Bayesian statistics in medicine: a 25 year review.

PubMed

Ashby, Deborah

2006-11-15

This review examines the state of Bayesian thinking as Statistics in Medicine was launched in 1982, reflecting particularly on its applicability and uses in medical research. It then looks at each subsequent five-year epoch, with a focus on papers appearing in Statistics in Medicine, putting these in the context of major developments in Bayesian thinking and computation with reference to important books, landmark meetings and seminal papers. It charts the growth of Bayesian statistics as it is applied to medicine and makes predictions for the future. From sparse beginnings, where Bayesian statistics was barely mentioned, Bayesian statistics has now permeated all the major areas of medical statistics, including clinical trials, epidemiology, meta-analyses and evidence synthesis, spatial modelling, longitudinal modelling, survival modelling, molecular genetics and decision-making in respect of new technologies.

Performance of the S - [chi][squared] Statistic for Full-Information Bifactor Models

ERIC Educational Resources Information Center

Li, Ying; Rupp, Andre A.

2011-01-01

This study investigated the Type I error rate and power of the multivariate extension of the S - [chi][squared] statistic using unidimensional and multidimensional item response theory (UIRT and MIRT, respectively) models as well as full-information bifactor (FI-bifactor) models through simulation. Manipulated factors included test length, sample…

Assessment of the scale effect on statistical downscaling quality at a station scale using a weather generator-based model

USDA-ARS?s Scientific Manuscript database

The resolution of General Circulation Models (GCMs) is too coarse to assess the fine scale or site-specific impacts of climate change. Downscaling approaches including dynamical and statistical downscaling have been developed to meet this requirement. As the resolution of climate model increases, it...

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

Regression Models for Identifying Noise Sources in Magnetic Resonance Images

PubMed Central

Zhu, Hongtu; Li, Yimei; Ibrahim, Joseph G.; Shi, Xiaoyan; An, Hongyu; Chen, Yashen; Gao, Wei; Lin, Weili; Rowe, Daniel B.; Peterson, Bradley S.

2009-01-01

Stochastic noise, susceptibility artifacts, magnetic field and radiofrequency inhomogeneities, and other noise components in magnetic resonance images (MRIs) can introduce serious bias into any measurements made with those images. We formally introduce three regression models including a Rician regression model and two associated normal models to characterize stochastic noise in various magnetic resonance imaging modalities, including diffusion-weighted imaging (DWI) and functional MRI (fMRI). Estimation algorithms are introduced to maximize the likelihood function of the three regression models. We also develop a diagnostic procedure for systematically exploring MR images to identify noise components other than simple stochastic noise, and to detect discrepancies between the fitted regression models and MRI data. The diagnostic procedure includes goodness-of-fit statistics, measures of influence, and tools for graphical display. The goodness-of-fit statistics can assess the key assumptions of the three regression models, whereas measures of influence can isolate outliers caused by certain noise components, including motion artifacts. The tools for graphical display permit graphical visualization of the values for the goodness-of-fit statistic and influence measures. Finally, we conduct simulation studies to evaluate performance of these methods, and we analyze a real dataset to illustrate how our diagnostic procedure localizes subtle image artifacts by detecting intravoxel variability that is not captured by the regression models. PMID:19890478

Statistical Modeling for Radiation Hardness Assurance

NASA Technical Reports Server (NTRS)

Ladbury, Raymond L.

2014-01-01

We cover the models and statistics associated with single event effects (and total ionizing dose), why we need them, and how to use them: What models are used, what errors exist in real test data, and what the model allows us to say about the DUT will be discussed. In addition, how to use other sources of data such as historical, heritage, and similar part and how to apply experience, physics, and expert opinion to the analysis will be covered. Also included will be concepts of Bayesian statistics, data fitting, and bounding rates.

Analyzing longitudinal data with the linear mixed models procedure in SPSS.

PubMed

West, Brady T

2009-09-01

Many applied researchers analyzing longitudinal data share a common misconception: that specialized statistical software is necessary to fit hierarchical linear models (also known as linear mixed models [LMMs], or multilevel models) to longitudinal data sets. Although several specialized statistical software programs of high quality are available that allow researchers to fit these models to longitudinal data sets (e.g., HLM), rapid advances in general purpose statistical software packages have recently enabled analysts to fit these same models when using preferred packages that also enable other more common analyses. One of these general purpose statistical packages is SPSS, which includes a very flexible and powerful procedure for fitting LMMs to longitudinal data sets with continuous outcomes. This article aims to present readers with a practical discussion of how to analyze longitudinal data using the LMMs procedure in the SPSS statistical software package.

Probability, statistics, and computational science.

PubMed

Beerenwinkel, Niko; Siebourg, Juliane

2012-01-01

In this chapter, we review basic concepts from probability theory and computational statistics that are fundamental to evolutionary genomics. We provide a very basic introduction to statistical modeling and discuss general principles, including maximum likelihood and Bayesian inference. Markov chains, hidden Markov models, and Bayesian network models are introduced in more detail as they occur frequently and in many variations in genomics applications. In particular, we discuss efficient inference algorithms and methods for learning these models from partially observed data. Several simple examples are given throughout the text, some of which point to models that are discussed in more detail in subsequent chapters.

Statistical error model for a solar electric propulsion thrust subsystem

NASA Technical Reports Server (NTRS)

Bantell, M. H.

1973-01-01

The solar electric propulsion thrust subsystem statistical error model was developed as a tool for investigating the effects of thrust subsystem parameter uncertainties on navigation accuracy. The model is currently being used to evaluate the impact of electric engine parameter uncertainties on navigation system performance for a baseline mission to Encke's Comet in the 1980s. The data given represent the next generation in statistical error modeling for low-thrust applications. Principal improvements include the representation of thrust uncertainties and random process modeling in terms of random parametric variations in the thrust vector process for a multi-engine configuration.

Statistical models for the analysis and design of digital polymerase chain (dPCR) experiments

USGS Publications Warehouse

Dorazio, Robert; Hunter, Margaret

2015-01-01

Statistical methods for the analysis and design of experiments using digital PCR (dPCR) have received only limited attention and have been misused in many instances. To address this issue and to provide a more general approach to the analysis of dPCR data, we describe a class of statistical models for the analysis and design of experiments that require quantification of nucleic acids. These models are mathematically equivalent to generalized linear models of binomial responses that include a complementary, log–log link function and an offset that is dependent on the dPCR partition volume. These models are both versatile and easy to fit using conventional statistical software. Covariates can be used to specify different sources of variation in nucleic acid concentration, and a model’s parameters can be used to quantify the effects of these covariates. For purposes of illustration, we analyzed dPCR data from different types of experiments, including serial dilution, evaluation of copy number variation, and quantification of gene expression. We also showed how these models can be used to help design dPCR experiments, as in selection of sample sizes needed to achieve desired levels of precision in estimates of nucleic acid concentration or to detect differences in concentration among treatments with prescribed levels of statistical power.

Multiple commodities in statistical microeconomics: Model and market

NASA Astrophysics Data System (ADS)

Baaquie, Belal E.; Yu, Miao; Du, Xin

2016-11-01

A statistical generalization of microeconomics has been made in Baaquie (2013). In Baaquie et al. (2015), the market behavior of single commodities was analyzed and it was shown that market data provides strong support for the statistical microeconomic description of commodity prices. The case of multiple commodities is studied and a parsimonious generalization of the single commodity model is made for the multiple commodities case. Market data shows that the generalization can accurately model the simultaneous correlation functions of up to four commodities. To accurately model five or more commodities, further terms have to be included in the model. This study shows that the statistical microeconomics approach is a comprehensive and complete formulation of microeconomics, and which is independent to the mainstream formulation of microeconomics.

Global Sensitivity Analysis of Environmental Systems via Multiple Indices based on Statistical Moments of Model Outputs

NASA Astrophysics Data System (ADS)

Guadagnini, A.; Riva, M.; Dell'Oca, A.

2017-12-01

We propose to ground sensitivity of uncertain parameters of environmental models on a set of indices based on the main (statistical) moments, i.e., mean, variance, skewness and kurtosis, of the probability density function (pdf) of a target model output. This enables us to perform Global Sensitivity Analysis (GSA) of a model in terms of multiple statistical moments and yields a quantification of the impact of model parameters on features driving the shape of the pdf of model output. Our GSA approach includes the possibility of being coupled with the construction of a reduced complexity model that allows approximating the full model response at a reduced computational cost. We demonstrate our approach through a variety of test cases. These include a commonly used analytical benchmark, a simplified model representing pumping in a coastal aquifer, a laboratory-scale tracer experiment, and the migration of fracturing fluid through a naturally fractured reservoir (source) to reach an overlying formation (target). Our strategy allows discriminating the relative importance of model parameters to the four statistical moments considered. We also provide an appraisal of the error associated with the evaluation of our sensitivity metrics by replacing the original system model through the selected surrogate model. Our results suggest that one might need to construct a surrogate model with increasing level of accuracy depending on the statistical moment considered in the GSA. The methodological framework we propose can assist the development of analysis techniques targeted to model calibration, design of experiment, uncertainty quantification and risk assessment.

A smoothed residual based goodness-of-fit statistic for nest-survival models

Treesearch

Rodney X. Sturdivant; Jay J. Rotella; Robin E. Russell

2008-01-01

Estimating nest success and identifying important factors related to nest-survival rates is an essential goal for many wildlife researchers interested in understanding avian population dynamics. Advances in statistical methods have led to a number of estimation methods and approaches to modeling this problem. Recently developed models allow researchers to include a...

SOCR Analyses – an Instructional Java Web-based Statistical Analysis Toolkit

PubMed Central

Chu, Annie; Cui, Jenny; Dinov, Ivo D.

2011-01-01

The Statistical Online Computational Resource (SOCR) designs web-based tools for educational use in a variety of undergraduate courses (Dinov 2006). Several studies have demonstrated that these resources significantly improve students' motivation and learning experiences (Dinov et al. 2008). SOCR Analyses is a new component that concentrates on data modeling and analysis using parametric and non-parametric techniques supported with graphical model diagnostics. Currently implemented analyses include commonly used models in undergraduate statistics courses like linear models (Simple Linear Regression, Multiple Linear Regression, One-Way and Two-Way ANOVA). In addition, we implemented tests for sample comparisons, such as t-test in the parametric category; and Wilcoxon rank sum test, Kruskal-Wallis test, Friedman's test, in the non-parametric category. SOCR Analyses also include several hypothesis test models, such as Contingency tables, Friedman's test and Fisher's exact test. The code itself is open source (http://socr.googlecode.com/), hoping to contribute to the efforts of the statistical computing community. The code includes functionality for each specific analysis model and it has general utilities that can be applied in various statistical computing tasks. For example, concrete methods with API (Application Programming Interface) have been implemented in statistical summary, least square solutions of general linear models, rank calculations, etc. HTML interfaces, tutorials, source code, activities, and data are freely available via the web (www.SOCR.ucla.edu). Code examples for developers and demos for educators are provided on the SOCR Wiki website. In this article, the pedagogical utilization of the SOCR Analyses is discussed, as well as the underlying design framework. As the SOCR project is on-going and more functions and tools are being added to it, these resources are constantly improved. The reader is strongly encouraged to check the SOCR site for most updated information and newly added models. PMID:21546994

Development of a statistical model for cervical cancer cell death with irreversible electroporation in vitro.

PubMed

Yang, Yongji; Moser, Michael A J; Zhang, Edwin; Zhang, Wenjun; Zhang, Bing

2018-01-01

The aim of this study was to develop a statistical model for cell death by irreversible electroporation (IRE) and to show that the statistic model is more accurate than the electric field threshold model in the literature using cervical cancer cells in vitro. HeLa cell line was cultured and treated with different IRE protocols in order to obtain data for modeling the statistical relationship between the cell death and pulse-setting parameters. In total, 340 in vitro experiments were performed with a commercial IRE pulse system, including a pulse generator and an electric cuvette. Trypan blue staining technique was used to evaluate cell death after 4 hours of incubation following IRE treatment. Peleg-Fermi model was used in the study to build the statistical relationship using the cell viability data obtained from the in vitro experiments. A finite element model of IRE for the electric field distribution was also built. Comparison of ablation zones between the statistical model and electric threshold model (drawn from the finite element model) was used to show the accuracy of the proposed statistical model in the description of the ablation zone and its applicability in different pulse-setting parameters. The statistical models describing the relationships between HeLa cell death and pulse length and the number of pulses, respectively, were built. The values of the curve fitting parameters were obtained using the Peleg-Fermi model for the treatment of cervical cancer with IRE. The difference in the ablation zone between the statistical model and the electric threshold model was also illustrated to show the accuracy of the proposed statistical model in the representation of ablation zone in IRE. This study concluded that: (1) the proposed statistical model accurately described the ablation zone of IRE with cervical cancer cells, and was more accurate compared with the electric field model; (2) the proposed statistical model was able to estimate the value of electric field threshold for the computer simulation of IRE in the treatment of cervical cancer; and (3) the proposed statistical model was able to express the change in ablation zone with the change in pulse-setting parameters.

