example, those concerned with mammalian birthweight. PMID:12019254

The correlation between relatives on the supposition of genomic imprinting.

PubMed

Spencer, Hamish G

2002-05-01

Standard genetic analyses assume that reciprocal heterozygotes are, on average, phenotypically identical. If a locus is subject to genomic imprinting, however, this assumption does not hold. We incorporate imprinting into the standard quantitative-genetic model for two alleles at a single locus, deriving expressions for the additive and dominance components of genetic variance, as well as measures of resemblance among relatives. We show that, in contrast to the case with Mendelian expression, the additive and dominance deviations are correlated. In principle, this correlation allows imprinting to be detected solely on the basis of different measures of familial resemblances, but in practice, the standard error of the estimate is likely to be too large for a test to have much statistical power. The effects of genomic imprinting will need to be incorporated into quantitative-genetic models of many traits, for example, those concerned with mammalian birthweight.

Air Traffic Control Improvement Using Prioritized CSMA

NASA Technical Reports Server (NTRS)

Robinson, Daryl C.

2001-01-01

Version 7 simulations of the industry-standard network simulation software "OPNET" are presented of two applications of the Aeronautical Telecommunications Network (ATN), Controller Pilot Data Link Communications (CPDLC) and Automatic Dependent Surveillance-Broadcast mode (ADS-B), over VHF Data Link mode 2 (VDL-2). Communication is modeled for air traffic between just three cities. All aircraft are assumed to have the same equipage. The simulation involves Air Traffic Control (ATC) ground stations and 105 aircraft taking off, flying realistic free-flight trajectories, and landing in a 24-hr period. All communication is modeled as unreliable. Collision-less, prioritized carrier sense multiple access (CSMA) is successfully tested. The statistics presented include latency, queue length, and packet loss. This research may show that a communications system simpler than the currently accepted standard envisioned may not only suffice, but also surpass performance of the standard at a lower cost of deployment.

The Muon $g$-$2$ Experiment at Fermilab

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

Gohn, Wesley

A new measurement of the anomalous magnetic moment of the muon,more » $$a_{\\mu} \\equiv (g-2)/2$$, will be performed at the Fermi National Accelerator Laboratory with data taking beginning in 2017. The most recent measurement, performed at Brookhaven National Laboratory (BNL) and completed in 2001, shows a 3.5 standard deviation discrepancy with the standard model value of $$a_\\mu$$. The new measurement will accumulate 21 times the BNL statistics using upgraded magnet, detector, and storage ring systems, enabling a measurement of $$a_\\mu$$ to 140 ppb, a factor of 4 improvement in the uncertainty the previous measurement. This improvement in precision, combined with recent improvements in our understanding of the QCD contributions to the muon $g$-$2$, could provide a discrepancy from the standard model greater than 7$$\\sigma$$ if the central value is the same as that measured by the BNL experiment, which would be a clear indication of new physics.« less

Blind image quality assessment without training on human opinion scores

NASA Astrophysics Data System (ADS)

Mittal, Anish; Soundararajan, Rajiv; Muralidhar, Gautam S.; Bovik, Alan C.; Ghosh, Joydeep

2013-03-01

We propose a family of image quality assessment (IQA) models based on natural scene statistics (NSS), that can predict the subjective quality of a distorted image without reference to a corresponding distortionless image, and without any training results on human opinion scores of distorted images. These `completely blind' models compete well with standard non-blind image quality indices in terms of subjective predictive performance when tested on the large publicly available `LIVE' Image Quality database.

pcr: an R package for quality assessment, analysis and testing of qPCR data

PubMed Central

Ahmed, Mahmoud

2018-01-01

Background Real-time quantitative PCR (qPCR) is a broadly used technique in the biomedical research. Currently, few different analysis models are used to determine the quality of data and to quantify the mRNA level across the experimental conditions. Methods We developed an R package to implement methods for quality assessment, analysis and testing qPCR data for statistical significance. Double Delta CT and standard curve models were implemented to quantify the relative expression of target genes from CT in standard qPCR control-group experiments. In addition, calculation of amplification efficiency and curves from serial dilution qPCR experiments are used to assess the quality of the data. Finally, two-group testing and linear models were used to test for significance of the difference in expression control groups and conditions of interest. Results Using two datasets from qPCR experiments, we applied different quality assessment, analysis and statistical testing in the pcr package and compared the results to the original published articles. The final relative expression values from the different models, as well as the intermediary outputs, were checked against the expected results in the original papers and were found to be accurate and reliable. Conclusion The pcr package provides an intuitive and unified interface for its main functions to allow biologist to perform all necessary steps of qPCR analysis and produce graphs in a uniform way. PMID:29576953

Optimizing ACS NSQIP modeling for evaluation of surgical quality and risk: patient risk adjustment, procedure mix adjustment, shrinkage adjustment, and surgical focus.

PubMed

Cohen, Mark E; Ko, Clifford Y; Bilimoria, Karl Y; Zhou, Lynn; Huffman, Kristopher; Wang, Xue; Liu, Yaoming; Kraemer, Kari; Meng, Xiangju; Merkow, Ryan; Chow, Warren; Matel, Brian; Richards, Karen; Hart, Amy J; Dimick, Justin B; Hall, Bruce L

2013-08-01

The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) collects detailed clinical data from participating hospitals using standardized data definitions, analyzes these data, and provides participating hospitals with reports that permit risk-adjusted comparisons with a surgical quality standard. Since its inception, the ACS NSQIP has worked to refine surgical outcomes measurements and enhance statistical methods to improve the reliability and validity of this hospital profiling. From an original focus on controlling for between-hospital differences in patient risk factors with logistic regression, ACS NSQIP has added a variable to better adjust for the complexity and risk profile of surgical procedures (procedure mix adjustment) and stabilized estimates derived from small samples by using a hierarchical model with shrinkage adjustment. New models have been developed focusing on specific surgical procedures (eg, "Procedure Targeted" models), which provide opportunities to incorporate indication and other procedure-specific variables and outcomes to improve risk adjustment. In addition, comparative benchmark reports given to participating hospitals have been expanded considerably to allow more detailed evaluations of performance. Finally, procedures have been developed to estimate surgical risk for individual patients. This article describes the development of, and justification for, these new statistical methods and reporting strategies in ACS NSQIP. Copyright © 2013 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

40 CFR 1048.510 - What transient duty cycles apply for laboratory testing?

Code of Federal Regulations, 2013 CFR

2013-07-01

... model year, measure emissions by testing the engine on a dynamometer with the duty cycle described in Appendix II to determine whether it meets the transient emission standards in § 1048.101(a). (b) Calculate cycle statistics and compare with the established criteria as specified in 40 CFR 1065.514 to confirm...

40 CFR 1048.510 - What transient duty cycles apply for laboratory testing?

Code of Federal Regulations, 2011 CFR

2011-07-01

... model year, measure emissions by testing the engine on a dynamometer with the duty cycle described in Appendix II to determine whether it meets the transient emission standards in § 1048.101(a). (b) Calculate cycle statistics and compare with the established criteria as specified in 40 CFR 1065.514 to confirm...

40 CFR 1048.510 - What transient duty cycles apply for laboratory testing?

Code of Federal Regulations, 2014 CFR

2014-07-01

... model year, measure emissions by testing the engine on a dynamometer with the duty cycle described in Appendix II to determine whether it meets the transient emission standards in § 1048.101(a). (b) Calculate cycle statistics and compare with the established criteria as specified in 40 CFR 1065.514 to confirm...

40 CFR 1048.510 - What transient duty cycles apply for laboratory testing?

Code of Federal Regulations, 2012 CFR

2012-07-01

... model year, measure emissions by testing the engine on a dynamometer with the duty cycle described in Appendix II to determine whether it meets the transient emission standards in § 1048.101(a). (b) Calculate cycle statistics and compare with the established criteria as specified in 40 CFR 1065.514 to confirm...

Estimating and modelling cure in population-based cancer studies within the framework of flexible parametric survival models.

PubMed

Andersson, Therese M L; Dickman, Paul W; Eloranta, Sandra; Lambert, Paul C

2011-06-22

When the mortality among a cancer patient group returns to the same level as in the general population, that is, the patients no longer experience excess mortality, the patients still alive are considered "statistically cured". Cure models can be used to estimate the cure proportion as well as the survival function of the "uncured". One limitation of parametric cure models is that the functional form of the survival of the "uncured" has to be specified. It can sometimes be hard to find a survival function flexible enough to fit the observed data, for example, when there is high excess hazard within a few months from diagnosis, which is common among older age groups. This has led to the exclusion of older age groups in population-based cancer studies using cure models. Here we have extended the flexible parametric survival model to incorporate cure as a special case to estimate the cure proportion and the survival of the "uncured". Flexible parametric survival models use splines to model the underlying hazard function, and therefore no parametric distribution has to be specified. We have compared the fit from standard cure models to our flexible cure model, using data on colon cancer patients in Finland. This new method gives similar results to a standard cure model, when it is reliable, and better fit when the standard cure model gives biased estimates. Cure models within the framework of flexible parametric models enables cure modelling when standard models give biased estimates. These flexible cure models enable inclusion of older age groups and can give stage-specific estimates, which is not always possible from parametric cure models. © 2011 Andersson et al; licensee BioMed Central Ltd.

Estimating and modelling cure in population-based cancer studies within the framework of flexible parametric survival models

PubMed Central

2011-01-01

Background When the mortality among a cancer patient group returns to the same level as in the general population, that is, the patients no longer experience excess mortality, the patients still alive are considered "statistically cured". Cure models can be used to estimate the cure proportion as well as the survival function of the "uncured". One limitation of parametric cure models is that the functional form of the survival of the "uncured" has to be specified. It can sometimes be hard to find a survival function flexible enough to fit the observed data, for example, when there is high excess hazard within a few months from diagnosis, which is common among older age groups. This has led to the exclusion of older age groups in population-based cancer studies using cure models. Methods Here we have extended the flexible parametric survival model to incorporate cure as a special case to estimate the cure proportion and the survival of the "uncured". Flexible parametric survival models use splines to model the underlying hazard function, and therefore no parametric distribution has to be specified. Results We have compared the fit from standard cure models to our flexible cure model, using data on colon cancer patients in Finland. This new method gives similar results to a standard cure model, when it is reliable, and better fit when the standard cure model gives biased estimates. Conclusions Cure models within the framework of flexible parametric models enables cure modelling when standard models give biased estimates. These flexible cure models enable inclusion of older age groups and can give stage-specific estimates, which is not always possible from parametric cure models. PMID:21696598

A statistical analysis of energy and power demand for the tractive purposes of an electric vehicle in urban traffic - an analysis of a short and long observation period

NASA Astrophysics Data System (ADS)

Slaski, G.; Ohde, B.

2016-09-01

The article presents the results of a statistical dispersion analysis of an energy and power demand for tractive purposes of a battery electric vehicle. The authors compare data distribution for different values of an average speed in two approaches, namely a short and long period of observation. The short period of observation (generally around several hundred meters) results from a previously proposed macroscopic energy consumption model based on an average speed per road section. This approach yielded high values of standard deviation and coefficient of variation (the ratio between standard deviation and the mean) around 0.7-1.2. The long period of observation (about several kilometers long) is similar in length to standardized speed cycles used in testing a vehicle energy consumption and available range. The data were analysed to determine the impact of observation length on the energy and power demand variation. The analysis was based on a simulation of electric power and energy consumption performed with speed profiles data recorded in Poznan agglomeration.

Uncertainty Analysis of Instrument Calibration and Application

NASA Technical Reports Server (NTRS)

Tripp, John S.; Tcheng, Ping

1999-01-01

Experimental aerodynamic researchers require estimated precision and bias uncertainties of measured physical quantities, typically at 95 percent confidence levels. Uncertainties of final computed aerodynamic parameters are obtained by propagation of individual measurement uncertainties through the defining functional expressions. In this paper, rigorous mathematical techniques are extended to determine precision and bias uncertainties of any instrument-sensor system. Through this analysis, instrument uncertainties determined through calibration are now expressed as functions of the corresponding measurement for linear and nonlinear univariate and multivariate processes. Treatment of correlated measurement precision error is developed. During laboratory calibration, calibration standard uncertainties are assumed to be an order of magnitude less than those of the instrument being calibrated. Often calibration standards do not satisfy this assumption. This paper applies rigorous statistical methods for inclusion of calibration standard uncertainty and covariance due to the order of their application. The effects of mathematical modeling error on calibration bias uncertainty are quantified. The effects of experimental design on uncertainty are analyzed. The importance of replication is emphasized, techniques for estimation of both bias and precision uncertainties using replication are developed. Statistical tests for stationarity of calibration parameters over time are obtained.

Three-Dimensional Algebraic Models of the tRNA Code and 12 Graphs for Representing the Amino Acids.

PubMed

José, Marco V; Morgado, Eberto R; Guimarães, Romeu Cardoso; Zamudio, Gabriel S; de Farías, Sávio Torres; Bobadilla, Juan R; Sosa, Daniela

2014-08-11

Three-dimensional algebraic models, also called Genetic Hotels, are developed to represent the Standard Genetic Code, the Standard tRNA Code (S-tRNA-C), and the Human tRNA code (H-tRNA-C). New algebraic concepts are introduced to be able to describe these models, to wit, the generalization of the 2n-Klein Group and the concept of a subgroup coset with a tail. We found that the H-tRNA-C displayed broken symmetries in regard to the S-tRNA-C, which is highly symmetric. We also show that there are only 12 ways to represent each of the corresponding phenotypic graphs of amino acids. The averages of statistical centrality measures of the 12 graphs for each of the three codes are carried out and they are statistically compared. The phenotypic graphs of the S-tRNA-C display a common triangular prism of amino acids in 10 out of the 12 graphs, whilst the corresponding graphs for the H-tRNA-C display only two triangular prisms. The graphs exhibit disjoint clusters of amino acids when their polar requirement values are used. We contend that the S-tRNA-C is in a frozen-like state, whereas the H-tRNA-C may be in an evolving state.

78 FR 9055 - National Center for Health Statistics (NCHS), Classifications and Public Health Data Standards...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-07

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Centers for Disease Control and Prevention National Center for Health Statistics (NCHS), Classifications and Public Health Data Standards Staff, Announces the..., Medical Systems Administrator, Classifications and Public Health Data Standards Staff, NCHS, 3311 Toledo...

Analysis of statistical and standard algorithms for detecting muscle onset with surface electromyography.

PubMed

Tenan, Matthew S; Tweedell, Andrew J; Haynes, Courtney A

2017-01-01

The timing of muscle activity is a commonly applied analytic method to understand how the nervous system controls movement. This study systematically evaluates six classes of standard and statistical algorithms to determine muscle onset in both experimental surface electromyography (EMG) and simulated EMG with a known onset time. Eighteen participants had EMG collected from the biceps brachii and vastus lateralis while performing a biceps curl or knee extension, respectively. Three established methods and three statistical methods for EMG onset were evaluated. Linear envelope, Teager-Kaiser energy operator + linear envelope and sample entropy were the established methods evaluated while general time series mean/variance, sequential and batch processing of parametric and nonparametric tools, and Bayesian changepoint analysis were the statistical techniques used. Visual EMG onset (experimental data) and objective EMG onset (simulated data) were compared with algorithmic EMG onset via root mean square error and linear regression models for stepwise elimination of inferior algorithms. The top algorithms for both data types were analyzed for their mean agreement with the gold standard onset and evaluation of 95% confidence intervals. The top algorithms were all Bayesian changepoint analysis iterations where the parameter of the prior (p0) was zero. The best performing Bayesian algorithms were p0 = 0 and a posterior probability for onset determination at 60-90%. While existing algorithms performed reasonably, the Bayesian changepoint analysis methodology provides greater reliability and accuracy when determining the singular onset of EMG activity in a time series. Further research is needed to determine if this class of algorithms perform equally well when the time series has multiple bursts of muscle activity.

Improved score statistics for meta-analysis in single-variant and gene-level association studies.

PubMed

Yang, Jingjing; Chen, Sai; Abecasis, Gonçalo

2018-06-01

Meta-analysis is now an essential tool for genetic association studies, allowing them to combine large studies and greatly accelerating the pace of genetic discovery. Although the standard meta-analysis methods perform equivalently as the more cumbersome joint analysis under ideal settings, they result in substantial power loss under unbalanced settings with various case-control ratios. Here, we investigate the power loss problem by the standard meta-analysis methods for unbalanced studies, and further propose novel meta-analysis methods performing equivalently to the joint analysis under both balanced and unbalanced settings. We derive improved meta-score-statistics that can accurately approximate the joint-score-statistics with combined individual-level data, for both linear and logistic regression models, with and without covariates. In addition, we propose a novel approach to adjust for population stratification by correcting for known population structures through minor allele frequencies. In the simulated gene-level association studies under unbalanced settings, our method recovered up to 85% power loss caused by the standard methods. We further showed the power gain of our methods in gene-level tests with 26 unbalanced studies of age-related macular degeneration . In addition, we took the meta-analysis of three unbalanced studies of type 2 diabetes as an example to discuss the challenges of meta-analyzing multi-ethnic samples. In summary, our improved meta-score-statistics with corrections for population stratification can be used to construct both single-variant and gene-level association studies, providing a useful framework for ensuring well-powered, convenient, cross-study analyses. © 2018 WILEY PERIODICALS, INC.

Dark Photon Searches at BESIII

NASA Astrophysics Data System (ADS)

Wang, Dayong

Many models beyond the Standard Model, motivated by the recent astrophysical anomalies, predict a new type of weak-interacting degrees of freedom. Typical models include the possibility of the low-mass dark gauge bosons of a few GeV and thus making them accessible at the BESIII experiment running at the tau-charm region. The BESIII has recently searched such dark bosons in several decay modes using the high statistics data set collected at charmonium resonaces. This talk will summarize the recent BESIII results of these dark photon searches and related new physics studies.

Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression.

PubMed

Chen, Yanguang

2016-01-01

In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson's statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran's index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China's regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test.

Central Limit Theorems for Linear Statistics of Heavy Tailed Random Matrices

NASA Astrophysics Data System (ADS)

Benaych-Georges, Florent; Guionnet, Alice; Male, Camille

2014-07-01

We show central limit theorems (CLT) for the linear statistics of symmetric matrices with independent heavy tailed entries, including entries in the domain of attraction of α-stable laws and entries with moments exploding with the dimension, as in the adjacency matrices of Erdös-Rényi graphs. For the second model, we also prove a central limit theorem of the moments of its empirical eigenvalues distribution. The limit laws are Gaussian, but unlike the case of standard Wigner matrices, the normalization is the one of the classical CLT for independent random variables.

Statistical method to compare massive parallel sequencing pipelines.

PubMed

Elsensohn, M H; Leblay, N; Dimassi, S; Campan-Fournier, A; Labalme, A; Roucher-Boulez, F; Sanlaville, D; Lesca, G; Bardel, C; Roy, P

2017-03-01

Today, sequencing is frequently carried out by Massive Parallel Sequencing (MPS) that cuts drastically sequencing time and expenses. Nevertheless, Sanger sequencing remains the main validation method to confirm the presence of variants. The analysis of MPS data involves the development of several bioinformatic tools, academic or commercial. We present here a statistical method to compare MPS pipelines and test it in a comparison between an academic (BWA-GATK) and a commercial pipeline (TMAP-NextGENe®), with and without reference to a gold standard (here, Sanger sequencing), on a panel of 41 genes in 43 epileptic patients. This method used the number of variants to fit log-linear models for pairwise agreements between pipelines. To assess the heterogeneity of the margins and the odds ratios of agreement, four log-linear models were used: a full model, a homogeneous-margin model, a model with single odds ratio for all patients, and a model with single intercept. Then a log-linear mixed model was fitted considering the biological variability as a random effect. Among the 390,339 base-pairs sequenced, TMAP-NextGENe® and BWA-GATK found, on average, 2253.49 and 1857.14 variants (single nucleotide variants and indels), respectively. Against the gold standard, the pipelines had similar sensitivities (63.47% vs. 63.42%) and close but significantly different specificities (99.57% vs. 99.65%; p < 0.001). Same-trend results were obtained when only single nucleotide variants were considered (99.98% specificity and 76.81% sensitivity for both pipelines). The method allows thus pipeline comparison and selection. It is generalizable to all types of MPS data and all pipelines.

MSSM-inspired multifield inflation

NASA Astrophysics Data System (ADS)

Dubinin, M. N.; Petrova, E. Yu.; Pozdeeva, E. O.; Sumin, M. V.; Vernov, S. Yu.