Statistics for X-chromosome associations.

PubMed

Özbek, Umut; Lin, Hui-Min; Lin, Yan; Weeks, Daniel E; Chen, Wei; Shaffer, John R; Purcell, Shaun M; Feingold, Eleanor

2018-06-13

In a genome-wide association study (GWAS), association between genotype and phenotype at autosomal loci is generally tested by regression models. However, X-chromosome data are often excluded from published analyses of autosomes because of the difference between males and females in number of X chromosomes. Failure to analyze X-chromosome data at all is obviously less than ideal, and can lead to missed discoveries. Even when X-chromosome data are included, they are often analyzed with suboptimal statistics. Several mathematically sensible statistics for X-chromosome association have been proposed. The optimality of these statistics, however, is based on very specific simple genetic models. In addition, while previous simulation studies of these statistics have been informative, they have focused on single-marker tests and have not considered the types of error that occur even under the null hypothesis when the entire X chromosome is scanned. In this study, we comprehensively tested several X-chromosome association statistics using simulation studies that include the entire chromosome. We also considered a wide range of trait models for sex differences and phenotypic effects of X inactivation. We found that models that do not incorporate a sex effect can have large type I error in some cases. We also found that many of the best statistics perform well even when there are modest deviations, such as trait variance differences between the sexes or small sex differences in allele frequencies, from assumptions. © 2018 WILEY PERIODICALS, INC.

Statistical ecology comes of age.

PubMed

Gimenez, Olivier; Buckland, Stephen T; Morgan, Byron J T; Bez, Nicolas; Bertrand, Sophie; Choquet, Rémi; Dray, Stéphane; Etienne, Marie-Pierre; Fewster, Rachel; Gosselin, Frédéric; Mérigot, Bastien; Monestiez, Pascal; Morales, Juan M; Mortier, Frédéric; Munoz, François; Ovaskainen, Otso; Pavoine, Sandrine; Pradel, Roger; Schurr, Frank M; Thomas, Len; Thuiller, Wilfried; Trenkel, Verena; de Valpine, Perry; Rexstad, Eric

2014-12-01

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1-4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.

Statistical ecology comes of age

PubMed Central

Gimenez, Olivier; Buckland, Stephen T.; Morgan, Byron J. T.; Bez, Nicolas; Bertrand, Sophie; Choquet, Rémi; Dray, Stéphane; Etienne, Marie-Pierre; Fewster, Rachel; Gosselin, Frédéric; Mérigot, Bastien; Monestiez, Pascal; Morales, Juan M.; Mortier, Frédéric; Munoz, François; Ovaskainen, Otso; Pavoine, Sandrine; Pradel, Roger; Schurr, Frank M.; Thomas, Len; Thuiller, Wilfried; Trenkel, Verena; de Valpine, Perry; Rexstad, Eric

2014-01-01

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1–4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data. PMID:25540151

Comparing estimates of climate change impacts from process-based and statistical crop models

NASA Astrophysics Data System (ADS)

Lobell, David B.; Asseng, Senthold

2017-01-01

The potential impacts of climate change on crop productivity are of widespread interest to those concerned with addressing climate change and improving global food security. Two common approaches to assess these impacts are process-based simulation models, which attempt to represent key dynamic processes affecting crop yields, and statistical models, which estimate functional relationships between historical observations of weather and yields. Examples of both approaches are increasingly found in the scientific literature, although often published in different disciplinary journals. Here we compare published sensitivities to changes in temperature, precipitation, carbon dioxide (CO2), and ozone from each approach for the subset of crops, locations, and climate scenarios for which both have been applied. Despite a common perception that statistical models are more pessimistic, we find no systematic differences between the predicted sensitivities to warming from process-based and statistical models up to +2 °C, with limited evidence at higher levels of warming. For precipitation, there are many reasons why estimates could be expected to differ, but few estimates exist to develop robust comparisons, and precipitation changes are rarely the dominant factor for predicting impacts given the prominent role of temperature, CO2, and ozone changes. A common difference between process-based and statistical studies is that the former tend to include the effects of CO2 increases that accompany warming, whereas statistical models typically do not. Major needs moving forward include incorporating CO2 effects into statistical studies, improving both approaches’ treatment of ozone, and increasing the use of both methods within the same study. At the same time, those who fund or use crop model projections should understand that in the short-term, both approaches when done well are likely to provide similar estimates of warming impacts, with statistical models generally requiring fewer resources to produce robust estimates, especially when applied to crops beyond the major grains.

Use of model calibration to achieve high accuracy in analysis of computer networks

DOEpatents

Frogner, Bjorn; Guarro, Sergio; Scharf, Guy

2004-05-11

A system and method are provided for creating a network performance prediction model, and calibrating the prediction model, through application of network load statistical analyses. The method includes characterizing the measured load on the network, which may include background load data obtained over time, and may further include directed load data representative of a transaction-level event. Probabilistic representations of load data are derived to characterize the statistical persistence of the network performance variability and to determine delays throughout the network. The probabilistic representations are applied to the network performance prediction model to adapt the model for accurate prediction of network performance. Certain embodiments of the method and system may be used for analysis of the performance of a distributed application characterized as data packet streams.

Statistical metrology—measurement and modeling of variation for advanced process development and design rule generation

NASA Astrophysics Data System (ADS)

Boning, Duane S.; Chung, James E.

1998-11-01

Advanced process technology will require more detailed understanding and tighter control of variation in devices and interconnects. The purpose of statistical metrology is to provide methods to measure and characterize variation, to model systematic and random components of that variation, and to understand the impact of variation on both yield and performance of advanced circuits. Of particular concern are spatial or pattern-dependencies within individual chips; such systematic variation within the chip can have a much larger impact on performance than wafer-level random variation. Statistical metrology methods will play an important role in the creation of design rules for advanced technologies. For example, a key issue in multilayer interconnect is the uniformity of interlevel dielectric (ILD) thickness within the chip. For the case of ILD thickness, we describe phases of statistical metrology development and application to understanding and modeling thickness variation arising from chemical-mechanical polishing (CMP). These phases include screening experiments including design of test structures and test masks to gather electrical or optical data, techniques for statistical decomposition and analysis of the data, and approaches to calibrating empirical and physical variation models. These models can be integrated with circuit CAD tools to evaluate different process integration or design rule strategies. One focus for the generation of interconnect design rules are guidelines for the use of "dummy fill" or "metal fill" to improve the uniformity of underlying metal density and thus improve the uniformity of oxide thickness within the die. Trade-offs that can be evaluated via statistical metrology include the improvements to uniformity possible versus the effect of increased capacitance due to additional metal.

Monte Carlo based statistical power analysis for mediation models: methods and software.

PubMed

Zhang, Zhiyong

2014-12-01

The existing literature on statistical power analysis for mediation models often assumes data normality and is based on a less powerful Sobel test instead of the more powerful bootstrap test. This study proposes to estimate statistical power to detect mediation effects on the basis of the bootstrap method through Monte Carlo simulation. Nonnormal data with excessive skewness and kurtosis are allowed in the proposed method. A free R package called bmem is developed to conduct the power analysis discussed in this study. Four examples, including a simple mediation model, a multiple-mediator model with a latent mediator, a multiple-group mediation model, and a longitudinal mediation model, are provided to illustrate the proposed method.

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.

Statistics of a neuron model driven by asymmetric colored noise.

PubMed

Müller-Hansen, Finn; Droste, Felix; Lindner, Benjamin

2015-02-01

Irregular firing of neurons can be modeled as a stochastic process. Here we study the perfect integrate-and-fire neuron driven by dichotomous noise, a Markovian process that jumps between two states (i.e., possesses a non-Gaussian statistics) and exhibits nonvanishing temporal correlations (i.e., represents a colored noise). Specifically, we consider asymmetric dichotomous noise with two different transition rates. Using a first-passage-time formulation, we derive exact expressions for the probability density and the serial correlation coefficient of the interspike interval (time interval between two subsequent neural action potentials) and the power spectrum of the spike train. Furthermore, we extend the model by including additional Gaussian white noise, and we give approximations for the interspike interval (ISI) statistics in this case. Numerical simulations are used to validate the exact analytical results for pure dichotomous noise, and to test the approximations of the ISI statistics when Gaussian white noise is included. The results may help to understand how correlations and asymmetry of noise and signals in nerve cells shape neuronal firing statistics.

Comparative evaluation of statistical and mechanistic models of Escherichia coli at beaches in southern Lake Michigan

USGS Publications Warehouse

Safaie, Ammar; Wendzel, Aaron; Ge, Zhongfu; Nevers, Meredith; Whitman, Richard L.; Corsi, Steven R.; Phanikumar, Mantha S.

2016-01-01

Statistical and mechanistic models are popular tools for predicting the levels of indicator bacteria at recreational beaches. Researchers tend to use one class of model or the other, and it is difficult to generalize statements about their relative performance due to differences in how the models are developed, tested, and used. We describe a cooperative modeling approach for freshwater beaches impacted by point sources in which insights derived from mechanistic modeling were used to further improve the statistical models and vice versa. The statistical models provided a basis for assessing the mechanistic models which were further improved using probability distributions to generate high-resolution time series data at the source, long-term “tracer” transport modeling based on observed electrical conductivity, better assimilation of meteorological data, and the use of unstructured-grids to better resolve nearshore features. This approach resulted in improved models of comparable performance for both classes including a parsimonious statistical model suitable for real-time predictions based on an easily measurable environmental variable (turbidity). The modeling approach outlined here can be used at other sites impacted by point sources and has the potential to improve water quality predictions resulting in more accurate estimates of beach closures.

Bayesian models: A statistical primer for ecologists

USGS Publications Warehouse

Hobbs, N. Thompson; Hooten, Mevin B.

2015-01-01

Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticiansCovers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and moreDeemphasizes computer coding in favor of basic principlesExplains how to write out properly factored statistical expressions representing Bayesian models

Statistics, Computation, and Modeling in Cosmology

NASA Astrophysics Data System (ADS)

Jewell, Jeff; Guiness, Joe; SAMSI 2016 Working Group in Cosmology

2017-01-01

Current and future ground and space based missions are designed to not only detect, but map out with increasing precision, details of the universe in its infancy to the present-day. As a result we are faced with the challenge of analyzing and interpreting observations from a wide variety of instruments to form a coherent view of the universe. Finding solutions to a broad range of challenging inference problems in cosmology is one of the goals of the “Statistics, Computation, and Modeling in Cosmology” workings groups, formed as part of the year long program on ‘Statistical, Mathematical, and Computational Methods for Astronomy’, hosted by the Statistical and Applied Mathematical Sciences Institute (SAMSI), a National Science Foundation funded institute. Two application areas have emerged for focused development in the cosmology working group involving advanced algorithmic implementations of exact Bayesian inference for the Cosmic Microwave Background, and statistical modeling of galaxy formation. The former includes study and development of advanced Markov Chain Monte Carlo algorithms designed to confront challenging inference problems including inference for spatial Gaussian random fields in the presence of sources of galactic emission (an example of a source separation problem). Extending these methods to future redshift survey data probing the nonlinear regime of large scale structure formation is also included in the working group activities. In addition, the working group is also focused on the study of ‘Galacticus’, a galaxy formation model applied to dark matter-only cosmological N-body simulations operating on time-dependent halo merger trees. The working group is interested in calibrating the Galacticus model to match statistics of galaxy survey observations; specifically stellar mass functions, luminosity functions, and color-color diagrams. The group will use subsampling approaches and fractional factorial designs to statistically and computationally efficiently explore the Galacticus parameter space. The group will also use the Galacticus simulations to study the relationship between the topological and physical structure of the halo merger trees and the properties of the resulting galaxies.

75 FR 72611 - Assessments, Large Bank Pricing

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-24

... the worst risk ranking and are included in the statistical analysis. Appendix 1 to the NPR describes the statistical analysis in detail. \\12\\ The percentage approximated by factors is based on the statistical model for that particual year. Actual weights assigned to each scorecard measure are largely based...

Statistical Models for Predicting Automobile Driving Postures for Men and Women Including Effects of Age.

PubMed

Park, Jangwoon; Ebert, Sheila M; Reed, Matthew P; Hallman, Jason J

2016-03-01

Previously published statistical models of driving posture have been effective for vehicle design but have not taken into account the effects of age. The present study developed new statistical models for predicting driving posture. Driving postures of 90 U.S. drivers with a wide range of age and body size were measured in laboratory mockup in nine package conditions. Posture-prediction models for female and male drivers were separately developed by employing a stepwise regression technique using age, body dimensions, vehicle package conditions, and two-way interactions, among other variables. Driving posture was significantly associated with age, and the effects of other variables depended on age. A set of posture-prediction models is presented for women and men. The results are compared with a previously developed model. The present study is the first study of driver posture to include a large cohort of older drivers and the first to report a significant effect of age. The posture-prediction models can be used to position computational human models or crash-test dummies for vehicle design and assessment. © 2015, Human Factors and Ergonomics Society.

Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment

PubMed Central

Hashim, Mazlan

2015-01-01

This research presents the results of the GIS-based statistical models for generation of landslide susceptibility mapping using geographic information system (GIS) and remote-sensing data for Cameron Highlands area in Malaysia. Ten factors including slope, aspect, soil, lithology, NDVI, land cover, distance to drainage, precipitation, distance to fault, and distance to road were extracted from SAR data, SPOT 5 and WorldView-1 images. The relationships between the detected landslide locations and these ten related factors were identified by using GIS-based statistical models including analytical hierarchy process (AHP), weighted linear combination (WLC) and spatial multi-criteria evaluation (SMCE) models. The landslide inventory map which has a total of 92 landslide locations was created based on numerous resources such as digital aerial photographs, AIRSAR data, WorldView-1 images, and field surveys. Then, 80% of the landslide inventory was used for training the statistical models and the remaining 20% was used for validation purpose. The validation results using the Relative landslide density index (R-index) and Receiver operating characteristic (ROC) demonstrated that the SMCE model (accuracy is 96%) is better in prediction than AHP (accuracy is 91%) and WLC (accuracy is 89%) models. These landslide susceptibility maps would be useful for hazard mitigation purpose and regional planning. PMID:25898919

Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment.

PubMed

Shahabi, Himan; Hashim, Mazlan

2015-04-22

This research presents the results of the GIS-based statistical models for generation of landslide susceptibility mapping using geographic information system (GIS) and remote-sensing data for Cameron Highlands area in Malaysia. Ten factors including slope, aspect, soil, lithology, NDVI, land cover, distance to drainage, precipitation, distance to fault, and distance to road were extracted from SAR data, SPOT 5 and WorldView-1 images. The relationships between the detected landslide locations and these ten related factors were identified by using GIS-based statistical models including analytical hierarchy process (AHP), weighted linear combination (WLC) and spatial multi-criteria evaluation (SMCE) models. The landslide inventory map which has a total of 92 landslide locations was created based on numerous resources such as digital aerial photographs, AIRSAR data, WorldView-1 images, and field surveys. Then, 80% of the landslide inventory was used for training the statistical models and the remaining 20% was used for validation purpose. The validation results using the Relative landslide density index (R-index) and Receiver operating characteristic (ROC) demonstrated that the SMCE model (accuracy is 96%) is better in prediction than AHP (accuracy is 91%) and WLC (accuracy is 89%) models. These landslide susceptibility maps would be useful for hazard mitigation purpose and regional planning.

The Effects of Measurement Error on Statistical Models for Analyzing Change. Final Report.

ERIC Educational Resources Information Center

Dunivant, Noel

The results of six major projects are discussed including a comprehensive mathematical and statistical analysis of the problems caused by errors of measurement in linear models for assessing change. In a general matrix representation of the problem, several new analytic results are proved concerning the parameters which affect bias in…

Statistical modeling of space shuttle environmental data

NASA Technical Reports Server (NTRS)

Tubbs, J. D.; Brewer, D. W.

1983-01-01

Statistical models which use a class of bivariate gamma distribution are examined. Topics discussed include: (1) the ratio of positively correlated gamma varieties; (2) a method to determine if unequal shape parameters are necessary in bivariate gamma distribution; (3) differential equations for modal location of a family of bivariate gamma distribution; and (4) analysis of some wind gust data using the analytical results developed for modeling application.

A new approach to fracture modelling in reservoirs using deterministic, genetic and statistical models of fracture growth

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

Rawnsley, K.; Swaby, P.

1996-08-01

It is increasingly acknowledged that in order to understand and forecast the behavior of fracture influenced reservoirs we must attempt to reproduce the fracture system geometry and use this as a basis for fluid flow calculation. This article aims to present a recently developed fracture modelling prototype designed specifically for use in hydrocarbon reservoir environments. The prototype {open_quotes}FRAME{close_quotes} (FRActure Modelling Environment) aims to provide a tool which will allow the generation of realistic 3D fracture systems within a reservoir model, constrained to the known geology of the reservoir by both mechanical and statistical considerations, and which can be used asmore » a basis for fluid flow calculation. Two newly developed modelling techniques are used. The first is an interactive tool which allows complex fault surfaces and their associated deformations to be reproduced. The second is a {open_quotes}genetic{close_quotes} model which grows fracture patterns from seeds using conceptual models of fracture development. The user defines the mechanical input and can retrieve all the statistics of the growing fractures to allow comparison to assumed statistical distributions for the reservoir fractures. Input parameters include growth rate, fracture interaction characteristics, orientation maps and density maps. More traditional statistical stochastic fracture models are also incorporated. FRAME is designed to allow the geologist to input hard or soft data including seismically defined surfaces, well fractures, outcrop models, analogue or numerical mechanical models or geological {open_quotes}feeling{close_quotes}. The geologist is not restricted to {open_quotes}a priori{close_quotes} models of fracture patterns that may not correspond to the data.« less

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

NASA Astrophysics Data System (ADS)

Sinha, Manodeep; Berlind, Andreas A.; McBride, Cameron K.; Scoccimarro, Roman; Piscionere, Jennifer A.; Wibking, Benjamin D.

2018-04-01

Interpreting the small-scale clustering of galaxies with halo models can elucidate the connection between galaxies and dark matter halos. Unfortunately, the modelling is typically not sufficiently accurate for ruling out models statistically. It is thus difficult to use the information encoded in small scales to test cosmological models or probe subtle features of the galaxy-halo connection. In this paper, we attempt to push halo modelling into the "accurate" regime with a fully numerical mock-based methodology and careful treatment of statistical and systematic errors. With our forward-modelling approach, we can incorporate clustering statistics beyond the traditional two-point statistics. We use this modelling methodology to test the standard ΛCDM + halo model against the clustering of SDSS DR7 galaxies. Specifically, we use the projected correlation function, group multiplicity function and galaxy number density as constraints. We find that while the model fits each statistic separately, it struggles to fit them simultaneously. Adding group statistics leads to a more stringent test of the model and significantly tighter constraints on model parameters. We explore the impact of varying the adopted halo definition and cosmological model and find that changing the cosmology makes a significant difference. The most successful model we tried (Planck cosmology with Mvir halos) matches the clustering of low luminosity galaxies, but exhibits a 2.3σ tension with the clustering of luminous galaxies, thus providing evidence that the "standard" halo model needs to be extended. This work opens the door to adding interesting freedom to the halo model and including additional clustering statistics as constraints.

Directional statistics-based reflectance model for isotropic bidirectional reflectance distribution functions.

PubMed

Nishino, Ko; Lombardi, Stephen

2011-01-01

We introduce a novel parametric bidirectional reflectance distribution function (BRDF) model that can accurately encode a wide variety of real-world isotropic BRDFs with a small number of parameters. The key observation we make is that a BRDF may be viewed as a statistical distribution on a unit hemisphere. We derive a novel directional statistics distribution, which we refer to as the hemispherical exponential power distribution, and model real-world isotropic BRDFs as mixtures of it. We derive a canonical probabilistic method for estimating the parameters, including the number of components, of this novel directional statistics BRDF model. We show that the model captures the full spectrum of real-world isotropic BRDFs with high accuracy, but a small footprint. We also demonstrate the advantages of the novel BRDF model by showing its use for reflection component separation and for exploring the space of isotropic BRDFs.

Statistical Surrogate Modeling of Atmospheric Dispersion Events Using Bayesian Adaptive Splines

NASA Astrophysics Data System (ADS)

Francom, D.; Sansó, B.; Bulaevskaya, V.; Lucas, D. D.

2016-12-01

Uncertainty in the inputs of complex computer models, including atmospheric dispersion and transport codes, is often assessed via statistical surrogate models. Surrogate models are computationally efficient statistical approximations of expensive computer models that enable uncertainty analysis. We introduce Bayesian adaptive spline methods for producing surrogate models that capture the major spatiotemporal patterns of the parent model, while satisfying all the necessities of flexibility, accuracy and computational feasibility. We present novel methodological and computational approaches motivated by a controlled atmospheric tracer release experiment conducted at the Diablo Canyon nuclear power plant in California. Traditional methods for building statistical surrogate models often do not scale well to experiments with large amounts of data. Our approach is well suited to experiments involving large numbers of model inputs, large numbers of simulations, and functional output for each simulation. Our approach allows us to perform global sensitivity analysis with ease. We also present an approach to calibration of simulators using field data.

Illuminating Tradespace Decisions Using Efficient Experimental Space-Filling Designs for the Engineered Resilient System Architecture

DTIC Science & Technology

2015-06-30

7. Building Statistical Metamodels using Simulation Experimental Designs ............................................... 34 7.1. Statistical Design...system design drivers across several different domain models, our methodology uses statistical metamodeling to approximate the simulations’ behavior. A...output. We build metamodels using a number of statistical methods that include stepwise regression, boosted trees, neural nets, and bootstrap forest

Illuminating Tradespace Decisions Using Efficient Experimental Space-Filling Designs for the Engineered Resilient System Architecture

DTIC Science & Technology

2015-06-01

7. Building Statistical Metamodels using Simulation Experimental Designs ............................................... 34 7.1. Statistical Design...system design drivers across several different domain models, our methodology uses statistical metamodeling to approximate the simulations’ behavior. A...output. We build metamodels using a number of statistical methods that include stepwise regression, boosted trees, neural nets, and bootstrap forest

Recent statistical methods for orientation data

NASA Technical Reports Server (NTRS)

Batschelet, E.

1972-01-01

The application of statistical methods for determining the areas of animal orientation and navigation are discussed. The method employed is limited to the two-dimensional case. Various tests for determining the validity of the statistical analysis are presented. Mathematical models are included to support the theoretical considerations and tables of data are developed to show the value of information obtained by statistical analysis.

A simple rain attenuation model for earth-space radio links operating at 10-35 GHz

NASA Technical Reports Server (NTRS)

Stutzman, W. L.; Yon, K. M.

1986-01-01

The simple attenuation model has been improved from an earlier version and now includes the effect of wave polarization. The model is for the prediction of rain attenuation statistics on earth-space communication links operating in the 10-35 GHz band. Simple calculations produce attenuation values as a function of average rain rate. These together with rain rate statistics (either measured or predicted) can be used to predict annual rain attenuation statistics. In this paper model predictions are compared to measured data from a data base of 62 experiments performed in the U.S., Europe, and Japan. Comparisons are also made to predictions from other models.

Improvements to an earth observing statistical performance model with applications to LWIR spectral variability

NASA Astrophysics Data System (ADS)

Zhao, Runchen; Ientilucci, Emmett J.

2017-05-01

Hyperspectral remote sensing systems provide spectral data composed of hundreds of narrow spectral bands. Spectral remote sensing systems can be used to identify targets, for example, without physical interaction. Often it is of interested to characterize the spectral variability of targets or objects. The purpose of this paper is to identify and characterize the LWIR spectral variability of targets based on an improved earth observing statistical performance model, known as the Forecasting and Analysis of Spectroradiometric System Performance (FASSP) model. FASSP contains three basic modules including a scene model, sensor model and a processing model. Instead of using mean surface reflectance only as input to the model, FASSP transfers user defined statistical characteristics of a scene through the image chain (i.e., from source to sensor). The radiative transfer model, MODTRAN, is used to simulate the radiative transfer based on user defined atmospheric parameters. To retrieve class emissivity and temperature statistics, or temperature / emissivity separation (TES), a LWIR atmospheric compensation method is necessary. The FASSP model has a method to transform statistics in the visible (ie., ELM) but currently does not have LWIR TES algorithm in place. This paper addresses the implementation of such a TES algorithm and its associated transformation of statistics.

Autoregressive statistical pattern recognition algorithms for damage detection in civil structures

NASA Astrophysics Data System (ADS)

Yao, Ruigen; Pakzad, Shamim N.

2012-08-01

Statistical pattern recognition has recently emerged as a promising set of complementary methods to system identification for automatic structural damage assessment. Its essence is to use well-known concepts in statistics for boundary definition of different pattern classes, such as those for damaged and undamaged structures. In this paper, several statistical pattern recognition algorithms using autoregressive models, including statistical control charts and hypothesis testing, are reviewed as potentially competitive damage detection techniques. To enhance the performance of statistical methods, new feature extraction techniques using model spectra and residual autocorrelation, together with resampling-based threshold construction methods, are proposed. Subsequently, simulated acceleration data from a multi degree-of-freedom system is generated to test and compare the efficiency of the existing and proposed algorithms. Data from laboratory experiments conducted on a truss and a large-scale bridge slab model are then used to further validate the damage detection methods and demonstrate the superior performance of proposed algorithms.

Combined Uncertainty and A-Posteriori Error Bound Estimates for General CFD Calculations: Theory and Software Implementation

NASA Technical Reports Server (NTRS)

Barth, Timothy J.