2017-12-01

Despite the fact that experimentally with a high degree of statistical significance only a single Standard Model-like Higgs boson is discovered at the LHC, extended Higgs sectors with multiple scalar fields not excluded by combined fits of the data are more preferable theoretically for internally consistent realistic models of particle physics. We analyze the inflationary scenarios which could be induced by the two-Higgs-doublet potential of the Minimal Supersymmetric Standard Model (MSSM) where five scalar fields have non-minimal couplings to gravity. Observables following from such MSSM-inspired multifield inflation are calculated and a number of consistent inflationary scenarios are constructed. Cosmological evolution with different initial conditions for the multifield system leads to consequences fully compatible with observational data on the spectral index and the tensor-to-scalar ratio. It is demonstrated that the strong coupling approximation is precise enough to describe such inflationary scenarios.

Langley Wind Tunnel Data Quality Assurance-Check Standard Results

NASA Technical Reports Server (NTRS)

Hemsch, Michael J.; Grubb, John P.; Krieger, William B.; Cler, Daniel L.

2000-01-01

A framework for statistical evaluation, control and improvement of wind funnel measurement processes is presented The methodology is adapted from elements of the Measurement Assurance Plans developed by the National Bureau of Standards (now the National Institute of Standards and Technology) for standards and calibration laboratories. The present methodology is based on the notions of statistical quality control (SQC) together with check standard testing and a small number of customer repeat-run sets. The results of check standard and customer repeat-run -sets are analyzed using the statistical control chart-methods of Walter A. Shewhart long familiar to the SQC community. Control chart results are presented for. various measurement processes in five facilities at Langley Research Center. The processes include test section calibration, force and moment measurements with a balance, and instrument calibration.

Non-standard neutrino interactions in the mu–tau sector

DOE PAGES

Mocioiu, Irina; Wright, Warren

2015-04-01

We discuss neutrino mass hierarchy implications arising from the effects of non-standard neutrino interactions on muon rates in high statistics atmospheric neutrino oscillation experiments like IceCube DeepCore. We concentrate on the mu–tau sector, which is presently the least constrained. It is shown that the magnitude of the effects depends strongly on the sign of the ϵμτ parameter describing this non-standard interaction. A simple analytic model is used to understand the parameter space where differences between the two signs are maximized. We discuss how this effect is partially degenerate with changing the neutrino mass hierarchy, as well as how this degeneracymore » could be lifted.« less

Dynamical thresholding of pancake models: a promising variant of the HDM picture

NASA Astrophysics Data System (ADS)

Buchert, Thomas

Variants of pancake models are considered which allow for the construction of a phenomenological link to the galaxy formation process. A control parameter space is introduced which defines different scenarios of galaxy formation. The sensibility of statistical measures of the small-scale structure with respect to this parameter freedom is demonstrated. This property of the galaxy formation model, together with the consequences of enlarging the box size of the simulation to a `fair sample scale', form the basis of arguments to support the possible revival of the standard `Hot-Dark-Matter' model.

Forecasting coconut production in the Philippines with ARIMA model

NASA Astrophysics Data System (ADS)

Lim, Cristina Teresa

2015-02-01

The study aimed to depict the situation of the coconut industry in the Philippines for the future years applying Autoregressive Integrated Moving Average (ARIMA) method. Data on coconut production, one of the major industrial crops of the country, for the period of 1990 to 2012 were analyzed using time-series methods. Autocorrelation (ACF) and partial autocorrelation functions (PACF) were calculated for the data. Appropriate Box-Jenkins autoregressive moving average model was fitted. Validity of the model was tested using standard statistical techniques. The forecasting power of autoregressive moving average (ARMA) model was used to forecast coconut production for the eight leading years.

Confusion Noise Due To Asteroids: From Mid-infrared To Millimetre Wavelengths

NASA Astrophysics Data System (ADS)

Kelemen, Janos; Kiss, C.; Pal, A.; Muller, T.; Abraham, P.

2006-12-01

We developed a statistical model for the asteroid component of the infrared sky for wavelengths 5 μm <= λ <= 1000 μm based on the Statistical Asteroid Model (Tedesco et al., 2005). Far-infrared fluxes of 1.9 million asteroids -derived with the help of the Standard Thermal Model -are used to calculate confusion noise values and expected asteroid counts for infrared space instruments in operation or in the near future (e.g. Akari, Herschel and Planck). Our results show that the confusion noise due to asteroids will not increase the detection threshold for most of the sky. However, there are specific areas near the ecliptic plane where the effect of asteroids can be comparable to the contribution of Galactic cirrus emission and that of the extragalactic background. This work was supported by the European Space Agency (PECS #98011) and by the Hungarian Space Office (TP286)

Generalized two-dimensional chiral QED: Anomaly and exotic statistics

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

Saradzhev, F.M.

1997-07-01

We study the influence of the anomaly on the physical quantum picture of the generalized chiral Schwinger model defined on S{sup 1}. We show that the anomaly (i) results in the background linearly rising electric field and (ii) makes the spectrum of the physical Hamiltonian nonrelativistic without a massive boson. The physical matter fields acquire exotic statistics. We construct explicitly the algebra of the Poincar{acute e} generators and show that it differs from the Poincar{acute e} one. We exhibit the role of the vacuum Berry phase in the failure of the Poincar{acute e} algebra to close. We prove that, inmore » spite of the background electric field, such phenomenon as the total screening of external charges characteristic for the standard Schwinger model takes place in the generalized chiral Schwinger model, too. {copyright} {ital 1997} {ital The American Physical Society}« less

Effect of clustering on the emission of light charged particles

NASA Astrophysics Data System (ADS)

Kundu, Samir; Bhattacharya, C.; Rana, T. K.; Bhattacharya, S.; Pandey, R.; Banerjee, K.; Roy, Pratap; Meena, J. K.; Mukherjee, G.; Ghosh, T. K.; Mukhopadhyay, S.; Saha, A. K.; Sahoo, J. K.; Mandal Saha, R.; Srivastava, V.; Sinha, M.; Asgar, Md. A.

2018-04-01

Energy spectra of light charged particles emitted in the reaction p + {}^{27}Al → {}^{28}Si^{\\ast} have been studied and compared with statistical model calculation. Unlike 16O + 12C where a large deformation was observed in 28Si*, the energy spectra of α-particles were well explained by the statistical model calculation with standard "deformability parameters" obtained using the rotating liquid drop model. It seems that the α-clustering in the entrance channel causes extra deformation in 28Si* in the case of 16O + 12C, but the reanalysis of other published data shows that there are several cases where extra deformation was observed for composites produced via non-α-cluster entrance channels also. An empirical relation was found between mass-asymmetry in the entrance channel and deformation, which indicates that along with α-clustering, mass-asymmetry may also affect the emission of a light charged particle.

On the adequacy of identified Cole Cole models

NASA Astrophysics Data System (ADS)

Xiang, Jianping; Cheng, Daizhan; Schlindwein, F. S.; Jones, N. B.

2003-06-01

The Cole-Cole model has been widely used to interpret electrical geophysical data. Normally an iterative computer program is used to invert the frequency domain complex impedance data and simple error estimation is obtained from the squared difference of the measured (field) and calculated values over the full frequency range. Recently a new direct inversion algorithm was proposed for the 'optimal' estimation of the Cole-Cole parameters, which differs from existing inversion algorithms in that the estimated parameters are direct solutions of a set of equations without the need for an initial guess for initialisation. This paper first briefly investigates the advantages and disadvantages of the new algorithm compared to the standard Levenberg-Marquardt "ridge regression" algorithm. Then, and more importantly, we address the adequacy of the models resulting from both the "ridge regression" and the new algorithm, using two different statistical tests and we give objective statistical criteria for acceptance or rejection of the estimated models. The first is the standard χ2 technique. The second is a parameter-accuracy based test that uses a joint multi-normal distribution. Numerical results that illustrate the performance of both testing methods are given. The main goals of this paper are (i) to provide the source code for the new ''direct inversion'' algorithm in Matlab and (ii) to introduce and demonstrate two methods to determine the reliability of a set of data before data processing, i.e., to consider the adequacy of the resulting Cole-Cole model.

Interferometric tests of Planckian quantum geometry models

DOE PAGES

Kwon, Ohkyung; Hogan, Craig J.

2016-04-19

The effect of Planck scale quantum geometrical effects on measurements with interferometers is estimated with standard physics, and with a variety of proposed extensions. It is shown that effects are negligible in standard field theory with canonically quantized gravity. Statistical noise levels are estimated in a variety of proposals for nonstandard metric fluctuations, and these alternatives are constrained using upper bounds on stochastic metric fluctuations from LIGO. Idealized models of several interferometer system architectures are used to predict signal noise spectra in a quantum geometry that cannot be described by a fluctuating metric, in which position noise arises from holographicmore » bounds on directional information. Lastly, predictions in this case are shown to be close to current and projected experimental bounds.« less

75 FR 39265 - National Center for Health Statistics (NCHS), Classifications and Public Health Data Standards...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-07-08

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Centers for Disease Control and Prevention National Center for Health Statistics (NCHS), Classifications and Public Health Data Standards Staff, Announces the... Prevention, Classifications and Public Health Data Standards, 3311 Toledo Road, Room 2337, Hyattsville, MD...

78 FR 53148 - National Center for Health Statistics (NCHS), Classifications and Public Health Data Standards...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-28

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Centers for Disease Control and Prevention National Center for Health Statistics (NCHS), Classifications and Public Health Data Standards Staff, Announces the... Administrator, Classifications and Public Health Data Standards Staff, NCHS, 3311 Toledo Road, Room 2337...

75 FR 56549 - National Center for Health Statistics (NCHS), Classifications and Public Health Data Standards...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-16

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Centers for Disease Control and Prevention National Center for Health Statistics (NCHS), Classifications and Public Health Data Standards Staff, Announces the... Public Health Data Standards Staff, NCHS, 3311 Toledo Road, Room 2337, Hyattsville, Maryland 20782, e...

Statistics as Unbiased Estimators: Exploring the Teaching of Standard Deviation

ERIC Educational Resources Information Center

Wasserman, Nicholas H.; Casey, Stephanie; Champion, Joe; Huey, Maryann

2017-01-01

This manuscript presents findings from a study about the knowledge for and planned teaching of standard deviation. We investigate how understanding variance as an unbiased (inferential) estimator--not just a descriptive statistic for the variation (spread) in data--is related to teachers' instruction regarding standard deviation, particularly…

Integrating Dynamic Data and Sensors with Semantic 3D City Models in the Context of Smart Cities

NASA Astrophysics Data System (ADS)

Chaturvedi, K.; Kolbe, T. H.

2016-10-01

Smart cities provide effective integration of human, physical and digital systems operating in the built environment. The advancements in city and landscape models, sensor web technologies, and simulation methods play a significant role in city analyses and improving quality of life of citizens and governance of cities. Semantic 3D city models can provide substantial benefits and can become a central information backbone for smart city infrastructures. However, current generation semantic 3D city models are static in nature and do not support dynamic properties and sensor observations. In this paper, we propose a new concept called Dynamizer allowing to represent highly dynamic data and providing a method for injecting dynamic variations of city object properties into the static representation. The approach also provides direct capability to model complex patterns based on statistics and general rules and also, real-time sensor observations. The concept is implemented as an Application Domain Extension for the CityGML standard. However, it could also be applied to other GML-based application schemas including the European INSPIRE data themes and national standards for topography and cadasters like the British Ordnance Survey Mastermap or the German cadaster standard ALKIS.

Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models.

PubMed

Mulder, Han A; Rönnegård, Lars; Fikse, W Freddy; Veerkamp, Roel F; Strandberg, Erling

2013-07-04

Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike's information criterion using h-likelihood to select the best fitting model. We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike's information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike's information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring.

Matching the Statistical Model to the Research Question for Dental Caries Indices with Many Zero Counts.

PubMed

Preisser, John S; Long, D Leann; Stamm, John W

2017-01-01

Marginalized zero-inflated count regression models have recently been introduced for the statistical analysis of dental caries indices and other zero-inflated count data as alternatives to traditional zero-inflated and hurdle models. Unlike the standard approaches, the marginalized models directly estimate overall exposure or treatment effects by relating covariates to the marginal mean count. This article discusses model interpretation and model class choice according to the research question being addressed in caries research. Two data sets, one consisting of fictional dmft counts in 2 groups and the other on DMFS among schoolchildren from a randomized clinical trial comparing 3 toothpaste formulations to prevent incident dental caries, are analyzed with negative binomial hurdle, zero-inflated negative binomial, and marginalized zero-inflated negative binomial models. In the first example, estimates of treatment effects vary according to the type of incidence rate ratio (IRR) estimated by the model. Estimates of IRRs in the analysis of the randomized clinical trial were similar despite their distinctive interpretations. The choice of statistical model class should match the study's purpose, while accounting for the broad decline in children's caries experience, such that dmft and DMFS indices more frequently generate zero counts. Marginalized (marginal mean) models for zero-inflated count data should be considered for direct assessment of exposure effects on the marginal mean dental caries count in the presence of high frequencies of zero counts. © 2017 S. Karger AG, Basel.

Matching the Statistical Model to the Research Question for Dental Caries Indices with Many Zero Counts

PubMed Central

Preisser, John S.; Long, D. Leann; Stamm, John W.

2017-01-01

Marginalized zero-inflated count regression models have recently been introduced for the statistical analysis of dental caries indices and other zero-inflated count data as alternatives to traditional zero-inflated and hurdle models. Unlike the standard approaches, the marginalized models directly estimate overall exposure or treatment effects by relating covariates to the marginal mean count. This article discusses model interpretation and model class choice according to the research question being addressed in caries research. Two datasets, one consisting of fictional dmft counts in two groups and the other on DMFS among schoolchildren from a randomized clinical trial (RCT) comparing three toothpaste formulations to prevent incident dental caries, are analysed with negative binomial hurdle (NBH), zero-inflated negative binomial (ZINB), and marginalized zero-inflated negative binomial (MZINB) models. In the first example, estimates of treatment effects vary according to the type of incidence rate ratio (IRR) estimated by the model. Estimates of IRRs in the analysis of the RCT were similar despite their distinctive interpretations. Choice of statistical model class should match the study’s purpose, while accounting for the broad decline in children’s caries experience, such that dmft and DMFS indices more frequently generate zero counts. Marginalized (marginal mean) models for zero-inflated count data should be considered for direct assessment of exposure effects on the marginal mean dental caries count in the presence of high frequencies of zero counts. PMID:28291962

Statistical and dynamical forecast of regional precipitation after mature phase of ENSO

NASA Astrophysics Data System (ADS)

Sohn, S.; Min, Y.; Lee, J.; Tam, C.; Ahn, J.

2010-12-01

While the seasonal predictability of general circulation models (GCMs) has been improved, the current model atmosphere in the mid-latitude does not respond correctly to external forcing such as tropical sea surface temperature (SST), particularly over the East Asia and western North Pacific summer monsoon regions. In addition, the time-scale of prediction scope is considerably limited and the model forecast skill still is very poor beyond two weeks. Although recent studies indicate that coupled model based multi-model ensemble (MME) forecasts show the better performance, the long-lead forecasts exceeding 9 months still show a dramatic decrease of the seasonal predictability. This study aims at diagnosing the dynamical MME forecasts comprised of the state of art 1-tier models as well as comparing them with the statistical model forecasts, focusing on the East Asian summer precipitation predictions after mature phase of ENSO. The lagged impact of El Nino as major climate contributor on the summer monsoon in model environments is also evaluated, in the sense of the conditional probabilities. To evaluate the probability forecast skills, the reliability (attributes) diagram and the relative operating characteristics following the recommendations of the World Meteorological Organization (WMO) Standardized Verification System for Long-Range Forecasts are used in this study. The results should shed light on the prediction skill for dynamical model and also for the statistical model, in forecasting the East Asian summer monsoon rainfall with a long-lead time.

The effects of BleedArrest on hemorrhage control in a porcine model.

PubMed

Gegel, Brian; Burgert, James; Loughren, Michael; Johnson, Don

2012-01-01

The purpose of this study was to examine the effectiveness of the hemostatic agent BleedArrest compared to control. This was a prospective, experimental design employing an established porcine model of uncontrolled hemorrhage. The minimum number of animals (n=10 per group) was used to obtain a statistically valid result. There were no statistically significant differences between the groups (P>.05) indicating that the groups were equivalent on the following parameters: activating clotting time, the subject weights, core body temperatures, amount of one minute hemorrhage, arterial blood pressures, and the amount and percentage of total blood volume. There were significant differences in the amount of hemorrhage (P=.033) between the BleedArrest (mean=72, SD±72 mL) and control (mean=317.30, SD±112.02 mL). BleedArrest is statistically and clinically superior at controlling hemorrhage compared to the standard pressure dressing control group. In conclusion, BleedArrest is an effective hemostatic agent for use in civilian and military trauma management.

A phylogenetic transform enhances analysis of compositional microbiota data

PubMed Central

Silverman, Justin D; Washburne, Alex D; Mukherjee, Sayan; David, Lawrence A

2017-01-01

Surveys of microbial communities (microbiota), typically measured as relative abundance of species, have illustrated the importance of these communities in human health and disease. Yet, statistical artifacts commonly plague the analysis of relative abundance data. Here, we introduce the PhILR transform, which incorporates microbial evolutionary models with the isometric log-ratio transform to allow off-the-shelf statistical tools to be safely applied to microbiota surveys. We demonstrate that analyses of community-level structure can be applied to PhILR transformed data with performance on benchmarks rivaling or surpassing standard tools. Additionally, by decomposing distance in the PhILR transformed space, we identified neighboring clades that may have adapted to distinct human body sites. Decomposing variance revealed that covariation of bacterial clades within human body sites increases with phylogenetic relatedness. Together, these findings illustrate how the PhILR transform combines statistical and phylogenetic models to overcome compositional data challenges and enable evolutionary insights relevant to microbial communities. DOI: http://dx.doi.org/10.7554/eLife.21887.001 PMID:28198697

75 FR 53925 - Sea Turtle Conservation; Shrimp and Summer Flounder Trawling Requirements

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-02

... because of the statistical probability the candidate TED may not achieve the standard (i.e., control TED... the test with 4 turtle captures because of the statistical probability the candidate TED may not... because of the statistical probability the candidate TED may not achieve the standard (i.e., [[Page 53930...

On-demand Reporting of Risk-adjusted and Smoothed Rates for Quality Profiling in ACS NSQIP.

PubMed

Cohen, Mark E; Liu, Yaoming; Huffman, Kristopher M; Ko, Clifford Y; Hall, Bruce L

2016-12-01

Surgical quality improvement depends on hospitals having accurate and timely information about comparative performance. Profiling accuracy is improved by risk adjustment and shrinkage adjustment to stabilize estimates. These adjustments are included in ACS NSQIP reports, where hospital odds ratios (OR) are estimated using hierarchical models built on contemporaneous data. However, the timeliness of feedback remains an issue. We describe an alternative, nonhierarchical approach, which yields risk- and shrinkage-adjusted rates. In contrast to our "Traditional" NSQIP method, this approach uses preexisting equations, built on historical data, which permits hospitals to have near immediate access to profiling results. We compared our traditional method to this new "on-demand" approach with respect to outlier determinations, kappa statistics, and correlations between logged OR and standardized rates, for 12 models (4 surgical groups by 3 outcomes). When both methods used the same contemporaneous data, there were similar numbers of hospital outliers and correlations between logged OR and standardized rates were high. However, larger differences were observed when the effect of contemporaneous versus historical data was added to differences in statistical methodology. The on-demand, nonhierarchical approach provides results similar to the traditional hierarchical method and offers immediacy, an "over-time" perspective, application to a broader range of models and data subsets, and reporting of more easily understood rates. Although the nonhierarchical method results are now available "on-demand" in a web-based application, the hierarchical approach has advantages, which support its continued periodic publication as the gold standard for hospital profiling in the program.

Comparison of Quadrapolar™ radiofrequency lesions produced by standard versus modified technique: an experimental model.

PubMed

Safakish, Ramin

2017-01-01

Lower back pain (LBP) is a global public health issue and is associated with substantial financial costs and loss of quality of life. Over the years, different literature has provided different statistics regarding the causes of the back pain. The following statistic is the closest estimation regarding our patient population. The sacroiliac (SI) joint pain is responsible for LBP in 18%-30% of individuals with LBP. Quadrapolar™ radiofrequency ablation, which involves ablation of the nerves of the SI joint using heat, is a commonly used treatment for SI joint pain. However, the standard Quadrapolar radiofrequency procedure is not always effective at ablating all the sensory nerves that cause the pain in the SI joint. One of the major limitations of the standard Quadrapolar radiofrequency procedure is that it produces small lesions of ~4 mm in diameter. Smaller lesions increase the likelihood of failure to ablate all nociceptive input. In this study, we compare the standard Quadrapolar radiofrequency ablation technique to a modified Quadrapolar ablation technique that has produced improved patient outcomes in our clinic. The methodology of the two techniques are compared. In addition, we compare results from an experimental model comparing the lesion sizes produced by the two techniques. Taken together, the findings from this study suggest that the modified Quadrapolar technique provides longer lasting relief for the back pain that is caused by SI joint dysfunction. A randomized controlled clinical trial is the next step required to quantify the difference in symptom relief and quality of life produced by the two techniques.

Bayesian model averaging using particle filtering and Gaussian mixture modeling: Theory, concepts, and simulation experiments

NASA Astrophysics Data System (ADS)

Rings, Joerg; Vrugt, Jasper A.; Schoups, Gerrit; Huisman, Johan A.; Vereecken, Harry

2012-05-01

Bayesian model averaging (BMA) is a standard method for combining predictive distributions from different models. In recent years, this method has enjoyed widespread application and use in many fields of study to improve the spread-skill relationship of forecast ensembles. The BMA predictive probability density function (pdf) of any quantity of interest is a weighted average of pdfs centered around the individual (possibly bias-corrected) forecasts, where the weights are equal to posterior probabilities of the models generating the forecasts, and reflect the individual models skill over a training (calibration) period. The original BMA approach presented by Raftery et al. (2005) assumes that the conditional pdf of each individual model is adequately described with a rather standard Gaussian or Gamma statistical distribution, possibly with a heteroscedastic variance. Here we analyze the advantages of using BMA with a flexible representation of the conditional pdf. A joint particle filtering and Gaussian mixture modeling framework is presented to derive analytically, as closely and consistently as possible, the evolving forecast density (conditional pdf) of each constituent ensemble member. The median forecasts and evolving conditional pdfs of the constituent models are subsequently combined using BMA to derive one overall predictive distribution. This paper introduces the theory and concepts of this new ensemble postprocessing method, and demonstrates its usefulness and applicability by numerical simulation of the rainfall-runoff transformation using discharge data from three different catchments in the contiguous United States. The revised BMA method receives significantly lower-prediction errors than the original default BMA method (due to filtering) with predictive uncertainty intervals that are substantially smaller but still statistically coherent (due to the use of a time-variant conditional pdf).

Bayesian inference for joint modelling of longitudinal continuous, binary and ordinal events.

PubMed

Li, Qiuju; Pan, Jianxin; Belcher, John

2016-12-01

In medical studies, repeated measurements of continuous, binary and ordinal outcomes are routinely collected from the same patient. Instead of modelling each outcome separately, in this study we propose to jointly model the trivariate longitudinal responses, so as to take account of the inherent association between the different outcomes and thus improve statistical inferences. This work is motivated by a large cohort study in the North West of England, involving trivariate responses from each patient: Body Mass Index, Depression (Yes/No) ascertained with cut-off score not less than 8 at the Hospital Anxiety and Depression Scale, and Pain Interference generated from the Medical Outcomes Study 36-item short-form health survey with values returned on an ordinal scale 1-5. There are some well-established methods for combined continuous and binary, or even continuous and ordinal responses, but little work was done on the joint analysis of continuous, binary and ordinal responses. We propose conditional joint random-effects models, which take into account the inherent association between the continuous, binary and ordinal outcomes. Bayesian analysis methods are used to make statistical inferences. Simulation studies show that, by jointly modelling the trivariate outcomes, standard deviations of the estimates of parameters in the models are smaller and much more stable, leading to more efficient parameter estimates and reliable statistical inferences. In the real data analysis, the proposed joint analysis yields a much smaller deviance information criterion value than the separate analysis, and shows other good statistical properties too. © The Author(s) 2014.

External validation of ADO, DOSE, COTE and CODEX at predicting death in primary care patients with COPD using standard and machine learning approaches.

PubMed

Morales, Daniel R; Flynn, Rob; Zhang, Jianguo; Trucco, Emmanuel; Quint, Jennifer K; Zutis, Kris

2018-05-01

Several models for predicting the risk of death in people with chronic obstructive pulmonary disease (COPD) exist but have not undergone large scale validation in primary care. The objective of this study was to externally validate these models using statistical and machine learning approaches. We used a primary care COPD cohort identified using data from the UK Clinical Practice Research Datalink. Age-standardised mortality rates were calculated for the population by gender and discrimination of ADO (age, dyspnoea, airflow obstruction), COTE (COPD-specific comorbidity test), DOSE (dyspnoea, airflow obstruction, smoking, exacerbations) and CODEX (comorbidity, dyspnoea, airflow obstruction, exacerbations) at predicting death over 1-3 years measured using logistic regression and a support vector machine learning (SVM) method of analysis. The age-standardised mortality rate was 32.8 (95%CI 32.5-33.1) and 25.2 (95%CI 25.4-25.7) per 1000 person years for men and women respectively. Complete data were available for 54879 patients to predict 1-year mortality. ADO performed the best (c-statistic of 0.730) compared with DOSE (c-statistic 0.645), COTE (c-statistic 0.655) and CODEX (c-statistic 0.649) at predicting 1-year mortality. Discrimination of ADO and DOSE improved at predicting 1-year mortality when combined with COTE comorbidities (c-statistic 0.780 ADO + COTE; c-statistic 0.727 DOSE + COTE). Discrimination did not change significantly over 1-3 years. Comparable results were observed using SVM. In primary care, ADO appears superior at predicting death in COPD. Performance of ADO and DOSE improved when combined with COTE comorbidities suggesting better models may be generated with additional data facilitated using novel approaches. Copyright © 2018. Published by Elsevier Ltd.

Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup.

PubMed

Liu, Hongyou; Gomez, Miguel-Ángel; Lago-Peñas, Carlos; Sampaio, Jaime

2015-01-01

Identifying match statistics that strongly contribute to winning in football matches is a very important step towards a more predictive and prescriptive performance analysis. The current study aimed to determine relationships between 24 match statistics and the match outcome (win, loss and draw) in all games and close games of the group stage of FIFA World Cup (2014, Brazil) by employing the generalised linear model. The cumulative logistic regression was run in the model taking the value of each match statistic as independent variable to predict the logarithm of the odds of winning. Relationships were assessed as effects of a two-standard-deviation increase in the value of each variable on the change in the probability of a team winning a match. Non-clinical magnitude-based inferences were employed and were evaluated by using the smallest worthwhile change. Results showed that for all the games, nine match statistics had clearly positive effects on the probability of winning (Shot, Shot on Target, Shot from Counter Attack, Shot from Inside Area, Ball Possession, Short Pass, Average Pass Streak, Aerial Advantage and Tackle), four had clearly negative effects (Shot Blocked, Cross, Dribble and Red Card), other 12 statistics had either trivial or unclear effects. While for the close games, the effects of Aerial Advantage and Yellow Card turned to trivial and clearly negative, respectively. Information from the tactical modelling can provide a more thorough and objective match understanding to coaches and performance analysts for evaluating post-match performances and for scouting upcoming oppositions.

Lower limb estimation from sparse landmarks using an articulated shape model.

PubMed

Zhang, Ju; Fernandez, Justin; Hislop-Jambrich, Jacqui; Besier, Thor F

2016-12-08

Rapid generation of lower limb musculoskeletal models is essential for clinically applicable patient-specific gait modeling. Estimation of muscle and joint contact forces requires accurate representation of bone geometry and pose, as well as their muscle attachment sites, which define muscle moment arms. Motion-capture is a routine part of gait assessment but contains relatively sparse geometric information. Standard methods for creating customized models from motion-capture data scale a reference model without considering natural shape variations. We present an articulated statistical shape model of the left lower limb with embedded anatomical landmarks and muscle attachment regions. This model is used in an automatic workflow, implemented in an easy-to-use software application, that robustly and accurately estimates realistic lower limb bone geometry, pose, and muscle attachment regions from seven commonly used motion-capture landmarks. Estimated bone models were validated on noise-free marker positions to have a lower (p=0.001) surface-to-surface root-mean-squared error of 4.28mm, compared to 5.22mm using standard isotropic scaling. Errors at a variety of anatomical landmarks were also lower (8.6mm versus 10.8mm, p=0.001). We improve upon standard lower limb model scaling methods with shape model-constrained realistic bone geometries, regional muscle attachment sites, and higher accuracy. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Comparison of Student Understanding of Seasons Using Inquiry and Didactic Teaching Methods

NASA Astrophysics Data System (ADS)

Ashcraft, Paul G.

2006-02-01

Student performance on open-ended questions concerning seasons in a university physical science content course was examined to note differences between classes that experienced inquiry using a 5-E lesson planning model and those that experienced the same content with a traditional, didactic lesson. The class examined is a required content course for elementary education majors and understanding the seasons is part of the university's state's elementary science standards. The two self-selected groups of students showed no statistically significant differences in pre-test scores, while there were statistically significant differences between the groups' post-test scores with those who participated in inquiry-based activities scoring higher. There were no statistically significant differences between the pre-test and the post-test for the students who experienced didactic teaching, while there were statistically significant improvements for the students who experienced the 5-E lesson.

Statistical characteristics of the spatial distribution of territorial contamination by radionuclides from the Chernobyl accident

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

Arutyunyan, R.V.; Bol`shov, L.A.; Vasil`ev, S.K.

1994-06-01

The objective of this study was to clarify a number of issues related to the spatial distribution of contaminants from the Chernobyl accident. The effects of local statistics were addressed by collecting and analyzing (for Cesium 137) soil samples from a number of regions, and it was found that sample activity differed by a factor of 3-5. The effect of local non-uniformity was estimated by modeling the distribution of the average activity of a set of five samples for each of the regions, with the spread in the activities for a {+-}2 range being equal to 25%. The statistical characteristicsmore » of the distribution of contamination were then analyzed and found to be a log-normal distribution with the standard deviation being a function of test area. All data for the Bryanskaya Oblast area were analyzed statistically and were adequately described by a log-normal function.« less

Statistical optics

NASA Astrophysics Data System (ADS)

Goodman, J. W.

This book is based on the thesis that some training in the area of statistical optics should be included as a standard part of any advanced optics curriculum. Random variables are discussed, taking into account definitions of probability and random variables, distribution functions and density functions, an extension to two or more random variables, statistical averages, transformations of random variables, sums of real random variables, Gaussian random variables, complex-valued random variables, and random phasor sums. Other subjects examined are related to random processes, some first-order properties of light waves, the coherence of optical waves, some problems involving high-order coherence, effects of partial coherence on imaging systems, imaging in the presence of randomly inhomogeneous media, and fundamental limits in photoelectric detection of light. Attention is given to deterministic versus statistical phenomena and models, the Fourier transform, and the fourth-order moment of the spectrum of a detected speckle image.

Fusion of multiscale wavelet-based fractal analysis on retina image for stroke prediction.

PubMed

Che Azemin, M Z; Kumar, Dinesh K; Wong, T Y; Wang, J J; Kawasaki, R; Mitchell, P; Arjunan, Sridhar P

2010-01-01

In this paper, we present a novel method of analyzing retinal vasculature using Fourier Fractal Dimension to extract the complexity of the retinal vasculature enhanced at different wavelet scales. Logistic regression was used as a fusion method to model the classifier for 5-year stroke prediction. The efficacy of this technique has been tested using standard pattern recognition performance evaluation, Receivers Operating Characteristics (ROC) analysis and medical prediction statistics, odds ratio. Stroke prediction model was developed using the proposed system.

Standard deviation and standard error of the mean.

PubMed

Lee, Dong Kyu; In, Junyong; Lee, Sangseok

2015-06-01

In most clinical and experimental studies, the standard deviation (SD) and the estimated standard error of the mean (SEM) are used to present the characteristics of sample data and to explain statistical analysis results. However, some authors occasionally muddle the distinctive usage between the SD and SEM in medical literature. Because the process of calculating the SD and SEM includes different statistical inferences, each of them has its own meaning. SD is the dispersion of data in a normal distribution. In other words, SD indicates how accurately the mean represents sample data. However the meaning of SEM includes statistical inference based on the sampling distribution. SEM is the SD of the theoretical distribution of the sample means (the sampling distribution). While either SD or SEM can be applied to describe data and statistical results, one should be aware of reasonable methods with which to use SD and SEM. We aim to elucidate the distinctions between SD and SEM and to provide proper usage guidelines for both, which summarize data and describe statistical results.

Standard deviation and standard error of the mean

PubMed Central

In, Junyong; Lee, Sangseok

2015-01-01

In most clinical and experimental studies, the standard deviation (SD) and the estimated standard error of the mean (SEM) are used to present the characteristics of sample data and to explain statistical analysis results. However, some authors occasionally muddle the distinctive usage between the SD and SEM in medical literature. Because the process of calculating the SD and SEM includes different statistical inferences, each of them has its own meaning. SD is the dispersion of data in a normal distribution. In other words, SD indicates how accurately the mean represents sample data. However the meaning of SEM includes statistical inference based on the sampling distribution. SEM is the SD of the theoretical distribution of the sample means (the sampling distribution). While either SD or SEM can be applied to describe data and statistical results, one should be aware of reasonable methods with which to use SD and SEM. We aim to elucidate the distinctions between SD and SEM and to provide proper usage guidelines for both, which summarize data and describe statistical results. PMID:26045923

A spatial scan statistic for nonisotropic two-level risk cluster.

PubMed

Li, Xiao-Zhou; Wang, Jin-Feng; Yang, Wei-Zhong; Li, Zhong-Jie; Lai, Sheng-Jie

2012-01-30

Spatial scan statistic methods are commonly used for geographical disease surveillance and cluster detection. The standard spatial scan statistic does not model any variability in the underlying risks of subregions belonging to a detected cluster. For a multilevel risk cluster, the isotonic spatial scan statistic could model a centralized high-risk kernel in the cluster. Because variations in disease risks are anisotropic owing to different social, economical, or transport factors, the real high-risk kernel will not necessarily take the central place in a whole cluster area. We propose a spatial scan statistic for a nonisotropic two-level risk cluster, which could be used to detect a whole cluster and a noncentralized high-risk kernel within the cluster simultaneously. The performance of the three methods was evaluated through an intensive simulation study. Our proposed nonisotropic two-level method showed better power and geographical precision with two-level risk cluster scenarios, especially for a noncentralized high-risk kernel. Our proposed method is illustrated using the hand-foot-mouth disease data in Pingdu City, Shandong, China in May 2009, compared with two other methods. In this practical study, the nonisotropic two-level method is the only way to precisely detect a high-risk area in a detected whole cluster. Copyright © 2011 John Wiley & Sons, Ltd.

Accuracy of Digital vs. Conventional Implant Impressions

PubMed Central

Lee, Sang J.; Betensky, Rebecca A.; Gianneschi, Grace E.; Gallucci, German O.

2015-01-01

The accuracy of digital impressions greatly influences the clinical viability in implant restorations. The aim of this study is to compare the accuracy of gypsum models acquired from the conventional implant impression to digitally milled models created from direct digitalization by three-dimensional analysis. Thirty gypsum and 30 digitally milled models impressed directly from a reference model were prepared. The models were scanned by a laboratory scanner and 30 STL datasets from each group were imported to an inspection software. The datasets were aligned to the reference dataset by a repeated best fit algorithm and 10 specified contact locations of interest were measured in mean volumetric deviations. The areas were pooled by cusps, fossae, interproximal contacts, horizontal and vertical axes of implant position and angulation. The pooled areas were statistically analysed by comparing each group to the reference model to investigate the mean volumetric deviations accounting for accuracy and standard deviations for precision. Milled models from digital impressions had comparable accuracy to gypsum models from conventional impressions. However, differences in fossae and vertical displacement of the implant position from the gypsum and digitally milled models compared to the reference model, exhibited statistical significance (p<0.001, p=0.020 respectively). PMID:24720423

Visually guided tube thoracostomy insertion comparison to standard of care in a large animal model.

PubMed

Hernandez, Matthew C; Vogelsang, David; Anderson, Jeff R; Thiels, Cornelius A; Beilman, Gregory; Zielinski, Martin D; Aho, Johnathon M

2017-04-01

Tube thoracostomy (TT) is a lifesaving procedure for a variety of thoracic pathologies. The most commonly utilized method for placement involves open dissection and blind insertion. Image guided placement is commonly utilized but is limited by an inability to see distal placement location. Unfortunately, TT is not without complications. We aim to demonstrate the feasibility of a disposable device to allow for visually directed TT placement compared to the standard of care in a large animal model. Three swine were sequentially orotracheally intubated and anesthetized. TT was conducted utilizing a novel visualization device, tube thoracostomy visual trocar (TTVT) and standard of care (open technique). Position of the TT in the chest cavity were recorded using direct thoracoscopic inspection and radiographic imaging with the operator blinded to results. Complications were evaluated using a validated complication grading system. Standard descriptive statistical analyses were performed. Thirty TT were placed, 15 using TTVT technique, 15 using standard of care open technique. All of the TT placed using TTVT were without complication and in optimal position. Conversely, 27% of TT placed using standard of care open technique resulted in complications. Necropsy revealed no injury to intrathoracic organs. Visual directed TT placement using TTVT is feasible and non-inferior to the standard of care in a large animal model. This improvement in instrumentation has the potential to greatly improve the safety of TT. Further study in humans is required. Therapeutic Level II. Copyright © 2017 Elsevier Ltd. All rights reserved.

Obtaining mathematical models for assessing efficiency of dust collectors using integrated system of analysis and data management STATISTICA Design of Experiments

NASA Astrophysics Data System (ADS)

Azarov, A. V.; Zhukova, N. S.; Kozlovtseva, E. Yu; Dobrinsky, D. R.

2018-05-01

The article considers obtaining mathematical models to assess the efficiency of the dust collectors using an integrated system of analysis and data management STATISTICA Design of Experiments. The procedure for obtaining mathematical models and data processing is considered by the example of laboratory studies on a mounted installation containing a dust collector in counter-swirling flows (CSF) using gypsum dust of various fractions. Planning of experimental studies has been carried out in order to reduce the number of experiments and reduce the cost of experimental research. A second-order non-position plan (Box-Bencken plan) was used, which reduced the number of trials from 81 to 27. The order of statistical data research of Box-Benken plan using standard tools of integrated system for analysis and data management STATISTICA Design of Experiments is considered. Results of statistical data processing with significance estimation of coefficients and adequacy of mathematical models are presented.

pyblocxs: Bayesian Low-Counts X-ray Spectral Analysis in Sherpa

NASA Astrophysics Data System (ADS)

Siemiginowska, A.; Kashyap, V.; Refsdal, B.; van Dyk, D.; Connors, A.; Park, T.

2011-07-01

Typical X-ray spectra have low counts and should be modeled using the Poisson distribution. However, χ2 statistic is often applied as an alternative and the data are assumed to follow the Gaussian distribution. A variety of weights to the statistic or a binning of the data is performed to overcome the low counts issues. However, such modifications introduce biases or/and a loss of information. Standard modeling packages such as XSPEC and Sherpa provide the Poisson likelihood and allow computation of rudimentary MCMC chains, but so far do not allow for setting a full Bayesian model. We have implemented a sophisticated Bayesian MCMC-based algorithm to carry out spectral fitting of low counts sources in the Sherpa environment. The code is a Python extension to Sherpa and allows to fit a predefined Sherpa model to high-energy X-ray spectral data and other generic data. We present the algorithm and discuss several issues related to the implementation, including flexible definition of priors and allowing for variations in the calibration information.

A statistical model to estimate refractivity turbulence structure constant C sub n sup 2 in the free atmosphere

NASA Technical Reports Server (NTRS)

Warnock, J. M.; Vanzandt, T. E.

1986-01-01

A computer program has been tested and documented (Warnock and VanZandt, 1985) that estimates mean values of the refractivity turbulence structure constant in the stable free atmosphere from standard National Weather Service balloon data or an equivalent data set. The program is based on the statistical model for the occurrence of turbulence developed by VanZandt et al. (1981). Height profiles of the estimated refractivity turbulence structure constant agree well with profiles measured by the Sunset radar with a height resolution of about 1 km. The program also estimates the energy dissipation rate (epsilon), but because of the lack of suitable observations of epsilon, the model for epsilon has not yet been evaluated sufficiently to be used in routine applications. Vertical profiles of the refractivity turbulence structure constant were compared with profiles measured by both radar and optical remote sensors and good agreement was found. However, at times the scintillometer measurements were less than both the radar and model values.

Model identification using stochastic differential equation grey-box models in diabetes.