2014-01-01

This workshop presentation discusses the design and implementation of numerical methods for the quantification of statistical uncertainty, including a-posteriori error bounds, for output quantities computed using CFD methods. Hydrodynamic realizations often contain numerical error arising from finite-dimensional approximation (e.g. numerical methods using grids, basis functions, particles) and statistical uncertainty arising from incomplete information and/or statistical characterization of model parameters and random fields. The first task at hand is to derive formal error bounds for statistics given realizations containing finite-dimensional numerical error [1]. The error in computed output statistics contains contributions from both realization error and the error resulting from the calculation of statistics integrals using a numerical method. A second task is to devise computable a-posteriori error bounds by numerically approximating all terms arising in the error bound estimates. For the same reason that CFD calculations including error bounds but omitting uncertainty modeling are only of limited value, CFD calculations including uncertainty modeling but omitting error bounds are only of limited value. To gain maximum value from CFD calculations, a general software package for uncertainty quantification with quantified error bounds has been developed at NASA. The package provides implementations for a suite of numerical methods used in uncertainty quantification: Dense tensorization basis methods [3] and a subscale recovery variant [1] for non-smooth data, Sparse tensorization methods[2] utilizing node-nested hierarchies, Sampling methods[4] for high-dimensional random variable spaces.

An introduction to Bayesian statistics in health psychology.

PubMed

Depaoli, Sarah; Rus, Holly M; Clifton, James P; van de Schoot, Rens; Tiemensma, Jitske

2017-09-01

The aim of the current article is to provide a brief introduction to Bayesian statistics within the field of health psychology. Bayesian methods are increasing in prevalence in applied fields, and they have been shown in simulation research to improve the estimation accuracy of structural equation models, latent growth curve (and mixture) models, and hierarchical linear models. Likewise, Bayesian methods can be used with small sample sizes since they do not rely on large sample theory. In this article, we discuss several important components of Bayesian statistics as they relate to health-based inquiries. We discuss the incorporation and impact of prior knowledge into the estimation process and the different components of the analysis that should be reported in an article. We present an example implementing Bayesian estimation in the context of blood pressure changes after participants experienced an acute stressor. We conclude with final thoughts on the implementation of Bayesian statistics in health psychology, including suggestions for reviewing Bayesian manuscripts and grant proposals. We have also included an extensive amount of online supplementary material to complement the content presented here, including Bayesian examples using many different software programmes and an extensive sensitivity analysis examining the impact of priors.

ModelTest Server: a web-based tool for the statistical selection of models of nucleotide substitution online

PubMed Central

Posada, David

2006-01-01

ModelTest server is a web-based application for the selection of models of nucleotide substitution using the program ModelTest. The server takes as input a text file with likelihood scores for the set of candidate models. Models can be selected with hierarchical likelihood ratio tests, or with the Akaike or Bayesian information criteria. The output includes several statistics for the assessment of model selection uncertainty, for model averaging or to estimate the relative importance of model parameters. The server can be accessed at . PMID:16845102

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 Primer on the Statistical Modelling of Learning Curves in Health Professions Education

ERIC Educational Resources Information Center

Pusic, Martin V.; Boutis, Kathy; Pecaric, Martin R.; Savenkov, Oleksander; Beckstead, Jason W.; Jaber, Mohamad Y.

2017-01-01

Learning curves are a useful way of representing the rate of learning over time. Features include an index of baseline performance (y-intercept), the efficiency of learning over time (slope parameter) and the maximal theoretical performance achievable (upper asymptote). Each of these parameters can be statistically modelled on an individual and…

Binomial test statistics using Psi functions

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

Bowman, Kimiko o

2007-01-01

For the negative binomial model (probability generating function (p + 1 - pt){sup -k}) a logarithmic derivative is the Psi function difference {psi}(k + x) - {psi}(k); this and its derivatives lead to a test statistic to decide on the validity of a specified model. The test statistic uses a data base so there exists a comparison available between theory and application. Note that the test function is not dominated by outliers. Applications to (i) Fisher's tick data, (ii) accidents data, (iii) Weldon's dice data are included.

ICD-11 and DSM-5 personality trait domains capture categorical personality disorders: Finding a common ground.

PubMed

Bach, Bo; Sellbom, Martin; Skjernov, Mathias; Simonsen, Erik

2018-05-01

The five personality disorder trait domains in the proposed International Classification of Diseases, 11th edition and the Diagnostic and Statistical Manual of Mental Disorders, 5th edition are comparable in terms of Negative Affectivity, Detachment, Antagonism/Dissociality and Disinhibition. However, the International Classification of Diseases, 11th edition model includes a separate domain of Anankastia, whereas the Diagnostic and Statistical Manual of Mental Disorders, 5th edition model includes an additional domain of Psychoticism. This study examined associations of International Classification of Diseases, 11th edition and Diagnostic and Statistical Manual of Mental Disorders, 5th edition trait domains, simultaneously, with categorical personality disorders. Psychiatric outpatients ( N = 226) were administered the Structured Clinical Interview for DSM-IV Axis II Personality Disorders Interview and the Personality Inventory for DSM-5. International Classification of Diseases, 11th edition and Diagnostic and Statistical Manual of Mental Disorders, 5th edition trait domain scores were obtained using pertinent scoring algorithms for the Personality Inventory for DSM-5. Associations between categorical personality disorders and trait domains were examined using correlation and multiple regression analyses. Both the International Classification of Diseases, 11th edition and the Diagnostic and Statistical Manual of Mental Disorders, 5th edition domain models showed relevant continuity with categorical personality disorders and captured a substantial amount of their information. As expected, the International Classification of Diseases, 11th edition model was superior in capturing obsessive-compulsive personality disorder, whereas the Diagnostic and Statistical Manual of Mental Disorders, 5th edition model was superior in capturing schizotypal personality disorder. These preliminary findings suggest that little information is 'lost' in a transition to trait domain models and potentially adds to narrowing the gap between Diagnostic and Statistical Manual of Mental Disorders, 5th edition and the proposed International Classification of Diseases, 11th edition model. Accordingly, the International Classification of Diseases, 11th edition and Diagnostic and Statistical Manual of Mental Disorders, 5th edition domain models may be used to delineate one another as well as features of familiar categorical personality disorder types. A preliminary category-to-domain 'cross walk' is provided in the article.

A consistent framework for Horton regression statistics that leads to a modified Hack's law

USGS Publications Warehouse

Furey, P.R.; Troutman, B.M.

2008-01-01

A statistical framework is introduced that resolves important problems with the interpretation and use of traditional Horton regression statistics. The framework is based on a univariate regression model that leads to an alternative expression for Horton ratio, connects Horton regression statistics to distributional simple scaling, and improves the accuracy in estimating Horton plot parameters. The model is used to examine data for drainage area A and mainstream length L from two groups of basins located in different physiographic settings. Results show that confidence intervals for the Horton plot regression statistics are quite wide. Nonetheless, an analysis of covariance shows that regression intercepts, but not regression slopes, can be used to distinguish between basin groups. The univariate model is generalized to include n > 1 dependent variables. For the case where the dependent variables represent ln A and ln L, the generalized model performs somewhat better at distinguishing between basin groups than two separate univariate models. The generalized model leads to a modification of Hack's law where L depends on both A and Strahler order ??. Data show that ?? plays a statistically significant role in the modified Hack's law expression. ?? 2008 Elsevier B.V.

A modified F-test for evaluating model performance by including both experimental and simulation uncertainties

USDA-ARS?s Scientific Manuscript database

Experimental and simulation uncertainties have not been included in many of the statistics used in assessing agricultural model performance. The objectives of this study were to develop an F-test that can be used to evaluate model performance considering experimental and simulation uncertainties, an...

Towards accurate modelling of galaxy clustering on small scales: testing the standard ΛCDM + halo model

NASA Astrophysics Data System (ADS)

Sinha, Manodeep; Berlind, Andreas A.; McBride, Cameron K.; Scoccimarro, Roman; Piscionere, Jennifer A.; Wibking, Benjamin D.

2018-07-01

Interpreting the small-scale clustering of galaxies with halo models can elucidate the connection between galaxies and dark matter haloes. Unfortunately, the modelling is typically not sufficiently accurate for ruling out models statistically. It is thus difficult to use the information encoded in small scales to test cosmological models or probe subtle features of the galaxy-halo connection. In this paper, we attempt to push halo modelling into the `accurate' regime with a fully numerical mock-based methodology and careful treatment of statistical and systematic errors. With our forward-modelling approach, we can incorporate clustering statistics beyond the traditional two-point statistics. We use this modelling methodology to test the standard Λ cold dark matter (ΛCDM) + halo model against the clustering of Sloan Digital Sky Survey (SDSS) seventh data release (DR7) galaxies. Specifically, we use the projected correlation function, group multiplicity function, and galaxy number density as constraints. We find that while the model fits each statistic separately, it struggles to fit them simultaneously. Adding group statistics leads to a more stringent test of the model and significantly tighter constraints on model parameters. We explore the impact of varying the adopted halo definition and cosmological model and find that changing the cosmology makes a significant difference. The most successful model we tried (Planck cosmology with Mvir haloes) matches the clustering of low-luminosity galaxies, but exhibits a 2.3σ tension with the clustering of luminous galaxies, thus providing evidence that the `standard' halo model needs to be extended. This work opens the door to adding interesting freedom to the halo model and including additional clustering statistics as constraints.

SEPEM: A tool for statistical modeling the solar energetic particle environment

NASA Astrophysics Data System (ADS)

Crosby, Norma; Heynderickx, Daniel; Jiggens, Piers; Aran, Angels; Sanahuja, Blai; Truscott, Pete; Lei, Fan; Jacobs, Carla; Poedts, Stefaan; Gabriel, Stephen; Sandberg, Ingmar; Glover, Alexi; Hilgers, Alain

2015-07-01

Solar energetic particle (SEP) events are a serious radiation hazard for spacecraft as well as a severe health risk to humans traveling in space. Indeed, accurate modeling of the SEP environment constitutes a priority requirement for astrophysics and solar system missions and for human exploration in space. The European Space Agency's Solar Energetic Particle Environment Modelling (SEPEM) application server is a World Wide Web interface to a complete set of cross-calibrated data ranging from 1973 to 2013 as well as new SEP engineering models and tools. Both statistical and physical modeling techniques have been included, in order to cover the environment not only at 1 AU but also in the inner heliosphere ranging from 0.2 AU to 1.6 AU using a newly developed physics-based shock-and-particle model to simulate particle flux profiles of gradual SEP events. With SEPEM, SEP peak flux and integrated fluence statistics can be studied, as well as durations of high SEP flux periods. Furthermore, effects tools are also included to allow calculation of single event upset rate and radiation doses for a variety of engineering scenarios.

An operational GLS model for hydrologic regression

USGS Publications Warehouse

Tasker, Gary D.; Stedinger, J.R.

1989-01-01

Recent Monte Carlo studies have documented the value of generalized least squares (GLS) procedures to estimate empirical relationships between streamflow statistics and physiographic basin characteristics. This paper presents a number of extensions of the GLS method that deal with realities and complexities of regional hydrologic data sets that were not addressed in the simulation studies. These extensions include: (1) a more realistic model of the underlying model errors; (2) smoothed estimates of cross correlation of flows; (3) procedures for including historical flow data; (4) diagnostic statistics describing leverage and influence for GLS regression; and (5) the formulation of a mathematical program for evaluating future gaging activities. ?? 1989.

The construction and assessment of a statistical model for the prediction of protein assay data.

PubMed

Pittman, J; Sacks, J; Young, S Stanley

2002-01-01

The focus of this work is the development of a statistical model for a bioinformatics database whose distinctive structure makes model assessment an interesting and challenging problem. The key components of the statistical methodology, including a fast approximation to the singular value decomposition and the use of adaptive spline modeling and tree-based methods, are described, and preliminary results are presented. These results are shown to compare favorably to selected results achieved using comparitive methods. An attempt to determine the predictive ability of the model through the use of cross-validation experiments is discussed. In conclusion a synopsis of the results of these experiments and their implications for the analysis of bioinformatic databases in general is presented.

Strongly magnetized classical plasma models

NASA Technical Reports Server (NTRS)

Montgomery, D. C.

1972-01-01

The class of plasma processes for which the so-called Vlasov approximation is inadequate is investigated. Results from the equilibrium statistical mechanics of two-dimensional plasmas are derived. These results are independent of the presence of an external dc magnetic field. The nonequilibrium statistical mechanics of the electrostatic guiding-center plasma, a two-dimensional plasma model, is discussed. This model is then generalized to three dimensions. The guiding-center model is relaxed to include finite Larmor radius effects for a two-dimensional plasma.

Probabilistic Graphical Model Representation in Phylogenetics

PubMed Central

Höhna, Sebastian; Heath, Tracy A.; Boussau, Bastien; Landis, Michael J.; Ronquist, Fredrik; Huelsenbeck, John P.