PubMed

Duun-Henriksen, Anne Katrine; Schmidt, Signe; Røge, Rikke Meldgaard; Møller, Jonas Bech; Nørgaard, Kirsten; Jørgensen, John Bagterp; Madsen, Henrik

2013-03-01

The acceptance of virtual preclinical testing of control algorithms is growing and thus also the need for robust and reliable models. Models based on ordinary differential equations (ODEs) can rarely be validated with standard statistical tools. Stochastic differential equations (SDEs) offer the possibility of building models that can be validated statistically and that are capable of predicting not only a realistic trajectory, but also the uncertainty of the prediction. In an SDE, the prediction error is split into two noise terms. This separation ensures that the errors are uncorrelated and provides the possibility to pinpoint model deficiencies. An identifiable model of the glucoregulatory system in a type 1 diabetes mellitus (T1DM) patient is used as the basis for development of a stochastic-differential-equation-based grey-box model (SDE-GB). The parameters are estimated on clinical data from four T1DM patients. The optimal SDE-GB is determined from likelihood-ratio tests. Finally, parameter tracking is used to track the variation in the "time to peak of meal response" parameter. We found that the transformation of the ODE model into an SDE-GB resulted in a significant improvement in the prediction and uncorrelated errors. Tracking of the "peak time of meal absorption" parameter showed that the absorption rate varied according to meal type. This study shows the potential of using SDE-GBs in diabetes modeling. Improved model predictions were obtained due to the separation of the prediction error. SDE-GBs offer a solid framework for using statistical tools for model validation and model development. © 2013 Diabetes Technology Society.

New Standards Require Teaching More Statistics: Are Preservice Secondary Mathematics Teachers Ready?

ERIC Educational Resources Information Center

Lovett, Jennifer N.; Lee, Hollylynne S.

2017-01-01

Mathematics teacher education programs often need to respond to changing expectations and standards for K-12 curriculum and accreditation. New standards for high school mathematics in the United States include a strong emphasis in statistics. This article reports results from a mixed methods cross-institutional study examining the preparedness of…

Standardized Effect Sizes for Moderated Conditional Fixed Effects with Continuous Moderator Variables

PubMed Central

Bodner, Todd E.

2017-01-01

Wilkinson and Task Force on Statistical Inference (1999) recommended that researchers include information on the practical magnitude of effects (e.g., using standardized effect sizes) to distinguish between the statistical and practical significance of research results. To date, however, researchers have not widely incorporated this recommendation into the interpretation and communication of the conditional effects and differences in conditional effects underlying statistical interactions involving a continuous moderator variable where at least one of the involved variables has an arbitrary metric. This article presents a descriptive approach to investigate two-way statistical interactions involving continuous moderator variables where the conditional effects underlying these interactions are expressed in standardized effect size metrics (i.e., standardized mean differences and semi-partial correlations). This approach permits researchers to evaluate and communicate the practical magnitude of particular conditional effects and differences in conditional effects using conventional and proposed guidelines, respectively, for the standardized effect size and therefore provides the researcher important supplementary information lacking under current approaches. The utility of this approach is demonstrated with two real data examples and important assumptions underlying the standardization process are highlighted. PMID:28484404

Methods for estimating the magnitude and frequency of peak streamflows at ungaged sites in and near the Oklahoma Panhandle

USGS Publications Warehouse

Smith, S. Jerrod; Lewis, Jason M.; Graves, Grant M.

2015-09-28

Generalized-least-squares multiple-linear regression analysis was used to formulate regression relations between peak-streamflow frequency statistics and basin characteristics. Contributing drainage area was the only basin characteristic determined to be statistically significant for all percentage of annual exceedance probabilities and was the only basin characteristic used in regional regression equations for estimating peak-streamflow frequency statistics on unregulated streams in and near the Oklahoma Panhandle. The regression model pseudo-coefficient of determination, converted to percent, for the Oklahoma Panhandle regional regression equations ranged from about 38 to 63 percent. The standard errors of prediction and the standard model errors for the Oklahoma Panhandle regional regression equations ranged from about 84 to 148 percent and from about 76 to 138 percent, respectively. These errors were comparable to those reported for regional peak-streamflow frequency regression equations for the High Plains areas of Texas and Colorado. The root mean square errors for the Oklahoma Panhandle regional regression equations (ranging from 3,170 to 92,000 cubic feet per second) were less than the root mean square errors for the Oklahoma statewide regression equations (ranging from 18,900 to 412,000 cubic feet per second); therefore, the Oklahoma Panhandle regional regression equations produce more accurate peak-streamflow statistic estimates for the irrigated period of record in the Oklahoma Panhandle than do the Oklahoma statewide regression equations. The regression equations developed in this report are applicable to streams that are not substantially affected by regulation, impoundment, or surface-water withdrawals. These regression equations are intended for use for stream sites with contributing drainage areas less than or equal to about 2,060 square miles, the maximum value for the independent variable used in the regression analysis.

A Framework for the Optimization of Discrete-Event Simulation Models

NASA Technical Reports Server (NTRS)

Joshi, B. D.; Unal, R.; White, N. H.; Morris, W. D.

1996-01-01

With the growing use of computer modeling and simulation, in all aspects of engineering, the scope of traditional optimization has to be extended to include simulation models. Some unique aspects have to be addressed while optimizing via stochastic simulation models. The optimization procedure has to explicitly account for the randomness inherent in the stochastic measures predicted by the model. This paper outlines a general purpose framework for optimization of terminating discrete-event simulation models. The methodology combines a chance constraint approach for problem formulation, together with standard statistical estimation and analyses techniques. The applicability of the optimization framework is illustrated by minimizing the operation and support resources of a launch vehicle, through a simulation model.

MQSA National Statistics

MedlinePlus

... Standards Act and Program MQSA Insights MQSA National Statistics Share Tweet Linkedin Pin it More sharing options ... but should level off with time. Archived Scorecard Statistics 2018 Scorecard Statistics 2017 Scorecard Statistics 2016 Scorecard ...

Assessing alternative measures of wealth in health research.

PubMed

Cubbin, Catherine; Pollack, Craig; Flaherty, Brian; Hayward, Mark; Sania, Ayesha; Vallone, Donna; Braveman, Paula

2011-05-01

We assessed whether it would be feasible to replace the standard measure of net worth with simpler measures of wealth in population-based studies examining associations between wealth and health. We used data from the 2004 Survey of Consumer Finances (respondents aged 25-64 years) and the 2004 Health and Retirement Survey (respondents aged 50 years or older) to construct logistic regression models relating wealth to health status and smoking. For our wealth measure, we used the standard measure of net worth as well as 9 simpler measures of wealth, and we compared results among the 10 models. In both data sets and for both health indicators, models using simpler wealth measures generated conclusions about the association between wealth and health that were similar to the conclusions generated by models using net worth. The magnitude and significance of the odds ratios were similar for the covariates in multivariate models, and the model-fit statistics for models using these simpler measures were similar to those for models using net worth. Our findings suggest that simpler measures of wealth may be acceptable in population-based studies of health.

Explorations in Statistics: Standard Deviations and Standard Errors

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2008-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This series in "Advances in Physiology Education" provides an opportunity to do just that: we will investigate basic concepts in statistics using the free software package R. Because this series uses R solely as a vehicle…

Unperturbed Schelling Segregation in Two or Three Dimensions

NASA Astrophysics Data System (ADS)

Barmpalias, George; Elwes, Richard; Lewis-Pye, Andrew

2016-09-01

Schelling's models of segregation, first described in 1969 (Am Econ Rev 59:488-493, 1969) are among the best known models of self-organising behaviour. Their original purpose was to identify mechanisms of urban racial segregation. But his models form part of a family which arises in statistical mechanics, neural networks, social science, and beyond, where populations of agents interact on networks. Despite extensive study, unperturbed Schelling models have largely resisted rigorous analysis, prior results generally focusing on variants in which noise is introduced into the dynamics, the resulting system being amenable to standard techniques from statistical mechanics or stochastic evolutionary game theory (Young in Individual strategy and social structure: an evolutionary theory of institutions, Princeton University Press, Princeton, 1998). A series of recent papers (Brandt et al. in: Proceedings of the 44th annual ACM symposium on theory of computing (STOC 2012), 2012); Barmpalias et al. in: 55th annual IEEE symposium on foundations of computer science, Philadelphia, 2014, J Stat Phys 158:806-852, 2015), has seen the first rigorous analyses of 1-dimensional unperturbed Schelling models, in an asymptotic framework largely unknown in statistical mechanics. Here we provide the first such analysis of 2- and 3-dimensional unperturbed models, establishing most of the phase diagram, and answering a challenge from Brandt et al. in: Proceedings of the 44th annual ACM symposium on theory of computing (STOC 2012), 2012).

A new method to address verification bias in studies of clinical screening tests: cervical cancer screening assays as an example.

PubMed

Xue, Xiaonan; Kim, Mimi Y; Castle, Philip E; Strickler, Howard D

2014-03-01

Studies to evaluate clinical screening tests often face the problem that the "gold standard" diagnostic approach is costly and/or invasive. It is therefore common to verify only a subset of negative screening tests using the gold standard method. However, undersampling the screen negatives can lead to substantial overestimation of the sensitivity and underestimation of the specificity of the diagnostic test. Our objective was to develop a simple and accurate statistical method to address this "verification bias." We developed a weighted generalized estimating equation approach to estimate, in a single model, the accuracy (eg, sensitivity/specificity) of multiple assays and simultaneously compare results between assays while addressing verification bias. This approach can be implemented using standard statistical software. Simulations were conducted to assess the proposed method. An example is provided using a cervical cancer screening trial that compared the accuracy of human papillomavirus and Pap tests, with histologic data as the gold standard. The proposed approach performed well in estimating and comparing the accuracy of multiple assays in the presence of verification bias. The proposed approach is an easy to apply and accurate method for addressing verification bias in studies of multiple screening methods. Copyright © 2014 Elsevier Inc. All rights reserved.

The Impact of Linking Distinct Achievement Test Scores on the Interpretation of Student Growth in Achievement

ERIC Educational Resources Information Center

Airola, Denise Tobin

2011-01-01

Changes to state tests impact the ability of State Education Agencies (SEAs) to monitor change in performance over time. The purpose of this study was to evaluate the Standardized Performance Growth Index (PGIz), a proposed statistical model for measuring change in student and school performance, across transitions in tests. The PGIz is a…

Complex dynamics and empirical evidence (Invited Paper)

NASA Astrophysics Data System (ADS)

Delli Gatti, Domenico; Gaffeo, Edoardo; Giulioni, Gianfranco; Gallegati, Mauro; Kirman, Alan; Palestrini, Antonio; Russo, Alberto

2005-05-01

Standard macroeconomics, based on a reductionist approach centered on the representative agent, is badly equipped to explain the empirical evidence where heterogeneity and industrial dynamics are the rule. In this paper we show that a simple agent-based model of heterogeneous financially fragile agents is able to replicate a large number of scaling type stylized facts with a remarkable degree of statistical precision.

A statistical study of gyro-averaging effects in a reduced model of drift-wave transport

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

Fonseca, Julio; Del-Castillo-Negrete, Diego B.; Sokolov, Igor M.

2016-08-25

Here, a statistical study of finite Larmor radius (FLR) effects on transport driven by electrostatic driftwaves is presented. The study is based on a reduced discrete Hamiltonian dynamical system known as the gyro-averaged standard map (GSM). In this system, FLR effects are incorporated through the gyro-averaging of a simplified weak-turbulence model of electrostatic fluctuations. Formally, the GSM is a modified version of the standard map in which the perturbation amplitude, K 0, becomes K 0J 0(more » $$\\hat{p}$$), where J 0 is the zeroth-order Bessel function and $$\\hat{p}$$ s the Larmor radius. Assuming a Maxwellian probability density function (pdf) for $$\\hat{p}$$ , we compute analytically and numerically the pdf and the cumulative distribution function of the effective drift-wave perturba- tion amplitude K 0J 0($$\\hat{p}$$). Using these results, we compute the probability of loss of confinement (i.e., global chaos), P c provides an upper bound for the escape rate, and that P t rovides a good estimate of the particle trapping rate. Lastly. the analytical results are compared with direct numerical Monte-Carlo simulations of particle transport.« less

Analysis of statistical misconception in terms of statistical reasoning

NASA Astrophysics Data System (ADS)

Maryati, I.; Priatna, N.

2018-05-01

Reasoning skill is needed for everyone to face globalization era, because every person have to be able to manage and use information from all over the world which can be obtained easily. Statistical reasoning skill is the ability to collect, group, process, interpret, and draw conclusion of information. Developing this skill can be done through various levels of education. However, the skill is low because many people assume that statistics is just the ability to count and using formulas and so do students. Students still have negative attitude toward course which is related to research. The purpose of this research is analyzing students’ misconception in descriptive statistic course toward the statistical reasoning skill. The observation was done by analyzing the misconception test result and statistical reasoning skill test; observing the students’ misconception effect toward statistical reasoning skill. The sample of this research was 32 students of math education department who had taken descriptive statistic course. The mean value of misconception test was 49,7 and standard deviation was 10,6 whereas the mean value of statistical reasoning skill test was 51,8 and standard deviation was 8,5. If the minimal value is 65 to state the standard achievement of a course competence, students’ mean value is lower than the standard competence. The result of students’ misconception study emphasized on which sub discussion that should be considered. Based on the assessment result, it was found that students’ misconception happen on this: 1) writing mathematical sentence and symbol well, 2) understanding basic definitions, 3) determining concept that will be used in solving problem. In statistical reasoning skill, the assessment was done to measure reasoning from: 1) data, 2) representation, 3) statistic format, 4) probability, 5) sample, and 6) association.

An emission-weighted proximity model for air pollution exposure assessment.

PubMed

Zou, Bin; Wilson, J Gaines; Zhan, F Benjamin; Zeng, Yongnian

2009-08-15

Among the most common spatial models for estimating personal exposure are Traditional Proximity Models (TPMs). Though TPMs are straightforward to configure and interpret, they are prone to extensive errors in exposure estimates and do not provide prospective estimates. To resolve these inherent problems with TPMs, we introduce here a novel Emission Weighted Proximity Model (EWPM) to improve the TPM, which takes into consideration the emissions from all sources potentially influencing the receptors. EWPM performance was evaluated by comparing the normalized exposure risk values of sulfur dioxide (SO(2)) calculated by EWPM with those calculated by TPM and monitored observations over a one-year period in two large Texas counties. In order to investigate whether the limitations of TPM in potential exposure risk prediction without recorded incidence can be overcome, we also introduce a hybrid framework, a 'Geo-statistical EWPM'. Geo-statistical EWPM is a synthesis of Ordinary Kriging Geo-statistical interpolation and EWPM. The prediction results are presented as two potential exposure risk prediction maps. The performance of these two exposure maps in predicting individual SO(2) exposure risk was validated with 10 virtual cases in prospective exposure scenarios. Risk values for EWPM were clearly more agreeable with the observed concentrations than those from TPM. Over the entire study area, the mean SO(2) exposure risk from EWPM was higher relative to TPM (1.00 vs. 0.91). The mean bias of the exposure risk values of 10 virtual cases between EWPM and 'Geo-statistical EWPM' are much smaller than those between TPM and 'Geo-statistical TPM' (5.12 vs. 24.63). EWPM appears to more accurately portray individual exposure relative to TPM. The 'Geo-statistical EWPM' effectively augments the role of the standard proximity model and makes it possible to predict individual risk in future exposure scenarios resulting in adverse health effects from environmental pollution.

Bias and inference from misspecified mixed‐effect models in stepped wedge trial analysis

PubMed Central

Fielding, Katherine L.; Davey, Calum; Aiken, Alexander M.; Hargreaves, James R.; Hayes, Richard J.

2017-01-01

Many stepped wedge trials (SWTs) are analysed by using a mixed‐effect model with a random intercept and fixed effects for the intervention and time periods (referred to here as the standard model). However, it is not known whether this model is robust to misspecification. We simulated SWTs with three groups of clusters and two time periods; one group received the intervention during the first period and two groups in the second period. We simulated period and intervention effects that were either common‐to‐all or varied‐between clusters. Data were analysed with the standard model or with additional random effects for period effect or intervention effect. In a second simulation study, we explored the weight given to within‐cluster comparisons by simulating a larger intervention effect in the group of the trial that experienced both the control and intervention conditions and applying the three analysis models described previously. Across 500 simulations, we computed bias and confidence interval coverage of the estimated intervention effect. We found up to 50% bias in intervention effect estimates when period or intervention effects varied between clusters and were treated as fixed effects in the analysis. All misspecified models showed undercoverage of 95% confidence intervals, particularly the standard model. A large weight was given to within‐cluster comparisons in the standard model. In the SWTs simulated here, mixed‐effect models were highly sensitive to departures from the model assumptions, which can be explained by the high dependence on within‐cluster comparisons. Trialists should consider including a random effect for time period in their SWT analysis model. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:28556355

Using decision trees to understand structure in missing data

PubMed Central

Tierney, Nicholas J; Harden, Fiona A; Harden, Maurice J; Mengersen, Kerrie L

2015-01-01

Objectives Demonstrate the application of decision trees—classification and regression trees (CARTs), and their cousins, boosted regression trees (BRTs)—to understand structure in missing data. Setting Data taken from employees at 3 different industrial sites in Australia. Participants 7915 observations were included. Materials and methods The approach was evaluated using an occupational health data set comprising results of questionnaires, medical tests and environmental monitoring. Statistical methods included standard statistical tests and the ‘rpart’ and ‘gbm’ packages for CART and BRT analyses, respectively, from the statistical software ‘R’. A simulation study was conducted to explore the capability of decision tree models in describing data with missingness artificially introduced. Results CART and BRT models were effective in highlighting a missingness structure in the data, related to the type of data (medical or environmental), the site in which it was collected, the number of visits, and the presence of extreme values. The simulation study revealed that CART models were able to identify variables and values responsible for inducing missingness. There was greater variation in variable importance for unstructured as compared to structured missingness. Discussion Both CART and BRT models were effective in describing structural missingness in data. CART models may be preferred over BRT models for exploratory analysis of missing data, and selecting variables important for predicting missingness. BRT models can show how values of other variables influence missingness, which may prove useful for researchers. Conclusions Researchers are encouraged to use CART and BRT models to explore and understand missing data. PMID:26124509

Optimization of Analytical Potentials for Coarse-Grained Biopolymer Models.

PubMed

Mereghetti, Paolo; Maccari, Giuseppe; Spampinato, Giulia Lia Beatrice; Tozzini, Valentina

2016-08-25

The increasing trend in the recent literature on coarse grained (CG) models testifies their impact in the study of complex systems. However, the CG model landscape is variegated: even considering a given resolution level, the force fields are very heterogeneous and optimized with very different parametrization procedures. Along the road for standardization of CG models for biopolymers, here we describe a strategy to aid building and optimization of statistics based analytical force fields and its implementation in the software package AsParaGS (Assisted Parameterization platform for coarse Grained modelS). Our method is based on the use and optimization of analytical potentials, optimized by targeting internal variables statistical distributions by means of the combination of different algorithms (i.e., relative entropy driven stochastic exploration of the parameter space and iterative Boltzmann inversion). This allows designing a custom model that endows the force field terms with a physically sound meaning. Furthermore, the level of transferability and accuracy can be tuned through the choice of statistical data set composition. The method-illustrated by means of applications to helical polypeptides-also involves the analysis of two and three variable distributions, and allows handling issues related to the FF term correlations. AsParaGS is interfaced with general-purpose molecular dynamics codes and currently implements the "minimalist" subclass of CG models (i.e., one bead per amino acid, Cα based). Extensions to nucleic acids and different levels of coarse graining are in the course.

Statistical analysis of tire treadwear data

DOT National Transportation Integrated Search

1985-03-01

This report describes the results of a statistical analysis of the treadwear : variability of radial tires subjected to the Uniform Tire Quality Grading (UTQG) : standard. Because unexplained variability in the treadwear portion of the standard : cou...

Phylogeography Takes a Relaxed Random Walk in Continuous Space and Time

PubMed Central

Lemey, Philippe; Rambaut, Andrew; Welch, John J.; Suchard, Marc A.

2010-01-01

Research aimed at understanding the geographic context of evolutionary histories is burgeoning across biological disciplines. Recent endeavors attempt to interpret contemporaneous genetic variation in the light of increasingly detailed geographical and environmental observations. Such interest has promoted the development of phylogeographic inference techniques that explicitly aim to integrate such heterogeneous data. One promising development involves reconstructing phylogeographic history on a continuous landscape. Here, we present a Bayesian statistical approach to infer continuous phylogeographic diffusion using random walk models while simultaneously reconstructing the evolutionary history in time from molecular sequence data. Moreover, by accommodating branch-specific variation in dispersal rates, we relax the most restrictive assumption of the standard Brownian diffusion process and demonstrate increased statistical efficiency in spatial reconstructions of overdispersed random walks by analyzing both simulated and real viral genetic data. We further illustrate how drawing inference about summary statistics from a fully specified stochastic process over both sequence evolution and spatial movement reveals important characteristics of a rabies epidemic. Together with recent advances in discrete phylogeographic inference, the continuous model developments furnish a flexible statistical framework for biogeographical reconstructions that is easily expanded upon to accommodate various landscape genetic features. PMID:20203288

Estimating peak-flow frequency statistics for selected gaged and ungaged sites in naturally flowing streams and rivers in Idaho

USGS Publications Warehouse

Wood, Molly S.; Fosness, Ryan L.; Skinner, Kenneth D.; Veilleux, Andrea G.

2016-06-27

The U.S. Geological Survey, in cooperation with the Idaho Transportation Department, updated regional regression equations to estimate peak-flow statistics at ungaged sites on Idaho streams using recent streamflow (flow) data and new statistical techniques. Peak-flow statistics with 80-, 67-, 50-, 43-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (1.25-, 1.50-, 2.00-, 2.33-, 5.00-, 10.0-, 25.0-, 50.0-, 100-, 200-, and 500-year recurrence intervals, respectively) were estimated for 192 streamgages in Idaho and bordering States with at least 10 years of annual peak-flow record through water year 2013. The streamgages were selected from drainage basins with little or no flow diversion or regulation. The peak-flow statistics were estimated by fitting a log-Pearson type III distribution to records of annual peak flows and applying two additional statistical methods: (1) the Expected Moments Algorithm to help describe uncertainty in annual peak flows and to better represent missing and historical record; and (2) the generalized Multiple Grubbs Beck Test to screen out potentially influential low outliers and to better fit the upper end of the peak-flow distribution. Additionally, a new regional skew was estimated for the Pacific Northwest and used to weight at-station skew at most streamgages. The streamgages were grouped into six regions (numbered 1_2, 3, 4, 5, 6_8, and 7, to maintain consistency in region numbering with a previous study), and the estimated peak-flow statistics were related to basin and climatic characteristics to develop regional regression equations using a generalized least squares procedure. Four out of 24 evaluated basin and climatic characteristics were selected for use in the final regional peak-flow regression equations.Overall, the standard error of prediction for the regional peak-flow regression equations ranged from 22 to 132 percent. Among all regions, regression model fit was best for region 4 in west-central Idaho (average standard error of prediction=46.4 percent; pseudo-R2>92 percent) and region 5 in central Idaho (average standard error of prediction=30.3 percent; pseudo-R2>95 percent). Regression model fit was poor for region 7 in southern Idaho (average standard error of prediction=103 percent; pseudo-R2<78 percent) compared to other regions because few streamgages in region 7 met the criteria for inclusion in the study, and the region’s semi-arid climate and associated variability in precipitation patterns causes substantial variability in peak flows.A drainage area ratio-adjustment method, using ratio exponents estimated using generalized least-squares regression, was presented as an alternative to the regional regression equations if peak-flow estimates are desired at an ungaged site that is close to a streamgage selected for inclusion in this study. The alternative drainage area ratio-adjustment method is appropriate for use when the drainage area ratio between the ungaged and gaged sites is between 0.5 and 1.5.The updated regional peak-flow regression equations had lower total error (standard error of prediction) than all regression equations presented in a 1982 study and in four of six regions presented in 2002 and 2003 studies in Idaho. A more extensive streamgage screening process used in the current study resulted in fewer streamgages used in the current study than in the 1982, 2002, and 2003 studies. Fewer streamgages used and the selection of different explanatory variables were likely causes of increased error in some regions compared to previous studies, but overall, regional peak‑flow regression model fit was generally improved for Idaho. The revised statistical procedures and increased streamgage screening applied in the current study most likely resulted in a more accurate representation of natural peak-flow conditions.The updated, regional peak-flow regression equations will be integrated in the U.S. Geological Survey StreamStats program to allow users to estimate basin and climatic characteristics and peak-flow statistics at ungaged locations of interest. StreamStats estimates peak-flow statistics with quantifiable certainty only when used at sites with basin and climatic characteristics within the range of input variables used to develop the regional regression equations. Both the regional regression equations and StreamStats should be used to estimate peak-flow statistics only in naturally flowing, relatively unregulated streams without substantial local influences to flow, such as large seeps, springs, or other groundwater-surface water interactions that are not widespread or characteristic of the respective region.

An expert panel-based study on recognition of gastro-esophageal reflux in difficult esophageal pH-impedance tracings.

PubMed

Smits, M J; Loots, C M; van Wijk, M P; Bredenoord, A J; Benninga, M A; Smout, A J P M

2015-05-01

Despite existing criteria for scoring gastro-esophageal reflux (GER) in esophageal multichannel pH-impedance measurement (pH-I) tracings, inter- and intra-rater variability is large and agreement with automated analysis is poor. To identify parameters of difficult to analyze pH-I patterns and combine these into a statistical model that can identify GER episodes with an international consensus as gold standard. Twenty-one experts from 10 countries were asked to mark GER presence for adult and pediatric pH-I patterns in an online pre-assessment. During a consensus meeting, experts voted on patterns not reaching majority consensus (>70% agreement). Agreement was calculated between raters, between consensus and individual raters, and between consensus and software generated automated analysis. With eight selected parameters, multiple logistic regression analysis was performed to describe an algorithm sensitive and specific for detection of GER. Majority consensus was reached for 35/79 episodes in the online pre-assessment (interrater κ = 0.332). Mean agreement between pre-assessment scores and final consensus was moderate (κ = 0.466). Combining eight pH-I parameters did not result in a statistically significant model able to identify presence of GER. Recognizing a pattern as retrograde is the best indicator of GER, with 100% sensitivity and 81% specificity with expert consensus as gold standard. Agreement between experts scoring difficult impedance patterns for presence or absence of GER is poor. Combining several characteristics into a statistical model did not improve diagnostic accuracy. Only the parameter 'retrograde propagation pattern' is an indicator of GER in difficult pH-I patterns. © 2015 John Wiley & Sons Ltd.

Kindergarten Predictors of Math Learning Disability

PubMed Central

Mazzocco, Michèle M. M.; Thompson, Richard E.

2009-01-01

The aim of the present study was to address how to effectively predict mathematics learning disability (MLD). Specifically, we addressed whether cognitive data obtained during kindergarten can effectively predict which children will have MLD in third grade, whether an abbreviated test battery could be as effective as a standard psychoeducational assessment at predicting MLD, and whether the abbreviated battery corresponded to the literature on MLD characteristics. Participants were 226 children who enrolled in a 4-year prospective longitudinal study during kindergarten. We administered measures of mathematics achievement, formal and informal mathematics ability, visual-spatial reasoning, and rapid automatized naming and examined which test scores and test items from kindergarten best predicted MLD at grades 2 and 3. Statistical models using standardized scores from the entire test battery correctly classified ~80–83 percent of the participants as having, or not having, MLD. Regression models using scores from only individual test items were less predictive than models containing the standard scores, except for models using a specific subset of test items that dealt with reading numerals, number constancy, magnitude judgments of one-digit numbers, or mental addition of one-digit numbers. These models were as accurate in predicting MLD as was the model including the entire set of standard scores from the battery of tests examined. Our findings indicate that it is possible to effectively predict which kindergartners are at risk for MLD, and thus the findings have implications for early screening of MLD. PMID:20084182

Toward standardized reporting for a cohort study on functioning: The Swiss Spinal Cord Injury Cohort Study.

PubMed

Prodinger, Birgit; Ballert, Carolina S; Brach, Mirjam; Brinkhof, Martin W G; Cieza, Alarcos; Hug, Kerstin; Jordan, Xavier; Post, Marcel W M; Scheel-Sailer, Anke; Schubert, Martin; Tennant, Alan; Stucki, Gerold

2016-02-01

Functioning is an important outcome to measure in cohort studies. Clear and operational outcomes are needed to judge the quality of a cohort study. This paper outlines guiding principles for reporting functioning in cohort studies and addresses some outstanding issues. Principles of how to standardize reporting of data from a cohort study on functioning, by deriving scores that are most useful for further statistical analysis and reporting, are outlined. The Swiss Spinal Cord Injury Cohort Study Community Survey serves as a case in point to provide a practical application of these principles. Development of reporting scores must be conceptually coherent and metrically sound. The International Classification of Functioning, Disability and Health (ICF) can serve as the frame of reference for this, with its categories serving as reference units for reporting. To derive a score for further statistical analysis and reporting, items measuring a single latent trait must be invariant across groups. The Rasch measurement model is well suited to test these assumptions. Our approach is a valuable guide for researchers and clinicians, as it fosters comparability of data, strengthens the comprehensiveness of scope, and provides invariant, interval-scaled data for further statistical analyses of functioning.

Incorporating Stroke Severity Into Hospital Measures of 30-Day Mortality After Ischemic Stroke Hospitalization.

PubMed

Schwartz, Jennifer; Wang, Yongfei; Qin, Li; Schwamm, Lee H; Fonarow, Gregg C; Cormier, Nicole; Dorsey, Karen; McNamara, Robert L; Suter, Lisa G; Krumholz, Harlan M; Bernheim, Susannah M

2017-11-01

The Centers for Medicare & Medicaid Services publicly reports a hospital-level stroke mortality measure that lacks stroke severity risk adjustment. Our objective was to describe novel measures of stroke mortality suitable for public reporting that incorporate stroke severity into risk adjustment. We linked data from the American Heart Association/American Stroke Association Get With The Guidelines-Stroke registry with Medicare fee-for-service claims data to develop the measures. We used logistic regression for variable selection in risk model development. We developed 3 risk-standardized mortality models for patients with acute ischemic stroke, all of which include the National Institutes of Health Stroke Scale score: one that includes other risk variables derived only from claims data (claims model); one that includes other risk variables derived from claims and clinical variables that could be obtained from electronic health record data (hybrid model); and one that includes other risk variables that could be derived only from electronic health record data (electronic health record model). The cohort used to develop and validate the risk models consisted of 188 975 hospital admissions at 1511 hospitals. The claims, hybrid, and electronic health record risk models included 20, 21, and 9 risk-adjustment variables, respectively; the C statistics were 0.81, 0.82, and 0.79, respectively (as compared with the current publicly reported model C statistic of 0.75); the risk-standardized mortality rates ranged from 10.7% to 19.0%, 10.7% to 19.1%, and 10.8% to 20.3%, respectively; the median risk-standardized mortality rate was 14.5% for all measures; and the odds of mortality for a high-mortality hospital (+1 SD) were 1.51, 1.52, and 1.52 times those for a low-mortality hospital (-1 SD), respectively. We developed 3 quality measures that demonstrate better discrimination than the Centers for Medicare & Medicaid Services' existing stroke mortality measure, adjust for stroke severity, and could be implemented in a variety of settings. © 2017 American Heart Association, Inc.

A statistical model for estimating stream temperatures in the Salmon and Clearwater River basins, central Idaho

USGS Publications Warehouse

Donato, Mary M.

2002-01-01

A water-quality standard for temperature is critical for the protection of threatened and endangered salmonids, which need cold, clean water to sustain life. The Idaho Department of Environmental Quality has established temperature standards to protect salmonids, yet little is known about the normal range of temperatures of most Idaho streams. A single temperature standard for all streams does not take into account the natural temperature variation of streams or the existence of naturally warm waters. To address these issues and to help the Idaho Department of Environmental Quality revise the existing State temperature standards for aquatic life, temperature data from more than 200 streams and rivers in the salmon and Clearwater River Basins were collected. From these data, a statistical model was developed for estimating stream temperatures on the basis of subbasin and site characteristics and climatic factors. Stream temperatures were monitored hourly for approximately 58 days during July, August, and September 2000 at relatively undisturbed sites in subbasins in the Salmon and Clearwater River Basins in central Idaho. The monitored subbasins vary widely in size, elevation, drainage area, vegetation cover, and other characteristics. The resulting data were analyzed for statistical correlations with subbasin and site characteristics to establish the most important factors affecting stream temperature. Maximum daily average stream temperatures were strongly correlated with elevation and total upstream drainage area; weaker correlations were noted with stream depth and width and aver-age subbasin slope. Stream temperatures also were correlated with certain types of vegetation cover, but these variables were not significant in the final model. The model takes into account seasonal temperature fluctuations, site elevation, total drainage area, average subbasin slope, and the deviation of daily average air temperature from a 30-year normal daily average air temperature. The goodness-of-fit of the model varies with day of the year. Overall, temperatures can be estimated with 95-percent confidence to within approximately plus or minus 4 degrees Celsius. The model performed well when tested on independent stream-temperature data previously collected by the U.S. Geological Survey and other agencies. Although the model provides insight into the natural temperature potential of a wide variety of streams and rivers in the Salmon and Clearwater River Basins, it has limitations. It is based on data collected in only one summer, during which temperatures were higher and streamflows were lower than normal. The effects of changes in streamflow on the effectiveness of the model are not known. Because the model is based on data from minimally disturbed or undisturbed streams, it should not be applied to streams known to be significantly affected by human activities such as disturbance of the streambed, diversion and return of water by irrigation ditches, and removal of riparian vegetation. Finally, because the model is based on data from streams in the Salmon and Clearwater River Basins and reflects climatological and landscape characteristics of those basins, it should not be applied to streams outside this region.

[Comparison of the effect of different diagnostic criteria of subclinical hypothyroidism and positive TPO-Ab on pregnancy outcomes].

PubMed

He, Yiping; He, Tongqiang; Wang, Yanxia; Xu, Zhao; Xu, Yehong; Wu, Yiqing; Ji, Jing; Mi, Yang

2014-11-01

To explore the effect of different diagnositic criteria of subclinical hypothyroidism using thyroid stimulating hormone (TSH) and positive thyroid peroxidase antibodies (TPO-Ab) on the pregnancy outcomes. 3 244 pregnant women who had their antenatal care and delivered in Child and Maternity Health Hospital of Shannxi Province August from 2011 to February 2013 were recruited prospectively. According to the standard of American Thyroid Association (ATA), pregnant women with normal serum free thyroxine (FT4) whose serum TSH level> 2.50 mU/L were diagnosed as subclinical hypothyroidism in pregnancy (foreign standard group). According to the Guideline of Diagnosis and Therapy of Prenatal and Postpartum Thyroid Disease made by Chinese Society of Endocrinology and Chinese Society of Perinatal Medicine in 2012, pregnant women with serum TSH level> 5.76 mU/L, and normal FT4 were diagnosed as subclinical hypothyroidism in pregnancy(national standard group). Pregnant women with subclinical hypothyroidism whose serum TSH levels were between 2.50-5.76 mU/L were referred as the study observed group; and pregnant women with serum TSH level< 2.50 mU/L and negative TPO- Ab were referred as the control group. Positive TPO-Ab results and the pregnancy outcomes were analyzed. (1) There were 635 cases in the foreign standard group, with the incidence of 19.57% (635/3 244). And there were 70 cases in the national standard group, with the incidence of 2.16% (70/3 244). There were statistically significant difference between the two groups (P < 0.01). There were 565 cases in the study observed group, with the incidence of 17.42% (565/3 244). There was statistically significant difference (P < 0.01) when compared with the national standard group; while there was no statistically significant difference (P > 0.05) when compared with the foreign standard group. (2) Among the 3 244 cases, 402 cases had positive TPO-Ab. 318 positive cases were in the foreign standard group, and the incidence of subclinical hypothyroidism was 79.10% (318/402). There were 317 negative cases in the foreign standard group, with the incidence of 11.15% (317/2 842). The difference was statistically significant (P < 0.01) between them. In the national standard group, 46 cases had positive TPO-Ab, with the incidence of 11.44% (46/402), and 24 cases had negative result, with the incidence of 0.84% (24/2 842). There were statistically significant difference (P < 0.01) between them. In the study observed group, 272 cases were TPO-Ab positive, with the incidence of 67.66% (272/402), and 293 cases were negative, with the incidence of 10.31% (293/2 842), the difference was statistically significant (P < 0.01). (3) The incidence of miscarriage, premature delivery, gestational hypertension disease, gestational diabetes mellitus(GDM)in the foreign standard group had statistically significant differences (P < 0.05) when compared with the control group, respectively. While there was no statistically significant difference (P > 0.05) in the incidence of placental abruption or fetal distress. And the incidence of miscarriage, premature delivery, gestational hypertension disease, GDM in the national standard group had statistical significant difference (P < 0.05) compared with the control group, respectively. While there was no statistically significant difference (P > 0.05) in the incidence of placental abruption or fetal distress. This study observed group of pregnant women's abortion, gestational hypertension disease, GDM incidence respectively compared with control group, the difference had statistical significance (P < 0.05); but in preterm labor, placental abruption, and fetal distress incidence, there were no statistically significant difference (P > 0.05). (4) The incidence of miscarriage, premature delivery, gestational hypertension disease, GDM, placental abruption, fetal distress in the TPO-Ab positive cases of the national standard group showed an increase trend when compared with TPO-Ab negative cases, with no statistically significant difference (P > 0.05). The incidence of gestational hypertension disease and GDM in the TPO-Ab positive cases of the study observed group had statistical significance difference (P < 0.05) when compared with TPO-Ab negative cases; while the incidence of miscarriage, premature birth, placental abruption, fetal distress had no statistically significant difference (P > 0.05). The incidence of gestational hypertension disease and GDM in the TPO-Ab positive cases had statistically significance difference when compared with TPO-Ab negtive cases of foreign standard group (P < 0.05). (1) The incidence of subclinical hypothyroidism is rather high during early pregnancy and can lead to adverse pregnancy outcome. (2) Positive TPO-Ab result has important predictive value of the thyroid dysfunction and GDM. (3) Relatively, the ATA standard of diagnosis (serum TSH level> 2.50 mU/L) is safer for the antenatal care; the national standard (serum TSH level> 5.76 mU/L) is not conducive to pregnancy management.

New methods and results for quantification of lightning-aircraft electrodynamics

NASA Technical Reports Server (NTRS)

Pitts, Felix L.; Lee, Larry D.; Perala, Rodney A.; Rudolph, Terence H.

1987-01-01

The NASA F-106 collected data on the rates of change of electromagnetic parameters on the aircraft surface during over 700 direct lightning strikes while penetrating thunderstorms at altitudes from 15,000 t0 40,000 ft (4,570 to 12,190 m). These in situ measurements provided the basis for the first statistical quantification of the lightning electromagnetic threat to aircraft appropriate for determining indirect lightning effects on aircraft. These data are used to update previous lightning criteria and standards developed over the years from ground-based measurements. The proposed standards will be the first which reflect actual aircraft responses measured at flight altitudes. Nonparametric maximum likelihood estimates of the distribution of the peak electromagnetic rates of change for consideration in the new standards are obtained based on peak recorder data for multiple-strike flights. The linear and nonlinear modeling techniques developed provide means to interpret and understand the direct-strike electromagnetic data acquired on the F-106. The reasonable results obtained with the models, compared with measured responses, provide increased confidence that the models may be credibly applied to other aircraft.

Bootstrap-based methods for estimating standard errors in Cox's regression analyses of clustered event times.

PubMed

Xiao, Yongling; Abrahamowicz, Michal

2010-03-30

We propose two bootstrap-based methods to correct the standard errors (SEs) from Cox's model for within-cluster correlation of right-censored event times. The cluster-bootstrap method resamples, with replacement, only the clusters, whereas the two-step bootstrap method resamples (i) the clusters, and (ii) individuals within each selected cluster, with replacement. In simulations, we evaluate both methods and compare them with the existing robust variance estimator and the shared gamma frailty model, which are available in statistical software packages. We simulate clustered event time data, with latent cluster-level random effects, which are ignored in the conventional Cox's model. For cluster-level covariates, both proposed bootstrap methods yield accurate SEs, and type I error rates, and acceptable coverage rates, regardless of the true random effects distribution, and avoid serious variance under-estimation by conventional Cox-based standard errors. However, the two-step bootstrap method over-estimates the variance for individual-level covariates. We also apply the proposed bootstrap methods to obtain confidence bands around flexible estimates of time-dependent effects in a real-life analysis of cluster event times.

Developing the Precision Magnetic Field for the E989 Muon g{2 Experiment

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

Smith, Matthias W.