2014-01-01

Recent years have seen a rapid expansion of the model space explored in statistical phylogenetics, emphasizing the need for new approaches to statistical model representation and software development. Clear communication and representation of the chosen model is crucial for: (i) reproducibility of an analysis, (ii) model development, and (iii) software design. Moreover, a unified, clear and understandable framework for model representation lowers the barrier for beginners and nonspecialists to grasp complex phylogenetic models, including their assumptions and parameter/variable dependencies. Graphical modeling is a unifying framework that has gained in popularity in the statistical literature in recent years. The core idea is to break complex models into conditionally independent distributions. The strength lies in the comprehensibility, flexibility, and adaptability of this formalism, and the large body of computational work based on it. Graphical models are well-suited to teach statistical models, to facilitate communication among phylogeneticists and in the development of generic software for simulation and statistical inference. Here, we provide an introduction to graphical models for phylogeneticists and extend the standard graphical model representation to the realm of phylogenetics. We introduce a new graphical model component, tree plates, to capture the changing structure of the subgraph corresponding to a phylogenetic tree. We describe a range of phylogenetic models using the graphical model framework and introduce modules to simplify the representation of standard components in large and complex models. Phylogenetic model graphs can be readily used in simulation, maximum likelihood inference, and Bayesian inference using, for example, Metropolis–Hastings or Gibbs sampling of the posterior distribution. [Computation; graphical models; inference; modularization; statistical phylogenetics; tree plate.] PMID:24951559

Comparisons of non-Gaussian statistical models in DNA methylation analysis.

PubMed

Ma, Zhanyu; Teschendorff, Andrew E; Yu, Hong; Taghia, Jalil; Guo, Jun

2014-06-16

As a key regulatory mechanism of gene expression, DNA methylation patterns are widely altered in many complex genetic diseases, including cancer. DNA methylation is naturally quantified by bounded support data; therefore, it is non-Gaussian distributed. In order to capture such properties, we introduce some non-Gaussian statistical models to perform dimension reduction on DNA methylation data. Afterwards, non-Gaussian statistical model-based unsupervised clustering strategies are applied to cluster the data. Comparisons and analysis of different dimension reduction strategies and unsupervised clustering methods are presented. Experimental results show that the non-Gaussian statistical model-based methods are superior to the conventional Gaussian distribution-based method. They are meaningful tools for DNA methylation analysis. Moreover, among several non-Gaussian methods, the one that captures the bounded nature of DNA methylation data reveals the best clustering performance.

Comparisons of Non-Gaussian Statistical Models in DNA Methylation Analysis

PubMed Central

Ma, Zhanyu; Teschendorff, Andrew E.; Yu, Hong; Taghia, Jalil; Guo, Jun

2014-01-01

As a key regulatory mechanism of gene expression, DNA methylation patterns are widely altered in many complex genetic diseases, including cancer. DNA methylation is naturally quantified by bounded support data; therefore, it is non-Gaussian distributed. In order to capture such properties, we introduce some non-Gaussian statistical models to perform dimension reduction on DNA methylation data. Afterwards, non-Gaussian statistical model-based unsupervised clustering strategies are applied to cluster the data. Comparisons and analysis of different dimension reduction strategies and unsupervised clustering methods are presented. Experimental results show that the non-Gaussian statistical model-based methods are superior to the conventional Gaussian distribution-based method. They are meaningful tools for DNA methylation analysis. Moreover, among several non-Gaussian methods, the one that captures the bounded nature of DNA methylation data reveals the best clustering performance. PMID:24937687

Right-Sizing Statistical Models for Longitudinal Data

PubMed Central

Wood, Phillip K.; Steinley, Douglas; Jackson, Kristina M.

2015-01-01

Arguments are proposed that researchers using longitudinal data should consider more and less complex statistical model alternatives to their initially chosen techniques in an effort to “right-size” the model to the data at hand. Such model comparisons may alert researchers who use poorly fitting overly parsimonious models to more complex better fitting alternatives, and, alternatively, may identify more parsimonious alternatives to overly complex (and perhaps empirically under-identified and/or less powerful) statistical models. A general framework is proposed for considering (often nested) relationships between a variety of psychometric and growth curve models. A three-step approach is proposed in which models are evaluated based on the number and patterning of variance components prior to selection of better-fitting growth models that explain both mean and variation/covariation patterns. The orthogonal, free-curve slope-intercept (FCSI) growth model is considered as a general model which includes, as special cases, many models including the Factor Mean model (FM, McArdle & Epstein, 1987), McDonald's (1967) linearly constrained factor model, Hierarchical Linear Models (HLM), Repeated Measures MANOVA, and the Linear Slope Intercept (LinearSI) Growth Model. The FCSI model, in turn, is nested within the Tuckerized factor model. The approach is illustrated by comparing alternative models in a longitudinal study of children's vocabulary and by comparison of several candidate parametric growth and chronometric models in a Monte Carlo study. PMID:26237507

Right-sizing statistical models for longitudinal data.

PubMed

Wood, Phillip K; Steinley, Douglas; Jackson, Kristina M

2015-12-01

Arguments are proposed that researchers using longitudinal data should consider more and less complex statistical model alternatives to their initially chosen techniques in an effort to "right-size" the model to the data at hand. Such model comparisons may alert researchers who use poorly fitting, overly parsimonious models to more complex, better-fitting alternatives and, alternatively, may identify more parsimonious alternatives to overly complex (and perhaps empirically underidentified and/or less powerful) statistical models. A general framework is proposed for considering (often nested) relationships between a variety of psychometric and growth curve models. A 3-step approach is proposed in which models are evaluated based on the number and patterning of variance components prior to selection of better-fitting growth models that explain both mean and variation-covariation patterns. The orthogonal free curve slope intercept (FCSI) growth model is considered a general model that includes, as special cases, many models, including the factor mean (FM) model (McArdle & Epstein, 1987), McDonald's (1967) linearly constrained factor model, hierarchical linear models (HLMs), repeated-measures multivariate analysis of variance (MANOVA), and the linear slope intercept (linearSI) growth model. The FCSI model, in turn, is nested within the Tuckerized factor model. The approach is illustrated by comparing alternative models in a longitudinal study of children's vocabulary and by comparing several candidate parametric growth and chronometric models in a Monte Carlo study. (c) 2015 APA, all rights reserved).

Analysis models for the estimation of oceanic fields

NASA Technical Reports Server (NTRS)

Carter, E. F.; Robinson, A. R.

1987-01-01

A general model for statistically optimal estimates is presented for dealing with scalar, vector and multivariate datasets. The method deals with anisotropic fields and treats space and time dependence equivalently. Problems addressed include the analysis, or the production of synoptic time series of regularly gridded fields from irregular and gappy datasets, and the estimate of fields by compositing observations from several different instruments and sampling schemes. Technical issues are discussed, including the convergence of statistical estimates, the choice of representation of the correlations, the influential domain of an observation, and the efficiency of numerical computations.

A Meta-Meta-Analysis: Empirical Review of Statistical Power, Type I Error Rates, Effect Sizes, and Model Selection of Meta-Analyses Published in Psychology

ERIC Educational Resources Information Center

Cafri, Guy; Kromrey, Jeffrey D.; Brannick, Michael T.

2010-01-01

This article uses meta-analyses published in "Psychological Bulletin" from 1995 to 2005 to describe meta-analyses in psychology, including examination of statistical power, Type I errors resulting from multiple comparisons, and model choice. Retrospective power estimates indicated that univariate categorical and continuous moderators, individual…

Linguistic Constraints on Statistical Word Segmentation: The Role of Consonants in Arabic and English

ERIC Educational Resources Information Center

Kastner, Itamar; Adriaans, Frans

2018-01-01

Statistical learning is often taken to lie at the heart of many cognitive tasks, including the acquisition of language. One particular task in which probabilistic models have achieved considerable success is the segmentation of speech into words. However, these models have mostly been tested against English data, and as a result little is known…

Seismic activity prediction using computational intelligence techniques in northern Pakistan

NASA Astrophysics Data System (ADS)

Asim, Khawaja M.; Awais, Muhammad; Martínez-Álvarez, F.; Iqbal, Talat

2017-10-01

Earthquake prediction study is carried out for the region of northern Pakistan. The prediction methodology includes interdisciplinary interaction of seismology and computational intelligence. Eight seismic parameters are computed based upon the past earthquakes. Predictive ability of these eight seismic parameters is evaluated in terms of information gain, which leads to the selection of six parameters to be used in prediction. Multiple computationally intelligent models have been developed for earthquake prediction using selected seismic parameters. These models include feed-forward neural network, recurrent neural network, random forest, multi layer perceptron, radial basis neural network, and support vector machine. The performance of every prediction model is evaluated and McNemar's statistical test is applied to observe the statistical significance of computational methodologies. Feed-forward neural network shows statistically significant predictions along with accuracy of 75% and positive predictive value of 78% in context of northern Pakistan.

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Singh, Vijay P.; Xia, Youlong

2018-03-01

Drought prediction is of critical importance to early warning for drought managements. This review provides a synthesis of drought prediction based on statistical, dynamical, and hybrid methods. Statistical drought prediction is achieved by modeling the relationship between drought indices of interest and a suite of potential predictors, including large-scale climate indices, local climate variables, and land initial conditions. Dynamical meteorological drought prediction relies on seasonal climate forecast from general circulation models (GCMs), which can be employed to drive hydrological models for agricultural and hydrological drought prediction with the predictability determined by both climate forcings and initial conditions. Challenges still exist in drought prediction at long lead time and under a changing environment resulting from natural and anthropogenic factors. Future research prospects to improve drought prediction include, but are not limited to, high-quality data assimilation, improved model development with key processes related to drought occurrence, optimal ensemble forecast to select or weight ensembles, and hybrid drought prediction to merge statistical and dynamical forecasts.

Fast, Statistical Model of Surface Roughness for Ion-Solid Interaction Simulations and Efficient Code Coupling

NASA Astrophysics Data System (ADS)

Drobny, Jon; Curreli, Davide; Ruzic, David; Lasa, Ane; Green, David; Canik, John; Younkin, Tim; Blondel, Sophie; Wirth, Brian

2017-10-01

Surface roughness greatly impacts material erosion, and thus plays an important role in Plasma-Surface Interactions. Developing strategies for efficiently introducing rough surfaces into ion-solid interaction codes will be an important step towards whole-device modeling of plasma devices and future fusion reactors such as ITER. Fractal TRIDYN (F-TRIDYN) is an upgraded version of the Monte Carlo, BCA program TRIDYN developed for this purpose that includes an explicit fractal model of surface roughness and extended input and output options for file-based code coupling. Code coupling with both plasma and material codes has been achieved and allows for multi-scale, whole-device modeling of plasma experiments. These code coupling results will be presented. F-TRIDYN has been further upgraded with an alternative, statistical model of surface roughness. The statistical model is significantly faster than and compares favorably to the fractal model. Additionally, the statistical model compares well to alternative computational surface roughness models and experiments. Theoretical links between the fractal and statistical models are made, and further connections to experimental measurements of surface roughness are explored. This work was supported by the PSI-SciDAC Project funded by the U.S. Department of Energy through contract DOE-DE-SC0008658.

Statistical modeling of natural backgrounds in hyperspectral LWIR data

NASA Astrophysics Data System (ADS)

Truslow, Eric; Manolakis, Dimitris; Cooley, Thomas; Meola, Joseph

2016-09-01

Hyperspectral sensors operating in the long wave infrared (LWIR) have a wealth of applications including remote material identification and rare target detection. While statistical models for modeling surface reflectance in visible and near-infrared regimes have been well studied, models for the temperature and emissivity in the LWIR have not been rigorously investigated. In this paper, we investigate modeling hyperspectral LWIR data using a statistical mixture model for the emissivity and surface temperature. Statistical models for the surface parameters can be used to simulate surface radiances and at-sensor radiance which drives the variability of measured radiance and ultimately the performance of signal processing algorithms. Thus, having models that adequately capture data variation is extremely important for studying performance trades. The purpose of this paper is twofold. First, we study the validity of this model using real hyperspectral data, and compare the relative variability of hyperspectral data in the LWIR and visible and near-infrared (VNIR) regimes. Second, we illustrate how materials that are easily distinguished in the VNIR, may be difficult to separate when imaged in the LWIR.

An empirical comparison of statistical tests for assessing the proportional hazards assumption of Cox's model.

PubMed

Ng'andu, N H

1997-03-30

In the analysis of survival data using the Cox proportional hazard (PH) model, it is important to verify that the explanatory variables analysed satisfy the proportional hazard assumption of the model. This paper presents results of a simulation study that compares five test statistics to check the proportional hazard assumption of Cox's model. The test statistics were evaluated under proportional hazards and the following types of departures from the proportional hazard assumption: increasing relative hazards; decreasing relative hazards; crossing hazards; diverging hazards, and non-monotonic hazards. The test statistics compared include those based on partitioning of failure time and those that do not require partitioning of failure time. The simulation results demonstrate that the time-dependent covariate test, the weighted residuals score test and the linear correlation test have equally good power for detection of non-proportionality in the varieties of non-proportional hazards studied. Using illustrative data from the literature, these test statistics performed similarly.