The experimental value ofmore » $$(g\\hbox{--}2)_\\mu$$ historically has been and contemporarily remains an important probe into the Standard Model and proposed extensions. Previous measurements of $$(g\\hbox{--}2)_\\mu$$ exhibit a persistent statistical tension with calculations using the Standard Model implying that the theory may be incomplete and constraining possible extensions. The Fermilab Muon g-2 experiment, E989, endeavors to increase the precision over previous experiments by a factor of four and probe more deeply into the tension with the Standard Model. The $$(g\\hbox{--}2)_\\mu$$ experimental implementation measures two spin precession frequencies defined by the magnetic field, proton precession and muon precession. The value of $$(g\\hbox{--}2)_\\mu$$ is derived from a relationship between the two frequencies. The precision of magnetic field measurements and the overall magnetic field uniformity achieved over the muon storage volume are then two undeniably important aspects of the e xperiment in minimizing uncertainty. The current thesis details the methods employed to achieve magnetic field goals and results of the effort.« less

The consistency of standard cosmology and the BATSE number versus brightness relation

NASA Technical Reports Server (NTRS)

Wickramasinghe, W. A. D. T.; Nemiroff, R. J.; Norris, J. P.; Kouveliotou, C.; Fishman, G. J.; Meegan, C. A.; Wilson, R. B.; Paciesas, W. S.

1993-01-01

The integrated number-peak-flux relation measured by the Burst and Transient Source Experiment (BATSE) on board the Compton Gamma Ray Observatory is compared with several standard cosmological distributions for gamma-ray bursts (GRBs). Friedmann-Robertson-Walker models were used along with the assumption that the bursts are standard candles and have no number or luminosity evolution. For a given Omega spectral shape, we used a free parameter, essentially the comoving number density of bursts, to generate a best fit between the cosmology and the measured relation. Our results are shown for a subsample of the first 260 GRBs recorded by BATSE. We find acceptable fits between simple cosmological models and the brightness distribution data, as determined by the Kolmogorov-Smirnov one-distribution statistical test. One cannot distinguish a single best cosmological model from the goodness of the fits. The best fit implies that BATSE GRBs are complete out to a redshift of about unity. However, significantly higher and lower redshifts, by as much as a factor of 2, are possible for other marginally acceptable fits.

Statistics and the Question of Standards

PubMed Central

Stigler, Stephen M.

1996-01-01

This is a written version of a memorial lecture given in honor of Churchill Eisenhart at the National Institute of Standards and Technology on May 5, 1995. The relationship and the interplay between statistics and standards over the past centuries are described. Historical examples are presented to illustrate mutual dependency and development in the two fields. PMID:27805077

EEG and MEG data analysis in SPM8.

PubMed

Litvak, Vladimir; Mattout, Jérémie; Kiebel, Stefan; Phillips, Christophe; Henson, Richard; Kilner, James; Barnes, Gareth; Oostenveld, Robert; Daunizeau, Jean; Flandin, Guillaume; Penny, Will; Friston, Karl

2011-01-01

SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools.

Search for high mass dilepton resonances in pp collisions at $$\\sqrt{s} = 7$$ TeV with the ATLAS experiment

DOE PAGES

Aad, G.

2011-06-01

This article presents a search for high mass e⁺e⁻ or μ⁺μ⁻ resonances in pp collisions at √s = 7 TeV at the LHC. The data were recorded by the ATLAS experiment during 2010 and correspond to a total integrated luminosity of ~ 40 pb⁻¹. No statistically significant excess above the Standard Model expectation is observed in the search region of dilepton invariant mass above 110 GeV. Upper limits at the 95% confidence level are set on the cross section times branching ratio of Z' resonances decaying to dielectrons and dimuons as a function of the resonance mass. Lastly, a lowermore » mass limit of 1.048 TeV on the Sequential Standard Model Z' boson is derived, as well as mass limits on Z' and E₆-motivated Z' models.« less

Search for high mass dilepton resonances in pp collisions at √{ s} = 7 TeV with the ATLAS experiment

NASA Astrophysics Data System (ADS)