Classical Statistics and Statistical Learning in Imaging Neuroscience

PubMed Central

Bzdok, Danilo

2017-01-01

Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques. PMID:29056896

A stochastic model of particle dispersion in turbulent reacting gaseous environments

NASA Astrophysics Data System (ADS)

Sun, Guangyuan; Lignell, David; Hewson, John

2012-11-01

We are performing fundamental studies of dispersive transport and time-temperature histories of Lagrangian particles in turbulent reacting flows. The particle-flow statistics including the full particle temperature PDF are of interest. A challenge in modeling particle motions is the accurate prediction of fine-scale aerosol-fluid interactions. A computationally affordable stochastic modeling approach, one-dimensional turbulence (ODT), is a proven method that captures the full range of length and time scales, and provides detailed statistics of fine-scale turbulent-particle mixing and transport. Limited results of particle transport in ODT have been reported in non-reacting flow. Here, we extend ODT to particle transport in reacting flow. The results of particle transport in three flow configurations are presented: channel flow, homogeneous isotropic turbulence, and jet flames. We investigate the functional dependence of the statistics of particle-flow interactions including (1) parametric study with varying temperatures, Reynolds numbers, and particle Stokes numbers; (2) particle temperature histories and PDFs; (3) time scale and the sensitivity of initial and boundary conditions. Flow statistics are compared to both experimental measurements and DNS data.

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory.

PubMed

Lord, Dominique; Washington, Simon P; Ivan, John N

2005-01-01

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states-perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of "excess" zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to "excess" zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed-and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros.

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.

Representing Micro-Macro Linkages by Actor-Based Dynamic Network Models

PubMed Central

Snijders, Tom A.B.; Steglich, Christian E.G.

2014-01-01

Stochastic actor-based models for network dynamics have the primary aim of statistical inference about processes of network change, but may be regarded as a kind of agent-based models. Similar to many other agent-based models, they are based on local rules for actor behavior. Different from many other agent-based models, by including elements of generalized linear statistical models they aim to be realistic detailed representations of network dynamics in empirical data sets. Statistical parallels to micro-macro considerations can be found in the estimation of parameters determining local actor behavior from empirical data, and the assessment of goodness of fit from the correspondence with network-level descriptives. This article studies several network-level consequences of dynamic actor-based models applied to represent cross-sectional network data. Two examples illustrate how network-level characteristics can be obtained as emergent features implied by micro-specifications of actor-based models. PMID:25960578

Developing statistical wildlife habitat relationships for assessing cumulative effects of fuels treatments: Final Report for Joint Fire Science Program Project

Treesearch

Samuel A. Cushman; Kevin S. McKelvey

2006-01-01

The primary weakness in our current ability to evaluate future landscapes in terms of wildlife lies in the lack of quantitative models linking wildlife to forest stand conditions, including fuels treatments. This project focuses on 1) developing statistical wildlife habitat relationships models (WHR) utilizing Forest Inventory and Analysis (FIA) and National Vegetation...

Elastic Network Models For Biomolecular Dynamics: Theory and Application to Membrane Proteins and Viruses

NASA Astrophysics Data System (ADS)

Lezon, Timothy R.; Shrivastava, Indira H.; Yang, Zheng; Bahar, Ivet

The following sections are included: * Introduction * Theory and Assumptions * Statistical mechanical foundations * Anisotropic network models * Gaussian network model * Rigid block models * Treatment of perturbations * Langevin dynamics * Applications * Membrane proteins * Viruses * Conclusion * References

BACTERIAL MORTALITY DUE TO SOLAR RADIATION, COMPARING EXPERIMENTAL AND STATISTICAL EVIDENCE

EPA Science Inventory

Many researchers report that sunlight is a primary stressor of beach indicator bacteria. Some water quality models include code that quantifies the effect of radiation on bacterial decay. For example, the EPA Visual Plumes model includes two coliform and one enterococcus submodel...

Using spatiotemporal statistical models to estimate animal abundance and infer ecological dynamics from survey counts

USGS Publications Warehouse

Conn, Paul B.; Johnson, Devin S.; Ver Hoef, Jay M.; Hooten, Mevin B.; London, Joshua M.; Boveng, Peter L.

2015-01-01

Ecologists often fit models to survey data to estimate and explain variation in animal abundance. Such models typically require that animal density remains constant across the landscape where sampling is being conducted, a potentially problematic assumption for animals inhabiting dynamic landscapes or otherwise exhibiting considerable spatiotemporal variation in density. We review several concepts from the burgeoning literature on spatiotemporal statistical models, including the nature of the temporal structure (i.e., descriptive or dynamical) and strategies for dimension reduction to promote computational tractability. We also review several features as they specifically relate to abundance estimation, including boundary conditions, population closure, choice of link function, and extrapolation of predicted relationships to unsampled areas. We then compare a suite of novel and existing spatiotemporal hierarchical models for animal count data that permit animal density to vary over space and time, including formulations motivated by resource selection and allowing for closed populations. We gauge the relative performance (bias, precision, computational demands) of alternative spatiotemporal models when confronted with simulated and real data sets from dynamic animal populations. For the latter, we analyze spotted seal (Phoca largha) counts from an aerial survey of the Bering Sea where the quantity and quality of suitable habitat (sea ice) changed dramatically while surveys were being conducted. Simulation analyses suggested that multiple types of spatiotemporal models provide reasonable inference (low positive bias, high precision) about animal abundance, but have potential for overestimating precision. Analysis of spotted seal data indicated that several model formulations, including those based on a log-Gaussian Cox process, had a tendency to overestimate abundance. By contrast, a model that included a population closure assumption and a scale prior on total abundance produced estimates that largely conformed to our a priori expectation. Although care must be taken to tailor models to match the study population and survey data available, we argue that hierarchical spatiotemporal statistical models represent a powerful way forward for estimating abundance and explaining variation in the distribution of dynamical populations.

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.

Functional Status Outperforms Comorbidities as a Predictor of 30-Day Acute Care Readmissions in the Inpatient Rehabilitation Population.

PubMed

Shih, Shirley L; Zafonte, Ross; Bates, David W; Gerrard, Paul; Goldstein, Richard; Mix, Jacqueline; Niewczyk, Paulette; Greysen, S Ryan; Kazis, Lewis; Ryan, Colleen M; Schneider, Jeffrey C

2016-10-01

Functional status is associated with patient outcomes, but is rarely included in hospital readmission risk models. The objective of this study was to determine whether functional status is a better predictor of 30-day acute care readmission than traditionally investigated variables including demographics and comorbidities. Retrospective database analysis between 2002 and 2011. 1158 US inpatient rehabilitation facilities. 4,199,002 inpatient rehabilitation facility admissions comprising patients from 16 impairment groups within the Uniform Data System for Medical Rehabilitation database. Logistic regression models predicting 30-day readmission were developed based on age, gender, comorbidities (Elixhauser comorbidity index, Deyo-Charlson comorbidity index, and Medicare comorbidity tier system), and functional status [Functional Independence Measure (FIM)]. We hypothesized that (1) function-based models would outperform demographic- and comorbidity-based models and (2) the addition of demographic and comorbidity data would not significantly enhance function-based models. For each impairment group, Function Only Models were compared against Demographic-Comorbidity Models and Function Plus Models (Function-Demographic-Comorbidity Models). The primary outcome was 30-day readmission, and the primary measure of model performance was the c-statistic. All-cause 30-day readmission rate from inpatient rehabilitation facilities to acute care hospitals was 9.87%. C-statistics for the Function Only Models were 0.64 to 0.70. For all 16 impairment groups, the Function Only Model demonstrated better c-statistics than the Demographic-Comorbidity Models (c-statistic difference: 0.03-0.12). The best-performing Function Plus Models exhibited negligible improvements in model performance compared to Function Only Models, with c-statistic improvements of only 0.01 to 0.05. Readmissions are currently used as a marker of hospital performance, with recent financial penalties to hospitals for excessive readmissions. Function-based readmission models outperform models based only on demographics and comorbidities. Readmission risk models would benefit from the inclusion of functional status as a primary predictor. Copyright © 2016 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.

Incorporating GIS and remote sensing for census population disaggregation

NASA Astrophysics Data System (ADS)

Wu, Shuo-Sheng'derek'

Census data are the primary source of demographic data for a variety of researches and applications. For confidentiality issues and administrative purposes, census data are usually released to the public by aggregated areal units. In the United States, the smallest census unit is census blocks. Due to data aggregation, users of census data may have problems in visualizing population distribution within census blocks and estimating population counts for areas not coinciding with census block boundaries. The main purpose of this study is to develop methodology for estimating sub-block areal populations and assessing the estimation errors. The City of Austin, Texas was used as a case study area. Based on tax parcel boundaries and parcel attributes derived from ancillary GIS and remote sensing data, detailed urban land use classes were first classified using a per-field approach. After that, statistical models by land use classes were built to infer population density from other predictor variables, including four census demographic statistics (the Hispanic percentage, the married percentage, the unemployment rate, and per capita income) and three physical variables derived from remote sensing images and building footprints vector data (a landscape heterogeneity statistics, a building pattern statistics, and a building volume statistics). In addition to statistical models, deterministic models were proposed to directly infer populations from building volumes and three housing statistics, including the average space per housing unit, the housing unit occupancy rate, and the average household size. After population models were derived or proposed, how well the models predict populations for another set of sample blocks was assessed. The results show that deterministic models were more accurate than statistical models. Further, by simulating the base unit for modeling from aggregating blocks, I assessed how well the deterministic models estimate sub-unit-level populations. I also assessed the aggregation effects and the resealing effects on sub-unit estimates. Lastly, from another set of mixed-land-use sample blocks, a mixed-land-use model was derived and compared with a residential-land-use model. The results of per-field land use classification are satisfactory with a Kappa accuracy statistics of 0.747. Model Assessments by land use show that population estimates for multi-family land use areas have higher errors than those for single-family land use areas, and population estimates for mixed land use areas have higher errors than those for residential land use areas. The assessments of sub-unit estimates using a simulation approach indicate that smaller areas show higher estimation errors, estimation errors do not relate to the base unit size, and resealing improves all levels of sub-unit estimates.

The use of imputed sibling genotypes in sibship-based association analysis: on modeling alternatives, power and model misspecification.

PubMed

Minică, Camelia C; Dolan, Conor V; Hottenga, Jouke-Jan; Willemsen, Gonneke; Vink, Jacqueline M; Boomsma, Dorret I

2013-05-01

When phenotypic, but no genotypic data are available for relatives of participants in genetic association studies, previous research has shown that family-based imputed genotypes can boost the statistical power when included in such studies. Here, using simulations, we compared the performance of two statistical approaches suitable to model imputed genotype data: the mixture approach, which involves the full distribution of the imputed genotypes and the dosage approach, where the mean of the conditional distribution features as the imputed genotype. Simulations were run by varying sibship size, size of the phenotypic correlations among siblings, imputation accuracy and minor allele frequency of the causal SNP. Furthermore, as imputing sibling data and extending the model to include sibships of size two or greater requires modeling the familial covariance matrix, we inquired whether model misspecification affects power. Finally, the results obtained via simulations were empirically verified in two datasets with continuous phenotype data (height) and with a dichotomous phenotype (smoking initiation). Across the settings considered, the mixture and the dosage approach are equally powerful and both produce unbiased parameter estimates. In addition, the likelihood-ratio test in the linear mixed model appears to be robust to the considered misspecification in the background covariance structure, given low to moderate phenotypic correlations among siblings. Empirical results show that the inclusion in association analysis of imputed sibling genotypes does not always result in larger test statistic. The actual test statistic may drop in value due to small effect sizes. That is, if the power benefit is small, that the change in distribution of the test statistic under the alternative is relatively small, the probability is greater of obtaining a smaller test statistic. As the genetic effects are typically hypothesized to be small, in practice, the decision on whether family-based imputation could be used as a means to increase power should be informed by prior power calculations and by the consideration of the background correlation.

Sample Size and Statistical Conclusions from Tests of Fit to the Rasch Model According to the Rasch Unidimensional Measurement Model (Rumm) Program in Health Outcome Measurement.

PubMed

Hagell, Peter; Westergren, Albert

Sample size is a major factor in statistical null hypothesis testing, which is the basis for many approaches to testing Rasch model fit. Few sample size recommendations for testing fit to the Rasch model concern the Rasch Unidimensional Measurement Models (RUMM) software, which features chi-square and ANOVA/F-ratio based fit statistics, including Bonferroni and algebraic sample size adjustments. This paper explores the occurrence of Type I errors with RUMM fit statistics, and the effects of algebraic sample size adjustments. Data with simulated Rasch model fitting 25-item dichotomous scales and sample sizes ranging from N = 50 to N = 2500 were analysed with and without algebraically adjusted sample sizes. Results suggest the occurrence of Type I errors with N less then or equal to 500, and that Bonferroni correction as well as downward algebraic sample size adjustment are useful to avoid such errors, whereas upward adjustment of smaller samples falsely signal misfit. Our observations suggest that sample sizes around N = 250 to N = 500 may provide a good balance for the statistical interpretation of the RUMM fit statistics studied here with respect to Type I errors and under the assumption of Rasch model fit within the examined frame of reference (i.e., about 25 item parameters well targeted to the sample).

Parameter Estimation and Model Validation of Nonlinear Dynamical Networks

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

Abarbanel, Henry; Gill, Philip

In the performance period of this work under a DOE contract, the co-PIs, Philip Gill and Henry Abarbanel, developed new methods for statistical data assimilation for problems of DOE interest, including geophysical and biological problems. This included numerical optimization algorithms for variational principles, new parallel processing Monte Carlo routines for performing the path integrals of statistical data assimilation. These results have been summarized in the monograph: “Predicting the Future: Completing Models of Observed Complex Systems” by Henry Abarbanel, published by Spring-Verlag in June 2013. Additional results and details have appeared in the peer reviewed literature.

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

NASA Technical Reports Server (NTRS)

Makowski, David; Asseng, Senthold; Ewert, Frank; Bassu, Simona; Durand, Jean-Louis; Martre, Pierre; Adam, Myriam; Aggarwal, Pramod K.; Angulo, Carlos; Baron, Chritian;

Statistical Models of Fracture Relevant to Nuclear-Grade Graphite: Review and Recommendations

NASA Technical Reports Server (NTRS)

Nemeth, Noel N.; Bratton, Robert L.

2011-01-01

The nuclear-grade (low-impurity) graphite needed for the fuel element and moderator material for next-generation (Gen IV) reactors displays large scatter in strength and a nonlinear stress-strain response from damage accumulation. This response can be characterized as quasi-brittle. In this expanded review, relevant statistical failure models for various brittle and quasi-brittle material systems are discussed with regard to strength distribution, size effect, multiaxial strength, and damage accumulation. This includes descriptions of the Weibull, Batdorf, and Burchell models as well as models that describe the strength response of composite materials, which involves distributed damage. Results from lattice simulations are included for a physics-based description of material breakdown. Consideration is given to the predicted transition between brittle and quasi-brittle damage behavior versus the density of damage (level of disorder) within the material system. The literature indicates that weakest-link-based failure modeling approaches appear to be reasonably robust in that they can be applied to materials that display distributed damage, provided that the level of disorder in the material is not too large. The Weibull distribution is argued to be the most appropriate statistical distribution to model the stochastic-strength response of graphite.

Reduction of chemical reaction models

NASA Technical Reports Server (NTRS)

Frenklach, Michael

1991-01-01

An attempt is made to reconcile the different terminologies pertaining to reduction of chemical reaction models. The approaches considered include global modeling, response modeling, detailed reduction, chemical lumping, and statistical lumping. The advantages and drawbacks of each of these methods are pointed out.

Biological evolution and statistical physics

NASA Astrophysics Data System (ADS)

Drossel, Barbara

2001-03-01

This review is an introduction to theoretical models and mathematical calculations for biological evolution, aimed at physicists. The methods in the field are naturally very similar to those used in statistical physics, although the majority of publications have appeared in biology journals. The review has three parts, which can be read independently. The first part deals with evolution in fitness landscapes and includes Fisher's theorem, adaptive walks, quasispecies models, effects of finite population sizes, and neutral evolution. The second part studies models of coevolution, including evolutionary game theory, kin selection, group selection, sexual selection, speciation, and coevolution of hosts and parasites. The third part discusses models for networks of interacting species and their extinction avalanches. Throughout the review, attention is paid to giving the necessary biological information, and to pointing out the assumptions underlying the models, and their limits of validity.

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.

A cross-national analysis of how economic inequality predicts biodiversity loss.

PubMed

Holland, Tim G; Peterson, Garry D; Gonzalez, Andrew

2009-10-01

We used socioeconomic models that included economic inequality to predict biodiversity loss, measured as the proportion of threatened plant and vertebrate species, across 50 countries. Our main goal was to evaluate whether economic inequality, measured as the Gini index of income distribution, improved the explanatory power of our statistical models. We compared four models that included the following: only population density, economic footprint (i.e., the size of the economy relative to the country area), economic footprint and income inequality (Gini index), and an index of environmental governance. We also tested the environmental Kuznets curve hypothesis, but it was not supported by the data. Statistical comparisons of the models revealed that the model including both economic footprint and inequality was the best predictor of threatened species. It significantly outperformed population density alone and the environmental governance model according to the Akaike information criterion. Inequality was a significant predictor of biodiversity loss and significantly improved the fit of our models. These results confirm that socioeconomic inequality is an important factor to consider when predicting rates of anthropogenic biodiversity loss.

QR-STEM: Energy and Environment as a Context for Improving QR and STEM Understandings of 6-12 Grade Teachers II. The Quantitative Reasoning

NASA Astrophysics Data System (ADS)

Mayes, R.; Lyford, M. E.; Myers, J. D.

2009-12-01

The Quantitative Reasoning in STEM (QR STEM) project is a state level Mathematics and Science Partnership Project (MSP) with a focus on the mathematics and statistics that underlies the understanding of complex global scientific issues. This session is a companion session to the QR STEM: The Science presentation. The focus of this session is the quantitative reasoning aspects of the project. As students move from understandings that range from local to global in perspective on issues of energy and environment, there is a significant increase in the need for mathematical and statistical conceptual understanding. These understandings must be accessible to the students within the scientific context, requiring the special understandings that are endemic within quantitative reasoning. The QR STEM project brings together interdisciplinary teams of higher education faculty and middle/high school teachers to explore complex problems in energy and environment. The disciplines include life sciences, physics, chemistry, earth science, statistics, and mathematics. These interdisciplinary teams develop open ended performance tasks to implement in the classroom, based on scientific concepts that underpin energy and environment. Quantitative reasoning is broken down into three components: Quantitative Literacy, Quantitative Interpretation, and Quantitative Modeling. Quantitative Literacy is composed of arithmetic concepts such as proportional reasoning, numeracy, and descriptive statistics. Quantitative Interpretation includes algebraic and geometric concepts that underlie the ability to interpret a model of natural phenomena which is provided for the student. This model may be a table, graph, or equation from which the student is to make predictions or identify trends, or from which they would use statistics to explore correlations or patterns in data. Quantitative modeling is the ability to develop the model from data, including the ability to test hypothesis using statistical procedures. We use the term model very broadly, so it includes visual models such as box models, as well as best fit equation models and hypothesis testing. One of the powerful outcomes of the project is the conversation which takes place between science teachers and mathematics teachers. First they realize that though they are teaching concepts that cross their disciplines, the barrier of scientific language within their subjects restricts students from applying the concepts across subjects. Second the mathematics teachers discover the context of science as a means of providing real world situations that engage students in the utility of mathematics as a tool for solving problems. Third the science teachers discover the barrier to understanding science that is presented by poor quantitative reasoning ability. Finally the students are engaged in exploring energy and environment in a manner which exposes the importance of seeing a problem from multiple interdisciplinary perspectives. The outcome is a democratic citizen capable of making informed decisions, and perhaps a future scientist.

Hierarchical modeling and inference in ecology: The analysis of data from populations, metapopulations and communities

USGS Publications Warehouse

Royle, J. Andrew; Dorazio, Robert M.

2008-01-01

A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics.

Simple Statistics: - Summarized!

ERIC Educational Resources Information Center

Blai, Boris, Jr.

Statistics are an essential tool for making proper judgement decisions. It is concerned with probability distribution models, testing of hypotheses, significance tests and other means of determining the correctness of deductions and the most likely outcome of decisions. Measures of central tendency include the mean, median and mode. A second…

An order statistics approach to the halo model for galaxies

NASA Astrophysics Data System (ADS)

Paul, Niladri; Paranjape, Aseem; Sheth, Ravi K.

2017-04-01

We use the halo model to explore the implications of assuming that galaxy luminosities in groups are randomly drawn from an underlying luminosity function. We show that even the simplest of such order statistics models - one in which this luminosity function p(L) is universal - naturally produces a number of features associated with previous analyses based on the 'central plus Poisson satellites' hypothesis. These include the monotonic relation of mean central luminosity with halo mass, the lognormal distribution around this mean and the tight relation between the central and satellite mass scales. In stark contrast to observations of galaxy clustering; however, this model predicts no luminosity dependence of large-scale clustering. We then show that an extended version of this model, based on the order statistics of a halo mass dependent luminosity function p(L|m), is in much better agreement with the clustering data as well as satellite luminosities, but systematically underpredicts central luminosities. This brings into focus the idea that central galaxies constitute a distinct population that is affected by different physical processes than are the satellites. We model this physical difference as a statistical brightening of the central luminosities, over and above the order statistics prediction. The magnitude gap between the brightest and second brightest group galaxy is predicted as a by-product, and is also in good agreement with observations. We propose that this order statistics framework provides a useful language in which to compare the halo model for galaxies with more physically motivated galaxy formation models.

High-temperature behavior of a deformed Fermi gas obeying interpolating statistics.

PubMed

Algin, Abdullah; Senay, Mustafa

2012-04-01

An outstanding idea originally introduced by Greenberg is to investigate whether there is equivalence between intermediate statistics, which may be different from anyonic statistics, and q-deformed particle algebra. Also, a model to be studied for addressing such an idea could possibly provide us some new consequences about the interactions of particles as well as their internal structures. Motivated mainly by this idea, in this work, we consider a q-deformed Fermi gas model whose statistical properties enable us to effectively study interpolating statistics. Starting with a generalized Fermi-Dirac distribution function, we derive several thermostatistical functions of a gas of these deformed fermions in the thermodynamical limit. We study the high-temperature behavior of the system by analyzing the effects of q deformation on the most important thermostatistical characteristics of the system such as the entropy, specific heat, and equation of state. It is shown that such a deformed fermion model in two and three spatial dimensions exhibits the interpolating statistics in a specific interval of the model deformation parameter 0 < q < 1. In particular, for two and three spatial dimensions, it is found from the behavior of the third virial coefficient of the model that the deformation parameter q interpolates completely between attractive and repulsive systems, including the free boson and fermion cases. From the results obtained in this work, we conclude that such a model could provide much physical insight into some interacting theories of fermions, and could be useful to further study the particle systems with intermediate statistics.

Modeling longitudinal data, I: principles of multivariate analysis.

PubMed

Ravani, Pietro; Barrett, Brendan; Parfrey, Patrick

2009-01-01

Statistical models are used to study the relationship between exposure and disease while accounting for the potential role of other factors' impact on outcomes. This adjustment is useful to obtain unbiased estimates of true effects or to predict future outcomes. Statistical models include a systematic component and an error component. The systematic component explains the variability of the response variable as a function of the predictors and is summarized in the effect estimates (model coefficients). The error element of the model represents the variability in the data unexplained by the model and is used to build measures of precision around the point estimates (confidence intervals).

A statistical simulation model for field testing of non-target organisms in environmental risk assessment of genetically modified plants.

PubMed

Goedhart, Paul W; van der Voet, Hilko; Baldacchino, Ferdinando; Arpaia, Salvatore

2014-04-01

Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment. A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart. Part of the environmental risk assessment is a comparative field experiment in which the effect on non-target organisms is compared. Statistical analysis of such trials come in two flavors: difference testing and equivalence testing. It is important to know the statistical properties of these, for example, the power to detect environmental change of a given magnitude, before the start of an experiment. Such prospective power analysis can best be studied by means of a statistical simulation model. This paper describes a general framework for simulating data typically encountered in environmental risk assessment of genetically modified plants. The simulation model, available as Supplementary Material, can be used to generate count data having different statistical distributions possibly with excess-zeros. In addition the model employs completely randomized or randomized block experiments, can be used to simulate single or multiple trials across environments, enables genotype by environment interaction by adding random variety effects, and finally includes repeated measures in time following a constant, linear or quadratic pattern in time possibly with some form of autocorrelation. The model also allows to add a set of reference varieties to the GM plants and its comparator to assess the natural variation which can then be used to set limits of concern for equivalence testing. The different count distributions are described in some detail and some examples of how to use the simulation model to study various aspects, including a prospective power analysis, are provided.