Aad, G.; Abbott, B.; Abdallah, J.; Abdelalim, A. A.; Abdesselam, A.; Abdinov, O.; Abi, B.; Abolins, M.; Abramowicz, H.; Abreu, H.; Acerbi, E.; Acharya, B. S.; Adams, D. L.; Addy, T. N.; Adelman, J.; Aderholz, M.; Adomeit, S.; Adragna, P.; Adye, T.; Aefsky, S.; Aguilar-Saavedra, J. A.; Aharrouche, M.; Ahlen, S. P.; Ahles, F.; Ahmad, A.; Ahsan, M.; Aielli, G.; Akdogan, T.; Åkesson, T. P. A.; Akimoto, G.; Akimov, A. V.; Akiyama, A.; Alam, M. S.; Alam, M. A.; Albrand, S.; Aleksa, M.; Aleksandrov, I. N.; Alessandria, F.; Alexa, C.; Alexander, G.; Alexandre, G.; Alexopoulos, T.; Alhroob, M.; Aliev, M.; Alimonti, G.; Alison, J.; Aliyev, M.; Allport, P. P.; Allwood-Spiers, S. E.; Almond, J.; Aloisio, A.; Alon, R.; Alonso, A.; Alviggi, M. G.; Amako, K.; Amaral, P.; Amelung, C.; Ammosov, V. V.; Amorim, A.; Amorós, G.; Amram, N.; Anastopoulos, C.; Andeen, T.; Anders, C. F.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Andrieux, M.-L.; Anduaga, X. S.; Angerami, A.; Anghinolfi, F.; Anjos, N.; Annovi, A.; Antonaki, A.; Antonelli, M.; Antonelli, S.; Antonov, A.; Antos, J.; Anulli, F.; Aoun, S.; Aperio Bella, L.; Apolle, R.; Arabidze, G.; Aracena, I.; Arai, Y.; Arce, A. T. H.; Archambault, J. P.; Arfaoui, S.; Arguin, J.-F.; Arik, E.; Arik, M.; Armbruster, A. J.; Arnaez, O.; Arnault, C.; Artamonov, A.; Artoni, G.; Arutinov, D.; Asai, S.; Asfandiyarov, R.; Ask, S.; Åsman, B.; Asquith, L.; Assamagan, K.; Astbury, A.; Astvatsatourov, A.; Atoian, G.; Aubert, B.; Auerbach, B.; Auge, E.; Augsten, K.; Aurousseau, M.; Austin, N.; Avramidou, R.; Axen, D.; Ay, C.; Azuelos, G.; Azuma, Y.; Baak, M. A.; Baccaglioni, G.; Bacci, C.; Bach, A. M.; Bachacou, H.; Bachas, K.; Bachy, G.; Backes, M.; Backhaus, M.; Badescu, E.; Bagnaia, P.; Bahinipati, S.; Bai, Y.; Bailey, D. C.; Bain, T.; Baines, J. T.; Baker, O. K.; Baker, M. D.; Baker, S.; Baltasar Dos Santos Pedrosa, F.; Banas, E.; Banerjee, P.; Banerjee, Sw.; Banfi, D.; Bangert, A.; Bansal, V.; Bansil, H. S.; Barak, L.; Baranov, S. P.; Barashkou, A.; Barbaro Galtieri, A.; Barber, T.; Barberio, E. L.; Barberis, D.; Barbero, M.; Bardin, D. Y.; Barillari, T.; Barisonzi, M.; Barklow, T.; Barlow, N.; Barnett, B. M.; Barnett, R. M.; Baroncelli, A.; Barr, A. J.; Barreiro, F.; Barreiro Guimarães da Costa, J.; Barrillon, P.; Bartoldus, R.; Barton, A. E.; Bartsch, D.; Bartsch, V.; Bates, R. L.; Batkova, L.; Batley, J. R.; Battaglia, A.; Battistin, M.; Battistoni, G.; Bauer, F.; Bawa, H. S.; Beare, B.; Beau, T.; Beauchemin, P. H.; Beccherle, R.; Bechtle, P.; Beck, H. P.; Beckingham, M.; Becks, K. H.; Beddall, A. J.; Beddall, A.; Bedikian, S.; Bednyakov, V. A.; Bee, C. P.; Begel, M.; Behar Harpaz, S.; Behera, P. K.; Beimforde, M.; Belanger-Champagne, C.; Bell, P. J.; Bell, W. H.; Bella, G.; Bellagamba, L.; Bellina, F.; Bellomo, M.; Belloni, A.; Beloborodova, O.; Belotskiy, K.; Beltramello, O.; Ben Ami, S.; Benary, O.; Benchekroun, D.; Benchouk, C.; Bendel, M.; Benedict, B. H.; Benekos, N.; Benhammou, Y.; Benjamin, D. P.; Benoit, M.; Bensinger, J. R.; Benslama, K.; Bentvelsen, S.; Berge, D.; Bergeaas Kuutmann, E.; Berger, N.; Berghaus, F.; Berglund, E.; Beringer, J.; Bernardet, K.; Bernat, P.; Bernhard, R.; Bernius, C.; Berry, T.; Bertin, A.; Bertinelli, F.; Bertolucci, F.; Besana, M. I.; Besson, N.; Bethke, S.; Bhimji, W.; Bianchi, R. M.; Bianco, M.; Biebel, O.; Bieniek, S. P.; Biesiada, J.; Biglietti, M.; Bilokon, H.; Bindi, M.; Binet, S.; Bingul, A.; Bini, C.; Biscarat, C.; Bitenc, U.; Black, K. M.; Blair, R. E.; Blanchard, J.-B.; Blanchot, G.; Blocker, C.; Blocki, J.; Blondel, A.; Blum, W.; Blumenschein, U.; Bobbink, G. J.; Bobrovnikov, V. B.; Bocchetta, S. S.; Bocci, A.; Boddy, C. R.; Boehler, M.; Boek, J.; Boelaert, N.; Böser, S.; Bogaerts, J. A.; Bogdanchikov, A.; Bogouch, A.; Bohm, C.; Boisvert, V.; Bold, T.; Boldea, V.; Bolnet, N. M.; Bona, M.; Bondarenko, V. G.; Boonekamp, M.; Boorman, G.; Booth, C. N.; Booth, P.; Bordoni, S.; Borer, C.; Borisov, A.; Borissov, G.; Borjanovic, I.; Borroni, S.; Bos, K.; Boscherini, D.; Bosman, M.; Boterenbrood, H.; Botterill, D.; Bouchami, J.; Boudreau, J.; Bouhova-Thacker, E. V.; Boulahouache, C.; Bourdarios, C.; Bousson, N.; Boveia, A.; Boyd, J.; Boyko, I. R.; Bozhko, N. I.; Bozovic-Jelisavcic, I.; Bracinik, J.; Braem, A.; Branchini, P.; Brandenburg, G. W.; Brandt, A.; Brandt, G.; Brandt, O.; Bratzler, U.; Brau, B.; Brau, J. E.; Braun, H. M.; Brelier, B.; Bremer, J.; Brenner, R.; Bressler, S.; Breton, D.; Brett, N. D.; Britton, D.; Brochu, F. M.; Brock, I.; Brock, R.; Brodbeck, T. J.; Brodet, E.; Broggi, F.; Bromberg, C.; Brooijmans, G.; Brooks, W. K.; Brown, G.; Brubaker, E.; Bruckman de Renstrom, P. A.; Bruncko, D.; Bruneliere, R.; Brunet, S.; Bruni, A.; Bruni, G.; Bruschi, M.; Buanes, T.; Bucci, F.; Buchanan, J.; Buchanan, N. J.; Buchholz, P.; Buckingham, R. M.; Buckley, A. G.; Buda, S. I.; Budagov, I. A.; Budick, B.; Büscher, V.; Bugge, L.; Buira-Clark, D.; Buis, E. J.; Bulekov, O.; Bunse, M.; Buran, T.; Burckhart, H.; Burdin, S.; Burgess, T.; Burke, S.; Busato, E.; Bussey, P.; Buszello, C. P.; Butin, F.; Butler, B.; Butler, J. M.; Buttar, C. M.; Butterworth, J. M.; Buttinger, W.; Byatt, T.; Cabrera Urbán, S.; Caforio, D.; Cakir, O.; Calafiura, P.; Calderini, G.; Calfayan, P.; Calkins, R.; Caloba, L. P.; Caloi, R.; Calvet, D.; Calvet, S.; Camacho Toro, R.; Camard, A.; Camarri, P.; Cambiaghi, M.; Cameron, D.; Cammin, J.; Campana, S.; Campanelli, M.; Canale, V.; Canelli, F.; Canepa, A.; Cantero, J.; Capasso, L.; Capeans Garrido, M. D. M.; Caprini, I.; Caprini, M.; Capriotti, D.; Capua, M.; Caputo, R.; Caramarcu, C.; Cardarelli, R.; Carli, T.; Carlino, G.; Carminati, L.; Caron, B.; Caron, S.; Carpentieri, C.; Carrillo Montoya, G. D.; Carter, A. A.; Carter, J. R.; Carvalho, J.; Casadei, D.; Casado, M. P.; Cascella, M.; Caso, C.; Castaneda Hernandez, A. M.; Castaneda-Miranda, E.; Castillo Gimenez, V.; Castro, N. F.; Cataldi, G.; Cataneo, F.; Catinaccio, A.; Catmore, J. R.; Cattai, A.; Cattani, G.; Caughron, S.; Cauz, D.; Cavallari, A.; Cavalleri, P.; Cavalli, D.; Cavalli-Sforza, M.; Cavasinni, V.; Cazzato, A.; Ceradini, F.; Cerqueira, A. S.; Cerri, A.; Cerrito, L.; Cerutti, F.; Cetin, S. A.; Cevenini, F.; Chafaq, A.; Chakraborty, D.; Chan, K.; Chapleau, B.; Chapman, J. D.; Chapman, J. W.; Chareyre, E.; Charlton, D. G.; Chavda, V.; Cheatham, S.; Chekanov, S.; Chekulaev, S. V.; Chelkov, G. A.; Chelstowska, M. A.; Chen, C.; Chen, H.; Chen, L.; Chen, S.; Chen, T.; Chen, X.; Cheng, S.; Cheplakov, A.; Chepurnov, V. F.; Cherkaoui El Moursli, R.; Chernyatin, V.; Cheu, E.; Cheung, S. L.; Chevalier, L.; Chiefari, G.; Chikovani, L.; Childers, J. T.; Chilingarov, A.; Chiodini, G.; Chizhov, M. V.; Choudalakis, G.; Chouridou, S.; Christidi, I. A.; Christov, A.; Chromek-Burckhart, D.; Chu, M. L.; Chudoba, J.; Ciapetti, G.; Ciba, K.; Ciftci, A. K.; Ciftci, R.; Cinca, D.; Cindro, V.; Ciobotaru, M. D.; Ciocca, C.; Ciocio, A.; Cirilli, M.; Ciubancan, M.; Clark, A.; Clark, P. J.; Cleland, W.; Clemens, J. C.; Clement, B.; Clement, C.; Clifft, R. W.; Coadou, Y.; Cobal, M.; Coccaro, A.; Cochran, J.; Coe, P.; Cogan, J. G.; Coggeshall, J.; Cogneras, E.; Cojocaru, C. D.; Colas, J.; Colijn, A. P.; Collard, C.; Collins, N. J.; Collins-Tooth, C.; Collot, J.; Colon, G.; Comune, G.; Conde Muiño, P.; Coniavitis, E.; Conidi, M. C.; Consonni, M.; Constantinescu, S.; Conta, C.; Conventi, F.; Cook, J.; Cooke, M.; Cooper, B. D.; Cooper-Sarkar, A. M.; Cooper-Smith, N. J.; Copic, K.; Cornelissen, T.; Corradi, M.; Corriveau, F.; Cortes-Gonzalez, A.; Cortiana, G.; Costa, G.; Costa, M. J.; Costanzo, D.; Costin, T.; Côté, D.; Coura Torres, R.; Courneyea, L.; Cowan, G.; Cowden, C.; Cox, B. E.; Cranmer, K.; Crescioli, F.; Cristinziani, M.; Crosetti, G.; Crupi, R.; Crépé-Renaudin, S.; Cuenca Almenar, C.; Cuhadar Donszelmann, T.; Cuneo, S.; Curatolo, M.; Curtis, C. J.; Cwetanski, P.; Czirr, H.; Czyczula, Z.; D'Auria, S.; D'Onofrio, M.; D'Orazio, A.; da Rocha Gesualdi Mello, A.; da Silva, P. V. M.; da Via, C.; Dabrowski, W.; Dahlhoff, A.; Dai, T.; Dallapiccola, C.; Dallison, S. J.; Dam, M.; Dameri, M.; Damiani, D. S.; Danielsson, H. O.; Dankers, R.; Dannheim, D.; Dao, V.; Darbo, G.; Darlea, G. L.; Daum, C.; Dauvergne, J. P.; Davey, W.; Davidek, T.; Davidson, N.; Davidson, R.; Davies, M.; Davison, A. R.; Dawe, E.; Dawson, I.; Dawson, J. W.; Daya, R. K.; de, K.; de Asmundis, R.; de Castro, S.; de Castro Faria Salgado, P. E.; de Cecco, S.; de Graat, J.; de Groot, N.; de Jong, P.; de La Taille, C.; de la Torre, H.; de Lotto, B.; de Mora, L.; de Nooij, L.; de Oliveira Branco, M.; de Pedis, D.; de Saintignon, P.; de Salvo, A.; de Sanctis, U.; de Santo, A.; de Vivie de Regie, J. B.; Dean, S.; Dedovich, D. V.; Degenhardt, J.; Dehchar, M.; Deile, M.; Del Papa, C.; Del Peso, J.; Del Prete, T.; Dell'Acqua, A.; Dell'Asta, L.; Della Pietra, M.; Della Volpe, D.; Delmastro, M.; Delpierre, P.; Delruelle, N.; Delsart, P. A.; Deluca, C.; Demers, S.; Demichev, M.; Demirkoz, B.; Deng, J.; Denisov, S. P.; Derendarz, D.; Derkaoui, J. E.; Derue, F.; Dervan, P.; Desch, K.; Devetak, E.; Deviveiros, P. O.; Dewhurst, A.; Dewilde, B.; Dhaliwal, S.; Dhullipudi, R.; di Ciaccio, A.; di Ciaccio, L.; di Girolamo, A.; di Girolamo, B.; di Luise, S.; di Mattia, A.; di Micco, B.; di Nardo, R.; di Simone, A.; di Sipio, R.; Diaz, M. A.; Diblen, F.; Diehl, E. B.; Dietl, H.; Dietrich, J.; Dietzsch, T. A.; Diglio, S.; Dindar Yagci, K.; Dingfelder, J.; Dionisi, C.; Dita, P.; Dita, S.; Dittus, F.; Djama, F.; Djilkibaev, R.; Djobava, T.; Do Vale, M. A. B.; Do Valle Wemans, A.; Doan, T. K. O.; Dobbs, M.; Dobinson, R.; Dobos, D.; Dobson, E.; Dobson, M.; Dodd, J.; Dogan, O. B.; Doglioni, C.; Doherty, T.; Doi, Y.; Dolejsi, J.; Dolenc, I.; Dolezal, Z.; Dolgoshein, B. A.; Dohmae, T.; Donadelli, M.; Donega, M.; Donini, J.; Dopke, J.; Doria, A.; Dos Anjos, A.; Dosil, M.; Dotti, A.; Dova, M. T.; Dowell, J. D.; Doxiadis, A. D.; Doyle, A. T.; Drasal, Z.; Drees, J.; Dressnandt, N.; Drevermann, H.; Driouichi, C.; Dris, M.; Drohan, J. G.; Dubbert, J.; Dubbs, T.; Dube, S.; Duchovni, E.; Duckeck, G.; Dudarev, A.; Dudziak, F.; Dührssen, M.; Duerdoth, I. P.; Duflot, L.; Dufour, M.-A.; Dunford, M.; Duran Yildiz, H.; Duxfield, R.; Dwuznik, M.; Dydak, F.; Dzahini, D.; Düren, M.; Ebenstein, W. L.; Ebke, J.; Eckert, S.; Eckweiler, S.; Edmonds, K.; Edwards, C. A.; Ehrenfeld, W.; Ehrich, T.; Eifert, T.; Eigen, G.; Einsweiler, K.; Eisenhandler, E.; Ekelof, T.; El Kacimi, M.; Ellert, M.; Elles, S.; Ellinghaus, F.; Ellis, K.; Ellis, N.; Elmsheuser, J.; Elsing, M.; Ely, R.; Emeliyanov, D.; Engelmann, R.; Engl, A.; Epp, B.; Eppig, A.; Erdmann, J.; Ereditato, A.; Eriksson, D.; Ernst, J.; Ernst, M.; Ernwein, J.; Errede, D.; Errede, S.; Ertel, E.; Escalier, M.; Escobar, C.; Espinal Curull, X.; Esposito, B.; Etienne, F.; Etienvre, A. I.; Etzion, E.; Evangelakou, D.; Evans, H.; Fabbri, L.; Fabre, C.; Facius, K.; Fakhrutdinov, R. M.; Falciano, S.; Falou, A. C.; Fang, Y.; Fanti, M.; Farbin, A.; Farilla, A.; Farley, J.; Farooque, T.; Farrington, S. M.; Farthouat, P.; Fasching, D.; Fassnacht, P.; Fassouliotis, D.; Fatholahzadeh, B.; Favareto, A.; Fayard, L.; Fazio, S.; Febbraro, R.; Federic, P.; Fedin, O. L.; Fedorko, I.; Fedorko, W.; Fehling-Kaschek, M.; Feligioni, L.; Fellmann, D.; Felzmann, C. U.; Feng, C.; Feng, E. J.; Fenyuk, A. B.; Ferencei, J.; Ferland, J.; Fernandes, B.; Fernando, W.; Ferrag, S.; Ferrando, J.; Ferrara, V.; Ferrari, A.; Ferrari, P.; Ferrari, R.; Ferrer, A.; Ferrer, M. L.; Ferrere, D.; Ferretti, C.; Ferretto Parodi, A.; Fiascaris, M.; Fiedler, F.; Filipčič, A.; Filippas, A.; Filthaut, F.; Fincke-Keeler, M.; Fiolhais, M. C. N.; Fiorini, L.; Firan, A.; Fischer, G.; Fischer, P.; Fisher, M. J.; Fisher, S. M.; Flammer, J.; Flechl, M.; Fleck, I.; Fleckner, J.; Fleischmann, P.; Fleischmann, S.; Flick, T.; Flores Castillo, L. R.; Flowerdew, M. J.; Föhlisch, F.; Fokitis, M.; Fonseca Martin, T.; Forbush, D. A.; Formica, A.; Forti, A.; Fortin, D.; Foster, J. M.; Fournier, D.; Foussat, A.; Fowler, A. J.; Fowler, K.; Fox, H.; Francavilla, P.; Franchino, S.; Francis, D.; Frank, T.; Franklin, M.; Franz, S.; Fraternali, M.; Fratina, S.; French, S. T.; Froeschl, R.; Froidevaux, D.; Frost, J. 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F.; Mora Herrera, C.; Moraes, A.; Morais, A.; Morange, N.; Morello, G.; Moreno, D.; Moreno Llácer, M.; Morettini, P.; Morii, M.; Morin, J.; Morita, Y.; Morley, A. K.; Mornacchi, G.; Morone, M.-C.; Morozov, S. V.; Morris, J. D.; Moser, H. G.; Mosidze, M.; Moss, J.; Mount, R.; Mountricha, E.; Mouraviev, S. V.; Moyse, E. J. W.; Mudrinic, M.; Mueller, F.; Mueller, J.; Mueller, K.; Müller, T. A.; Muenstermann, D.; Muijs, A.; Muir, A.; Munwes, Y.; Murakami, K.; Murray, W. J.; Mussche, I.; Musto, E.; Myagkov, A. G.; Myska, M.; Nadal, J.; Nagai, K.; Nagano, K.; Nagasaka, Y.; Nairz, A. M.; Nakahama, Y.; Nakamura, K.; Nakano, I.; Nanava, G.; Napier, A.; Nash, M.; Nation, N. R.; Nattermann, T.; Naumann, T.; Navarro, G.; Neal, H. A.; Nebot, E.; Nechaeva, P. Yu.; Negri, A.; Negri, G.; Nektarijevic, S.; Nelson, A.; Nelson, S.; Nelson, T. K.; Nemecek, S.; Nemethy, P.; Nepomuceno, A. A.; Nessi, M.; Nesterov, S. Y.; Neubauer, M. S.; Neusiedl, A.; Neves, R. M.; Nevski, P.; Newman, P. R.; Nickerson, R. 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T.; Perez Reale, V.; Peric, I.; Perini, L.; Pernegger, H.; Perrino, R.; Perrodo, P.; Persembe, S.; Peshekhonov, V. D.; Peters, O.; Petersen, B. A.; Petersen, J.; Petersen, T. C.; Petit, E.; Petridis, A.; Petridou, C.; Petrolo, E.; Petrucci, F.; Petschull, D.; Petteni, M.; Pezoa, R.; Phan, A.; Phillips, A. W.; Phillips, P. W.; Piacquadio, G.; Piccaro, E.; Piccinini, M.; Pickford, A.; Piec, S. M.; Piegaia, R.; Pilcher, J. E.; Pilkington, A. D.; Pina, J.; Pinamonti, M.; Pinder, A.; Pinfold, J. L.; Ping, J.; Pinto, B.; Pirotte, O.; Pizio, C.; Placakyte, R.; Plamondon, M.; Plano, W. G.; Pleier, M.-A.; Pleskach, A. V.; Poblaguev, A.; Poddar, S.; Podlyski, F.; Poggioli, L.; Poghosyan, T.; Pohl, M.; Polci, F.; Polesello, G.; Policicchio, A.; Polini, A.; Poll, J.; Polychronakos, V.; Pomarede, D. M.; Pomeroy, D.; Pommès, K.; Pontecorvo, L.; Pope, B. G.; Popeneciu, G. A.; Popovic, D. S.; Poppleton, A.; Portell Bueso, X.; Porter, R.; Posch, C.; Pospelov, G. E.; Pospisil, S.; Potrap, I. 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I.; Rudolph, G.; Rühr, F.; Ruggieri, F.; Ruiz-Martinez, A.; Rulikowska-Zarebska, E.; Rumiantsev, V.; Rumyantsev, L.; Runge, K.; Runolfsson, O.; Rurikova, Z.; Rusakovich, N. A.; Rust, D. R.; Rutherfoord, J. P.; Ruwiedel, C.; Ruzicka, P.; Ryabov, Y. F.; Ryadovikov, V.; Ryan, P.; Rybar, M.; Rybkin, G.; Ryder, N. C.; Rzaeva, S.; Saavedra, A. F.; Sadeh, I.; Sadrozinski, H. F.-W.; Sadykov, R.; Safai Tehrani, F.; Sakamoto, H.; Salamanna, G.; Salamon, A.; Saleem, M.; Salihagic, D.; Salnikov, A.; Salt, J.; Salvachua Ferrando, B. M.; Salvatore, D.; Salvatore, F.; Salzburger, A.; Sampsonidis, D.; Samset, B. H.; Sandaker, H.; Sander, H. G.; Sanders, M. P.; Sandhoff, M.; Sandhu, P.; Sandoval, T.; Sandstroem, R.; Sandvoss, S.; Sankey, D. P. C.; Sansoni, A.; Santamarina Rios, C.; Santoni, C.; Santonico, R.; Santos, H.; Saraiva, J. G.; Sarangi, T.; Sarkisyan-Grinbaum, E.; Sarri, F.; Sartisohn, G.; Sasaki, O.; Sasaki, T.; Sasao, N.; Satsounkevitch, I.; Sauvage, G.; Sauvan, J. 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M.; Smizanska, M.; Smolek, K.; Snesarev, A. A.; Snow, S. W.; Snow, J.; Snuverink, J.; Snyder, S.; Soares, M.; Sobie, R.; Sodomka, J.; Soffer, A.; Solans, C. A.; Solar, M.; Solc, J.; Soldatov, E.; Soldevila, U.; Solfaroli Camillocci, E.; Solodkov, A. A.; Solovyanov, O. V.; Sondericker, J.; Soni, N.; Sopko, V.; Sopko, B.; Sorbi, M.; Sosebee, M.; Soukharev, A.; Spagnolo, S.; Spanò, F.; Spighi, R.; Spigo, G.; Spila, F.; Spiriti, E.; Spiwoks, R.; Spousta, M.; Spreitzer, T.; Spurlock, B.; St. Denis, R. D.; Stahl, T.; Stahlman, J.; Stamen, R.; Stanecka, E.; Stanek, R. W.; Stanescu, C.; Stapnes, S.; Starchenko, E. A.; Stark, J.; Staroba, P.; Starovoitov, P.; Staude, A.; Stavina, P.; Stavropoulos, G.; Steele, G.; Steinbach, P.; Steinberg, P.; Stekl, I.; Stelzer, B.; Stelzer, H. J.; Stelzer-Chilton, O.; Stenzel, H.; Stevenson, K.; Stewart, G. A.; Stillings, J. A.; Stockmanns, T.; Stockton, M. C.; Stoerig, K.; Stoicea, G.; Stonjek, S.; Strachota, P.; Stradling, A. R.; Straessner, A.; Strandberg, J.; Strandberg, S.; Strandlie, A.; Strang, M.; Strauss, E.; Strauss, M.; Strizenec, P.; Ströhmer, R.; Strom, D. M.; Strong, J. A.; Stroynowski, R.; Strube, J.; Stugu, B.; Stumer, I.; Stupak, J.; Sturm, P.; Soh, D. A.; Su, D.; Subramania, H. S.; Succurro, A.; Sugaya, Y.; Sugimoto, T.; Suhr, C.; Suita, K.; Suk, M.; Sulin, V. V.; Sultansoy, S.; Sumida, T.; Sun, X.; Sundermann, J. E.; Suruliz, K.; Sushkov, S.; Susinno, G.; Sutton, M. R.; Suzuki, Y.; Sviridov, Yu. M.; Swedish, S.; Sykora, I.; Sykora, T.; Szeless, B.; Sánchez, J.; Ta, D.; Tackmann, K.; Taffard, A.; Tafirout, R.; Taga, A.; Taiblum, N.; Takahashi, Y.; Takai, H.; Takashima, R.; Takeda, H.; Takeshita, T.; Talby, M.; Talyshev, A.; Tamsett, M. C.; Tanaka, J.; Tanaka, R.; Tanaka, S.; Tanaka, S.; Tanaka, Y.; Tani, K.; Tannoury, N.; Tappern, G. P.; Tapprogge, S.; Tardif, D.; Tarem, S.; Tarrade, F.; Tartarelli, G. F.; Tas, P.; Tasevsky, M.; Tassi, E.; Tatarkhanov, M.; Taylor, C.; Taylor, F. E.; Taylor, G. N.; Taylor, W.; Teixeira Dias Castanheira, M.; Teixeira-Dias, P.; Temming, K. K.; Ten Kate, H.; Teng, P. K.; Terada, S.; Terashi, K.; Terron, J.; Terwort, M.; Testa, M.; Teuscher, R. J.; Tevlin, C. M.; Thadome, J.; Therhaag, J.; Theveneaux-Pelzer, T.; Thioye, M.; Thoma, S.; Thomas, J. P.; Thompson, E. N.; Thompson, P. D.; Thompson, P. D.; Thompson, A. S.; Thomson, E.; Thomson, M.; Thun, R. P.; Tic, T.; Tikhomirov, V. O.; Tikhonov, Y. A.; Timmermans, C. J. W. P.; Tipton, P.; Tique Aires Viegas, F. J.; Tisserant, S.; Tobias, J.; Toczek, B.; Todorov, T.; Todorova-Nova, S.; Toggerson, B.; Tojo, J.; Tokár, S.; Tokunaga, K.; Tokushuku, K.; Tollefson, K.; Tomoto, M.; Tompkins, L.; Toms, K.; Tong, G.; Tonoyan, A.; Topfel, C.; Topilin, N. D.; Torchiani, I.; Torrence, E.; Torró Pastor, E.; Toth, J.; Touchard, F.; Tovey, D. R.; Traynor, D.; Trefzger, T.; Treis, J.; Tremblet, L.; Tricoli, A.; Trigger, I. M.; Trincaz-Duvoid, S.; Trinh, T. N.; Tripiana, M. F.; Triplett, N.; Trischuk, W.; Trivedi, A.; Trocmé, B.; Troncon, C.; Trottier-McDonald, M.; Trzupek, A.; Tsarouchas, C.; Tseng, J. C.-L.; Tsiakiris, M.; Tsiareshka, P. V.; Tsionou, D.; Tsipolitis, G.; Tsiskaridze, V.; Tskhadadze, E. G.; Tsukerman, I. I.; Tsulaia, V.; Tsung, J.-W.; Tsuno, S.; Tsybychev, D.; Tua, A.; Tuggle, J. M.; Turala, M.; Turecek, D.; Turk Cakir, I.; Turlay, E.; Turra, R.; Tuts, P. M.; Tykhonov, A.; Tylmad, M.; Tyndel, M.; Tyrvainen, H.; Tzanakos, G.; Uchida, K.; Ueda, I.; Ueno, R.; Ugland, M.; Uhlenbrock, M.; Uhrmacher, M.; Ukegawa, F.; Unal, G.; Underwood, D. G.; Undrus, A.; Unel, G.; Unno, Y.; Urbaniec, D.; Urkovsky, E.; Urrejola, P.; Usai, G.; Uslenghi, M.; Vacavant, L.; Vacek, V.; Vachon, B.; Vahsen, S.; Valderanis, C.; Valenta, J.; Valente, P.; Valentinetti, S.; Valkar, S.; Valladolid Gallego, E.; Vallecorsa, S.; Valls Ferrer, J. A.; van der Graaf, H.; van der Kraaij, E.; van der Leeuw, R.; van der Poel, E.; van der Ster, D.; van Eijk, B.; van Eldik, N.; van Gemmeren, P.; van Kesteren, Z.; van Vulpen, I.; Vandelli, W.; Vandoni, G.; Vaniachine, A.; Vankov, P.; Vannucci, F.; Varela Rodriguez, F.; Vari, R.; Varnes, E. W.; Varouchas, D.; Vartapetian, A.; Varvell, K. E.; Vassilakopoulos, V. I.; Vazeille, F.; Vegni, G.; Veillet, J. J.; Vellidis, C.; Veloso, F.; Veness, R.; Veneziano, S.; Ventura, A.; Ventura, D.; Venturi, M.; Venturi, N.; Vercesi, V.; Verducci, M.; Verkerke, W.; Vermeulen, J. C.; Vest, A.; Vetterli, M. C.; Vichou, I.; Vickey, T.; Viehhauser, G. H. A.; Viel, S.; Villa, M.; Villaplana Perez, M.; Vilucchi, E.; Vincter, M. G.; Vinek, E.; Vinogradov, V. B.; Virchaux, M.; Viret, S.; Virzi, J.; Vitale, A.; Vitells, O.; Viti, M.; Vivarelli, I.; Vives Vaque, F.; Vlachos, S.; Vlasak, M.; Vlasov, N.; Vogel, A.; Vokac, P.; Volpi, G.; Volpi, M.; Volpini, G.; von der Schmitt, H.; von Loeben, J.; von Radziewski, H.; von Toerne, E.; Vorobel, V.; Vorobiev, A. P.; Vorwerk, V.; Vos, M.; Voss, R.; Voss, T. T.; Vossebeld, J. H.; Vovenko, A. S.; Vranjes, N.; Vranjes Milosavljevic, M.; Vrba, V.; Vreeswijk, M.; Vu Anh, T.; Vuillermet, R.; Vukotic, I.; Wagner, W.; Wagner, P.; Wahlen, H.; Wakabayashi, J.; Walbersloh, J.; Walch, S.; Walder, J.; Walker, R.; Walkowiak, W.; Wall, R.; Waller, P.; Wang, C.; Wang, H.; Wang, H.; Wang, J.; Wang, J.; Wang, J. C.; Wang, R.; Wang, S. M.; Warburton, A.; Ward, C. P.; Warsinsky, M.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, A. T.; Waugh, B. M.; Weber, J.; Weber, M.; Weber, M. S.; Weber, P.; Weidberg, A. R.; Weigell, P.; Weingarten, J.; Weiser, C.; Wellenstein, H.; Wells, P. S.; Wen, M.; Wenaus, T.; Wendler, S.; Weng, Z.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M.; Werner, P.; Werth, M.; Wessels, M.; Whalen, K.; Wheeler-Ellis, S. J.; Whitaker, S. P.; White, A.; White, M. J.; White, S.; Whitehead, S. R.; Whiteson, D.; Whittington, D.; Wicek, F.; Wicke, D.; Wickens, F. J.; Wiedenmann, W.; Wielers, M.; Wienemann, P.; Wiglesworth, C.; Wiik, L. A. M.; Wijeratne, P. A.; Wildauer, A.; Wildt, M. A.; Wilhelm, I.; Wilkens, H. G.; Will, J. Z.; Williams, E.; Williams, H. H.; Willis, W.; Willocq, S.; Wilson, J. A.; Wilson, M. G.; Wilson, A.; Wingerter-Seez, I.; Winkelmann, S.; Winklmeier, F.; Wittgen, M.; Wolter, M. W.; Wolters, H.; Wooden, G.; Wosiek, B. K.; Wotschack, J.; Woudstra, M. J.; Wraight, K.; Wright, C.; Wrona, B.; Wu, S. L.; Wu, X.; Wu, Y.; Wulf, E.; Wunstorf, R.; Wynne, B. M.; Xaplanteris, L.; Xella, S.; Xie, S.; Xie, Y.; Xu, C.; Xu, D.; Xu, G.; Yabsley, B.; Yamada, M.; Yamamoto, A.; Yamamoto, K.; Yamamoto, S.; Yamamura, T.; Yamaoka, J.; Yamazaki, T.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, U. K.; Yang, Y.; Yang, Y.; Yang, Z.; Yanush, S.; Yao, W.-M.; Yao, Y.; Yasu, Y.; Ybeles Smit, G. V.; Ye, J.; Ye, S.; Yilmaz, M.; Yoosoofmiya, R.; Yorita, K.; Yoshida, R.; Young, C.; Youssef, S.; Yu, D.; Yu, J.; Yu, J.; Yuan, L.; Yurkewicz, A.; Zaets, V. G.; Zaidan, R.; Zaitsev, A. M.; Zajacova, Z.; Zalite, Yo. K.; Zanello, L.; Zarzhitsky, P.; Zaytsev, A.; Zeitnitz, C.; Zeller, M.; Zema, P. F.; Zemla, A.; Zendler, C.; Zenin, A. V.; Zenin, O.; Ženiš, T.; Zenonos, Z.; Zenz, S.; Zerwas, D.; Zevi Della Porta, G.; Zhan, Z.; Zhang, D.; Zhang, H.; Zhang, J.; Zhang, X.; Zhang, Z.; Zhao, L.; Zhao, T.; Zhao, Z.; Zhemchugov, A.; Zheng, S.; Zhong, J.; Zhou, B.; Zhou, N.; Zhou, Y.; Zhu, C. G.; Zhu, H.; Zhu, Y.; Zhuang, X.; Zhuravlov, V.; Zieminska, D.; Zimmermann, R.; Zimmermann, S.; Zimmermann, S.; Ziolkowski, M.; Zitoun, R.; Živković, L.; Zmouchko, V. V.; Zobernig, G.; Zoccoli, A.; Zolnierowski, Y.; Zsenei, A.; Zur Nedden, M.; Zutshi, V.; Zwalinski, L.; Atlas Collaboration

2011-06-01

This Letter presents a search for high mass e+e- or μ+μ- resonances in pp collisions at √{ s} = 7 TeV at the LHC. The data were recorded by the ATLAS experiment during 2010 and correspond to a total integrated luminosity of ∼ 40pb-1. No statistically significant excess above the Standard Model expectation is observed in the search region of dilepton invariant mass above 110 GeV. Upper limits at the 95% confidence level are set on the cross section times branching ratio of Z‧ resonances decaying to dielectrons and dimuons as a function of the resonance mass. A lower mass limit of 1.048 TeV on the Sequential Standard Model Z‧ boson is derived, as well as mass limits on Z* and E6-motivated Z‧ models.

EEG and MEG Data Analysis in SPM8

PubMed Central

Litvak, Vladimir; Mattout, Jérémie; Kiebel, Stefan; Phillips, Christophe; Henson, Richard; Kilner, James; Barnes, Gareth; Oostenveld, Robert; Daunizeau, Jean; Flandin, Guillaume; Penny, Will; Friston, Karl

2011-01-01

SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools. PMID:21437221

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

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

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.

2013-07-25

This paper presents four algorithms to generate random forecast error time series. The performance of four algorithms is compared. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets used in power grid operation to study the net load balancing need in variable generation integration studies. The four algorithms are truncated-normal distribution models, state-space based Markov models, seasonal autoregressive moving average (ARMA) models, and a stochastic-optimization based approach. The comparison is made using historical DA load forecast and actual load valuesmore » to generate new sets of DA forecasts with similar stoical forecast error characteristics (i.e., mean, standard deviation, autocorrelation, and cross-correlation). The results show that all methods generate satisfactory results. One method may preserve one or two required statistical characteristics better the other methods, but may not preserve other statistical characteristics as well compared with the other methods. Because the wind and load forecast error generators are used in wind integration studies to produce wind and load forecasts time series for stochastic planning processes, it is sometimes critical to use multiple methods to generate the error time series to obtain a statistically robust result. Therefore, this paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

Predicting the potential distribution of invasive exotic species using GIS and information-theoretic approaches: A case of ragweed (Ambrosia artemisiifolia L.) distribution in China

USGS Publications Warehouse

Hao, Chen; LiJun, Chen; Albright, Thomas P.

2007-01-01

Invasive exotic species pose a growing threat to the economy, public health, and ecological integrity of nations worldwide. Explaining and predicting the spatial distribution of invasive exotic species is of great importance to prevention and early warning efforts. We are investigating the potential distribution of invasive exotic species, the environmental factors that influence these distributions, and the ability to predict them using statistical and information-theoretic approaches. For some species, detailed presence/absence occurrence data are available, allowing the use of a variety of standard statistical techniques. However, for most species, absence data are not available. Presented with the challenge of developing a model based on presence-only information, we developed an improved logistic regression approach using Information Theory and Frequency Statistics to produce a relative suitability map. This paper generated a variety of distributions of ragweed (Ambrosia artemisiifolia L.) from logistic regression models applied to herbarium specimen location data and a suite of GIS layers including climatic, topographic, and land cover information. Our logistic regression model was based on Akaike's Information Criterion (AIC) from a suite of ecologically reasonable predictor variables. Based on the results we provided a new Frequency Statistical method to compartmentalize habitat-suitability in the native range. Finally, we used the model and the compartmentalized criterion developed in native ranges to "project" a potential distribution onto the exotic ranges to build habitat-suitability maps. ?? Science in China Press 2007.

Summary Statistics for Fun Dough Data Acquired at LLNL

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

Kallman, J S; Morales, K E; Whipple, R E

Using x-ray computerized tomography (CT), we have characterized the x-ray linear attenuation coefficients (LAC) of a Play Dough{trademark}-like product, Fun Dough{trademark}, designated as PD. Table 1 gives the first-order statistics for each of four CT measurements, estimated with a Gaussian kernel density estimator (KDE) analysis. The mean values of the LAC range from a high of about 2100 LMHU{sub D} at 100kVp to a low of about 1100 LMHU{sub D} at 300kVp. The standard deviation of each measurement is around 1% of the mean. The entropy covers the range from 3.9 to 4.6. Ordinarily, we would model the LAC ofmore » the material and compare the modeled values to the measured values. In this case, however, we did not have the composition of the material and therefore did not model the LAC. Using a method recently proposed by Lawrence Livermore National Laboratory (LLNL), we estimate the value of the effective atomic number, Z{sub eff}, to be near 8.5. LLNL prepared about 50mL of the Fun Dough{trademark} in a polypropylene vial and firmly compressed it immediately prior to the x-ray measurements. Still, layers can plainly be seen in the reconstructed images, indicating that the bulk density of the material in the container is affected by voids and bubbles. We used the computer program IMGREC to reconstruct the CT images. The values of the key parameters used in the data capture and image reconstruction are given in this report. Additional details may be found in the experimental SOP and a separate document. To characterize the statistical distribution of LAC values in each CT image, we first isolated an 80% central-core segment of volume elements ('voxels') lying completely within the specimen, away from the walls of the polypropylene vial. All of the voxels within this central core, including those comprised of voids and inclusions, are included in the statistics. We then calculated the mean value, standard deviation and entropy for (a) the four image segments and for (b) their digital gradient images. (A digital gradient image of a given image was obtained by taking the absolute value of the difference between the initial image and that same image offset by one voxel horizontally, parallel to the rows of the x-ray detector array.) The statistics of the initial image of LAC values are called 'first order statistics;' those of the gradient image, 'second order statistics.'« less

S2O - A software tool for integrating research data from general purpose statistic software into electronic data capture systems.

PubMed

Bruland, Philipp; Dugas, Martin

2017-01-07

Data capture for clinical registries or pilot studies is often performed in spreadsheet-based applications like Microsoft Excel or IBM SPSS. Usually, data is transferred into statistic software, such as SAS, R or IBM SPSS Statistics, for analyses afterwards. Spreadsheet-based solutions suffer from several drawbacks: It is generally not possible to ensure a sufficient right and role management; it is not traced who has changed data when and why. Therefore, such systems are not able to comply with regulatory requirements for electronic data capture in clinical trials. In contrast, Electronic Data Capture (EDC) software enables a reliable, secure and auditable collection of data. In this regard, most EDC vendors support the CDISC ODM standard to define, communicate and archive clinical trial meta- and patient data. Advantages of EDC systems are support for multi-user and multicenter clinical trials as well as auditable data. Migration from spreadsheet based data collection to EDC systems is labor-intensive and time-consuming at present. Hence, the objectives of this research work are to develop a mapping model and implement a converter between the IBM SPSS and CDISC ODM standard and to evaluate this approach regarding syntactic and semantic correctness. A mapping model between IBM SPSS and CDISC ODM data structures was developed. SPSS variables and patient values can be mapped and converted into ODM. Statistical and display attributes from SPSS are not corresponding to any ODM elements; study related ODM elements are not available in SPSS. The S2O converting tool was implemented as command-line-tool using the SPSS internal Java plugin. Syntactic and semantic correctness was validated with different ODM tools and reverse transformation from ODM into SPSS format. Clinical data values were also successfully transformed into the ODM structure. Transformation between the spreadsheet format IBM SPSS and the ODM standard for definition and exchange of trial data is feasible. S2O facilitates migration from Excel- or SPSS-based data collections towards reliable EDC systems. Thereby, advantages of EDC systems like reliable software architecture for secure and traceable data collection and particularly compliance with regulatory requirements are achievable.

A practical method to test the validity of the standard Gumbel distribution in logit-based multinomial choice models of travel behavior

DOE PAGES

Ye, Xin; Garikapati, Venu M.; You, Daehyun; ...

2017-11-08

Most multinomial choice models (e.g., the multinomial logit model) adopted in practice assume an extreme-value Gumbel distribution for the random components (error terms) of utility functions. This distributional assumption offers a closed-form likelihood expression when the utility maximization principle is applied to model choice behaviors. As a result, model coefficients can be easily estimated using the standard maximum likelihood estimation method. However, maximum likelihood estimators are consistent and efficient only if distributional assumptions on the random error terms are valid. It is therefore critical to test the validity of underlying distributional assumptions on the error terms that form the basismore » of parameter estimation and policy evaluation. In this paper, a practical yet statistically rigorous method is proposed to test the validity of the distributional assumption on the random components of utility functions in both the multinomial logit (MNL) model and multiple discrete-continuous extreme value (MDCEV) model. Based on a semi-nonparametric approach, a closed-form likelihood function that nests the MNL or MDCEV model being tested is derived. The proposed method allows traditional likelihood ratio tests to be used to test violations of the standard Gumbel distribution assumption. Simulation experiments are conducted to demonstrate that the proposed test yields acceptable Type-I and Type-II error probabilities at commonly available sample sizes. The test is then applied to three real-world discrete and discrete-continuous choice models. For all three models, the proposed test rejects the validity of the standard Gumbel distribution in most utility functions, calling for the development of robust choice models that overcome adverse effects of violations of distributional assumptions on the error terms in random utility functions.« less

A practical method to test the validity of the standard Gumbel distribution in logit-based multinomial choice models of travel behavior

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

Ye, Xin; Garikapati, Venu M.; You, Daehyun

Most multinomial choice models (e.g., the multinomial logit model) adopted in practice assume an extreme-value Gumbel distribution for the random components (error terms) of utility functions. This distributional assumption offers a closed-form likelihood expression when the utility maximization principle is applied to model choice behaviors. As a result, model coefficients can be easily estimated using the standard maximum likelihood estimation method. However, maximum likelihood estimators are consistent and efficient only if distributional assumptions on the random error terms are valid. It is therefore critical to test the validity of underlying distributional assumptions on the error terms that form the basismore » of parameter estimation and policy evaluation. In this paper, a practical yet statistically rigorous method is proposed to test the validity of the distributional assumption on the random components of utility functions in both the multinomial logit (MNL) model and multiple discrete-continuous extreme value (MDCEV) model. Based on a semi-nonparametric approach, a closed-form likelihood function that nests the MNL or MDCEV model being tested is derived. The proposed method allows traditional likelihood ratio tests to be used to test violations of the standard Gumbel distribution assumption. Simulation experiments are conducted to demonstrate that the proposed test yields acceptable Type-I and Type-II error probabilities at commonly available sample sizes. The test is then applied to three real-world discrete and discrete-continuous choice models. For all three models, the proposed test rejects the validity of the standard Gumbel distribution in most utility functions, calling for the development of robust choice models that overcome adverse effects of violations of distributional assumptions on the error terms in random utility functions.« less

Quality Space and Launch Requirements Addendum to AS9100C

DTIC Science & Technology

2015-03-05

45 8.9.1 Statistical Process Control (SPC) .......................................................................... 45 8.9.1.1 Out of Control...Systems Center SME Subject Matter Expert SOW Statement of Work SPC Statistical Process Control SPO System Program Office SRP Standard Repair...individual data exceeding the control limits. Control limits are developed using standard statistical methods or other approved techniques and are based on

Statistical variation in progressive scrambling

NASA Astrophysics Data System (ADS)

Clark, Robert D.; Fox, Peter C.

2004-07-01

The two methods most often used to evaluate the robustness and predictivity of partial least squares (PLS) models are cross-validation and response randomization. Both methods may be overly optimistic for data sets that contain redundant observations, however. The kinds of perturbation analysis widely used for evaluating model stability in the context of ordinary least squares regression are only applicable when the descriptors are independent of each other and errors are independent and normally distributed; neither assumption holds for QSAR in general and for PLS in particular. Progressive scrambling is a novel, non-parametric approach to perturbing models in the response space in a way that does not disturb the underlying covariance structure of the data. Here, we introduce adjustments for two of the characteristic values produced by a progressive scrambling analysis - the deprecated predictivity (Q_s^{ast^2}) and standard error of prediction (SDEP s * ) - that correct for the effect of introduced perturbation. We also explore the statistical behavior of the adjusted values (Q_0^{ast^2} and SDEP 0 * ) and the sensitivity to perturbation (d q 2/d r yy ' 2). It is shown that the three statistics are all robust for stable PLS models, in terms of the stochastic component of their determination and of their variation due to sampling effects involved in training set selection.

Analysis of statistical and standard algorithms for detecting muscle onset with surface electromyography

PubMed Central

Tweedell, Andrew J.; Haynes, Courtney A.

2017-01-01

The timing of muscle activity is a commonly applied analytic method to understand how the nervous system controls movement. This study systematically evaluates six classes of standard and statistical algorithms to determine muscle onset in both experimental surface electromyography (EMG) and simulated EMG with a known onset time. Eighteen participants had EMG collected from the biceps brachii and vastus lateralis while performing a biceps curl or knee extension, respectively. Three established methods and three statistical methods for EMG onset were evaluated. Linear envelope, Teager-Kaiser energy operator + linear envelope and sample entropy were the established methods evaluated while general time series mean/variance, sequential and batch processing of parametric and nonparametric tools, and Bayesian changepoint analysis were the statistical techniques used. Visual EMG onset (experimental data) and objective EMG onset (simulated data) were compared with algorithmic EMG onset via root mean square error and linear regression models for stepwise elimination of inferior algorithms. The top algorithms for both data types were analyzed for their mean agreement with the gold standard onset and evaluation of 95% confidence intervals. The top algorithms were all Bayesian changepoint analysis iterations where the parameter of the prior (p0) was zero. The best performing Bayesian algorithms were p0 = 0 and a posterior probability for onset determination at 60–90%. While existing algorithms performed reasonably, the Bayesian changepoint analysis methodology provides greater reliability and accuracy when determining the singular onset of EMG activity in a time series. Further research is needed to determine if this class of algorithms perform equally well when the time series has multiple bursts of muscle activity. PMID:28489897

Observation of $$\\mathrm{t\\overline{t}}$$H production

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

Sirunyan, Albert M; et al.

The observation of Higgs boson production in association with a top quark-antiquark pair is reported, based on a combined analysis of proton-proton collision data at center-of-mass energies ofmore » $$\\sqrt{s}=$$ 7, 8, and 13 TeV, corresponding to integrated luminosities of up to 5.1, 19.7, and 35.9 fb$$^{-1}$$, respectively. The data were collected with the CMS detector at the CERN LHC. The results of statistically independent searches for Higgs bosons produced in conjunction with a top quark-antiquark pair and decaying to pairs of W bosons, Z bosons, photons, $$\\tau$$ leptons, or bottom quark jets are combined to maximize sensitivity. An excess of events is observed, with a significance of 5.2 standard deviations, over the expectation from the background-only hypothesis. The corresponding expected significance from the standard model for a Higgs boson mass of 125.09 GeV is 4.2 standard deviations. The combined best fit signal strength normalized to the standard model prediction is 1.26$${^{+0.31}_{-0.26}}$$.« less

A simple linear regression method for quantitative trait loci linkage analysis with censored observations.

PubMed

Anderson, Carl A; McRae, Allan F; Visscher, Peter M

2006-07-01

Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.

MORTICIA, a statistical analysis software package for determining optical surveillance system effectiveness.

NASA Astrophysics Data System (ADS)

Ramkilowan, A.; Griffith, D. J.

2017-10-01

Surveillance modelling in terms of the standard Detect, Recognise and Identify (DRI) thresholds remains a key requirement for determining the effectiveness of surveillance sensors. With readily available computational resources it has become feasible to perform statistically representative evaluations of the effectiveness of these sensors. A new capability for performing this Monte-Carlo type analysis is demonstrated in the MORTICIA (Monte- Carlo Optical Rendering for Theatre Investigations of Capability under the Influence of the Atmosphere) software package developed at the Council for Scientific and Industrial Research (CSIR). This first generation, python-based open-source integrated software package, currently in the alpha stage of development aims to provide all the functionality required to perform statistical investigations of the effectiveness of optical surveillance systems in specific or generic deployment theatres. This includes modelling of the mathematical and physical processes that govern amongst other components of a surveillance system; a sensor's detector and optical components, a target and its background as well as the intervening atmospheric influences. In this paper we discuss integral aspects of the bespoke framework that are critical to the longevity of all subsequent modelling efforts. Additionally, some preliminary results are presented.

Climate Considerations Of The Electricity Supply Systems In Industries

NASA Astrophysics Data System (ADS)

Asset, Khabdullin; Zauresh, Khabdullina

2014-12-01

The study is focused on analysis of climate considerations of electricity supply systems in a pellet industry. The developed analysis model consists of two modules: statistical data of active power losses evaluation module and climate aspects evaluation module. The statistical data module is presented as a universal mathematical model of electrical systems and components of industrial load. It forms a basis for detailed accounting of power loss from the voltage levels. On the basis of the universal model, a set of programs is designed to perform the calculation and experimental research. It helps to obtain the statistical characteristics of the power losses and loads of the electricity supply systems and to define the nature of changes in these characteristics. Within the module, several methods and algorithms for calculating parameters of equivalent circuits of low- and high-voltage ADC and SD with a massive smooth rotor with laminated poles are developed. The climate aspects module includes an analysis of the experimental data of power supply system in pellet production. It allows identification of GHG emission reduction parameters: operation hours, type of electrical motors, values of load factor and deviation of standard value of voltage.

Scaling laws and fluctuations in the statistics of word frequencies

NASA Astrophysics Data System (ADS)

Gerlach, Martin; Altmann, Eduardo G.

2014-11-01

In this paper, we combine statistical analysis of written texts and simple stochastic models to explain the appearance of scaling laws in the statistics of word frequencies. The average vocabulary of an ensemble of fixed-length texts is known to scale sublinearly with the total number of words (Heaps’ law). Analyzing the fluctuations around this average in three large databases (Google-ngram, English Wikipedia, and a collection of scientific articles), we find that the standard deviation scales linearly with the average (Taylor's law), in contrast to the prediction of decaying fluctuations obtained using simple sampling arguments. We explain both scaling laws (Heaps’ and Taylor) by modeling the usage of words using a Poisson process with a fat-tailed distribution of word frequencies (Zipf's law) and topic-dependent frequencies of individual words (as in topic models). Considering topical variations lead to quenched averages, turn the vocabulary size a non-self-averaging quantity, and explain the empirical observations. For the numerous practical applications relying on estimations of vocabulary size, our results show that uncertainties remain large even for long texts. We show how to account for these uncertainties in measurements of lexical richness of texts with different lengths.

PREDICTION OF VO2PEAK USING OMNI RATINGS OF PERCEIVED EXERTION FROM A SUBMAXIMAL CYCLE EXERCISE TEST

PubMed Central

Mays, Ryan J.; Goss, Fredric L.; Nagle-Stilley, Elizabeth F.; Gallagher, Michael; Schafer, Mark A.; Kim, Kevin H.; Robertson, Robert J.

2015-01-01

Summary The primary aim of this study was to develop statistical models to predict peak oxygen consumption (VO2peak) using OMNI Ratings of Perceived Exertion measured during submaximal cycle ergometry. Men (mean ± standard error: 20.90 ± 0.42 yrs) and women (21.59 ± 0.49 yrs) participants (n = 81) completed a load-incremented maximal cycle ergometer exercise test. Simultaneous multiple linear regression was used to develop separate VO2peak statistical models using submaximal ratings of perceived exertion for the overall body, legs, and chest/breathing as predictor variables. VO2peak (L·min−1) predicted for men and women from ratings of perceived exertion for the overall body (3.02 ± 0.06; 2.03 ± 0.04), legs (3.02 ± 0.06; 2.04 ± 0.04) and chest/breathing (3.02 ± 0.05; 2.03 ± 0.03) were similar with measured VO2peak (3.02 ± 0.10; 2.03 ± 0.06, ps > .05). Statistical models based on submaximal OMNI Ratings of Perceived Exertion provide an easily administered and accurate method to predict VO2peak. PMID:25068750

Standard electrode potential, Tafel equation, and the solvation thermodynamics.

PubMed

Matyushov, Dmitry V

2009-06-21

Equilibrium in the electronic subsystem across the solution-metal interface is considered to connect the standard electrode potential to the statistics of localized electronic states in solution. We argue that a correct derivation of the Nernst equation for the electrode potential requires a careful separation of the relevant time scales. An equation for the standard metal potential is derived linking it to the thermodynamics of solvation. The Anderson-Newns model for electronic delocalization between the solution and the electrode is combined with a bilinear model of solute-solvent coupling introducing nonlinear solvation into the theory of heterogeneous electron transfer. We therefore are capable of addressing the question of how nonlinear solvation affects electrochemical observables. The transfer coefficient of electrode kinetics is shown to be equal to the derivative of the free energy, or generalized force, required to shift the unoccupied electronic level in the bulk. The transfer coefficient thus directly quantifies the extent of nonlinear solvation of the redox couple. The current model allows the transfer coefficient to deviate from the value of 0.5 of the linear solvation models at zero electrode overpotential. The electrode current curves become asymmetric in respect to the change in the sign of the electrode overpotential.

Preliminary work toward the development of a dimensional tolerance standard for rapid prototyping

NASA Technical Reports Server (NTRS)

Kennedy, W. J.

1996-01-01

Rapid prototyping is a new technology for building parts quickly from CAD models. It works by slicing a CAD model into layers, then by building a model of the part one layer at a time. Since most parts can be sliced, most parts can be modeled using rapid prototyping. The layers themselves are created in a number of different ways - by using a laser to cure a layer of an epoxy or a resin, by depositing a layer of plastic or wax upon a surface, by using a laser to sinter a layer of powder, or by using a laser to cut a layer of paper. Rapid prototyping (RP) is new, and a standard part for use in comparing dimensional tolerances has not yet been chosen and accepted by ASTM (the American Society for Testing Materials). Such a part is needed when RP is used to build parts for investment casting or for direct use. The objective of this project was to start the development of a standard part by using statistical techniques to choose the features of the part which show curl - the vertical deviation of a part from its intended horizontal plane.

[The evaluation of costs: standards of medical care and clinical statistic groups].

PubMed

Semenov, V Iu; Samorodskaia, I V

2014-01-01

The article presents the comparative analysis of techniques of evaluation of costs of hospital treatment using medical economic standards of medical care and clinical statistical groups. The technique of evaluation of costs on the basis of clinical statistical groups was developed almost fifty years ago and is largely applied in a number of countries. Nowadays, in Russia the payment for completed case of treatment on the basis of medical economic standards is the main mode of payment for medical care in hospital. It is very conditionally a Russian analogue of world-wide prevalent system of diagnostic related groups. The tariffs for these cases of treatment as opposed to clinical statistical groups are counted on basis of standards of provision of medical care approved by Minzdrav of Russia. The information derived from generalization of cases of treatment of real patients is not applied.