A statistical simulation model for field testing of non-target organisms in environmental risk assessment of genetically modified plants

PubMed Central

Goedhart, Paul W; van der Voet, Hilko; Baldacchino, Ferdinando; Arpaia, Salvatore

2014-01-01

Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment. A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart. Part of the environmental risk assessment is a comparative field experiment in which the effect on non-target organisms is compared. Statistical analysis of such trials come in two flavors: difference testing and equivalence testing. It is important to know the statistical properties of these, for example, the power to detect environmental change of a given magnitude, before the start of an experiment. Such prospective power analysis can best be studied by means of a statistical simulation model. This paper describes a general framework for simulating data typically encountered in environmental risk assessment of genetically modified plants. The simulation model, available as Supplementary Material, can be used to generate count data having different statistical distributions possibly with excess-zeros. In addition the model employs completely randomized or randomized block experiments, can be used to simulate single or multiple trials across environments, enables genotype by environment interaction by adding random variety effects, and finally includes repeated measures in time following a constant, linear or quadratic pattern in time possibly with some form of autocorrelation. The model also allows to add a set of reference varieties to the GM plants and its comparator to assess the natural variation which can then be used to set limits of concern for equivalence testing. The different count distributions are described in some detail and some examples of how to use the simulation model to study various aspects, including a prospective power analysis, are provided. PMID:24834325

A Management Information System Model for Program Management. Ph.D. Thesis - Oklahoma State Univ.; [Computerized Systems Analysis

NASA Technical Reports Server (NTRS)

Shipman, D. L.

1972-01-01

The development of a model to simulate the information system of a program management type of organization is reported. The model statistically determines the following parameters: type of messages, destinations, delivery durations, type processing, processing durations, communication channels, outgoing messages, and priorites. The total management information system of the program management organization is considered, including formal and informal information flows and both facilities and equipment. The model is written in General Purpose System Simulation 2 computer programming language for use on the Univac 1108, Executive 8 computer. The model is simulated on a daily basis and collects queue and resource utilization statistics for each decision point. The statistics are then used by management to evaluate proposed resource allocations, to evaluate proposed changes to the system, and to identify potential problem areas. The model employs both empirical and theoretical distributions which are adjusted to simulate the information flow being studied.

A New Scoring System to Predict the Risk for High-risk Adenoma and Comparison of Existing Risk Calculators.

PubMed

Murchie, Brent; Tandon, Kanwarpreet; Hakim, Seifeldin; Shah, Kinchit; O'Rourke, Colin; Castro, Fernando J

2017-04-01

Colorectal cancer (CRC) screening guidelines likely over-generalizes CRC risk, 35% of Americans are not up to date with screening, and there is growing incidence of CRC in younger patients. We developed a practical prediction model for high-risk colon adenomas in an average-risk population, including an expanded definition of high-risk polyps (≥3 nonadvanced adenomas), exposing higher than average-risk patients. We also compared results with previously created calculators. Patients aged 40 to 59 years, undergoing first-time average-risk screening or diagnostic colonoscopies were evaluated. Risk calculators for advanced adenomas and high-risk adenomas were created based on age, body mass index, sex, race, and smoking history. Previously established calculators with similar risk factors were selected for comparison of concordance statistic (c-statistic) and external validation. A total of 5063 patients were included. Advanced adenomas, and high-risk adenomas were seen in 5.7% and 7.4% of the patient population, respectively. The c-statistic for our calculator was 0.639 for the prediction of advanced adenomas, and 0.650 for high-risk adenomas. When applied to our population, all previous models had lower c-statistic results although one performed similarly. Our model compares favorably to previously established prediction models. Age and body mass index were used as continuous variables, likely improving the c-statistic. It also reports absolute predictive probabilities of advanced and high-risk polyps, allowing for more individualized risk assessment of CRC.

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

Statistical modeling of temperature, humidity and wind fields in the atmospheric boundary layer over the Siberian region

NASA Astrophysics Data System (ADS)

Lomakina, N. Ya.

2017-11-01

The work presents the results of the applied climatic division of the Siberian region into districts based on the methodology of objective classification of the atmospheric boundary layer climates by the "temperature-moisture-wind" complex realized with using the method of principal components and the special similarity criteria of average profiles and the eigen values of correlation matrices. On the territory of Siberia, it was identified 14 homogeneous regions for winter season and 10 regions were revealed for summer. The local statistical models were constructed for each region. These include vertical profiles of mean values, mean square deviations, and matrices of interlevel correlation of temperature, specific humidity, zonal and meridional wind velocity. The advantage of the obtained local statistical models over the regional models is shown.

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.

A statistical model including age to predict passenger postures in the rear seats of automobiles.

PubMed

Park, Jangwoon; Ebert, Sheila M; Reed, Matthew P; Hallman, Jason J

2016-06-01

Few statistical models of rear seat passenger posture have been published, and none has taken into account the effects of occupant age. This study developed new statistical models for predicting passenger postures in the rear seats of automobiles. Postures of 89 adults with a wide range of age and body size were measured in a laboratory mock-up in seven seat configurations. Posture-prediction models for female and male passengers were separately developed by stepwise regression using age, body dimensions, seat configurations and two-way interactions as potential predictors. Passenger posture was significantly associated with age and the effects of other two-way interaction variables depended on age. A set of posture-prediction models are presented for women and men, and the prediction results are compared with previously published models. This study is the first study of passenger posture to include a large cohort of older passengers and the first to report a significant effect of age for adults. The presented models can be used to position computational and physical human models for vehicle design and assessment. Practitioner Summary: The significant effects of age, body dimensions and seat configuration on rear seat passenger posture were identified. The models can be used to accurately position computational human models or crash test dummies for older passengers in known rear seat configurations.

Performance of Reclassification Statistics in Comparing Risk Prediction Models

PubMed Central

Paynter, Nina P.

2012-01-01

Concerns have been raised about the use of traditional measures of model fit in evaluating risk prediction models for clinical use, and reclassification tables have been suggested as an alternative means of assessing the clinical utility of a model. Several measures based on the table have been proposed, including the reclassification calibration (RC) statistic, the net reclassification improvement (NRI), and the integrated discrimination improvement (IDI), but the performance of these in practical settings has not been fully examined. We used simulations to estimate the type I error and power for these statistics in a number of scenarios, as well as the impact of the number and type of categories, when adding a new marker to an established or reference model. The type I error was found to be reasonable in most settings, and power was highest for the IDI, which was similar to the test of association. The relative power of the RC statistic, a test of calibration, and the NRI, a test of discrimination, varied depending on the model assumptions. These tools provide unique but complementary information. PMID:21294152

Statistical framework for evaluation of climate model simulations by use of climate proxy data from the last millennium - Part 1: Theory

NASA Astrophysics Data System (ADS)

Sundberg, R.; Moberg, A.; Hind, A.

2012-08-01

A statistical framework for comparing the output of ensemble simulations from global climate models with networks of climate proxy and instrumental records has been developed, focusing on near-surface temperatures for the last millennium. This framework includes the formulation of a joint statistical model for proxy data, instrumental data and simulation data, which is used to optimize a quadratic distance measure for ranking climate model simulations. An essential underlying assumption is that the simulations and the proxy/instrumental series have a shared component of variability that is due to temporal changes in external forcing, such as volcanic aerosol load, solar irradiance or greenhouse gas concentrations. Two statistical tests have been formulated. Firstly, a preliminary test establishes whether a significant temporal correlation exists between instrumental/proxy and simulation data. Secondly, the distance measure is expressed in the form of a test statistic of whether a forced simulation is closer to the instrumental/proxy series than unforced simulations. The proposed framework allows any number of proxy locations to be used jointly, with different seasons, record lengths and statistical precision. The goal is to objectively rank several competing climate model simulations (e.g. with alternative model parameterizations or alternative forcing histories) by means of their goodness of fit to the unobservable true past climate variations, as estimated from noisy proxy data and instrumental observations.

Probabilistic models for reactive behaviour in heterogeneous condensed phase media

NASA Astrophysics Data System (ADS)

Baer, M. R.; Gartling, D. K.; DesJardin, P. E.

2012-02-01

This work presents statistically-based models to describe reactive behaviour in heterogeneous energetic materials. Mesoscale effects are incorporated in continuum-level reactive flow descriptions using probability density functions (pdfs) that are associated with thermodynamic and mechanical states. A generalised approach is presented that includes multimaterial behaviour by treating the volume fraction as a random kinematic variable. Model simplifications are then sought to reduce the complexity of the description without compromising the statistical approach. Reactive behaviour is first considered for non-deformable media having a random temperature field as an initial state. A pdf transport relationship is derived and an approximate moment approach is incorporated in finite element analysis to model an example application whereby a heated fragment impacts a reactive heterogeneous material which leads to a delayed cook-off event. Modelling is then extended to include deformation effects associated with shock loading of a heterogeneous medium whereby random variables of strain, strain-rate and temperature are considered. A demonstrative mesoscale simulation of a non-ideal explosive is discussed that illustrates the joint statistical nature of the strain and temperature fields during shock loading to motivate the probabilistic approach. This modelling is derived in a Lagrangian framework that can be incorporated in continuum-level shock physics analysis. Future work will consider particle-based methods for a numerical implementation of this modelling approach.

Statistics of acoustic emissions and stress drops during granular shearing using a stick-slip fiber bundle mode

NASA Astrophysics Data System (ADS)

Cohen, D.; Michlmayr, G.; Or, D.

2012-04-01

Shearing of dense granular materials appears in many engineering and Earth sciences applications. Under a constant strain rate, the shearing stress at steady state oscillates with slow rises followed by rapid drops that are linked to the build up and failure of force chains. Experiments indicate that these drops display exponential statistics. Measurements of acoustic emissions during shearing indicates that the energy liberated by failure of these force chains has power-law statistics. Representing force chains as fibers, we use a stick-slip fiber bundle model to obtain analytical solutions of the statistical distribution of stress drops and failure energy. In the model, fibers stretch, fail, and regain strength during deformation. Fibers have Weibull-distributed threshold strengths with either quenched and annealed disorder. The shape of the distribution for drops and energy obtained from the model are similar to those measured during shearing experiments. This simple model may be useful to identify failure events linked to force chain failures. Future generalizations of the model that include different types of fiber failure may also allow identification of different types of granular failures that have distinct statistical acoustic emission signatures.

Characterizing Uncertainty and Variability in PBPK Models ...

EPA Pesticide Factsheets

Mode-of-action based risk and safety assessments can rely upon tissue dosimetry estimates in animals and humans obtained from physiologically-based pharmacokinetic (PBPK) modeling. However, risk assessment also increasingly requires characterization of uncertainty and variability; such characterization for PBPK model predictions represents a continuing challenge to both modelers and users. Current practices show significant progress in specifying deterministic biological models and the non-deterministic (often statistical) models, estimating their parameters using diverse data sets from multiple sources, and using them to make predictions and characterize uncertainty and variability. The International Workshop on Uncertainty and Variability in PBPK Models, held Oct 31-Nov 2, 2006, sought to identify the state-of-the-science in this area and recommend priorities for research and changes in practice and implementation. For the short term, these include: (1) multidisciplinary teams to integrate deterministic and non-deterministic/statistical models; (2) broader use of sensitivity analyses, including for structural and global (rather than local) parameter changes; and (3) enhanced transparency and reproducibility through more complete documentation of the model structure(s) and parameter values, the results of sensitivity and other analyses, and supporting, discrepant, or excluded data. Longer-term needs include: (1) theoretic and practical methodological impro

Bridging the gap between habitat-modeling research and bird conservation with dynamic landscape and population models

Treesearch

Frank R., III Thompson

2009-01-01

Habitat models are widely used in bird conservation planning to assess current habitat or populations and to evaluate management alternatives. These models include species-habitat matrix or database models, habitat suitability models, and statistical models that predict abundance. While extremely useful, these approaches have some limitations.

Noise limitations in optical linear algebra processors.

PubMed

Batsell, S G; Jong, T L; Walkup, J F; Krile, T F

1990-05-10

A general statistical noise model is presented for optical linear algebra processors. A statistical analysis which includes device noise, the multiplication process, and the addition operation is undertaken. We focus on those processes which are architecturally independent. Finally, experimental results which verify the analytical predictions are also presented.

Statistical assessment of the learning curves of health technologies.

PubMed

Ramsay, C R; Grant, A M; Wallace, S A; Garthwaite, P H; Monk, A F; Russell, I T

2001-01-01

(1) To describe systematically studies that directly assessed the learning curve effect of health technologies. (2) Systematically to identify 'novel' statistical techniques applied to learning curve data in other fields, such as psychology and manufacturing. (3) To test these statistical techniques in data sets from studies of varying designs to assess health technologies in which learning curve effects are known to exist. METHODS - STUDY SELECTION (HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW): For a study to be included, it had to include a formal analysis of the learning curve of a health technology using a graphical, tabular or statistical technique. METHODS - STUDY SELECTION (NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH): For a study to be included, it had to include a formal assessment of a learning curve using a statistical technique that had not been identified in the previous search. METHODS - DATA SOURCES: Six clinical and 16 non-clinical biomedical databases were searched. A limited amount of handsearching and scanning of reference lists was also undertaken. METHODS - DATA EXTRACTION (HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW): A number of study characteristics were abstracted from the papers such as study design, study size, number of operators and the statistical method used. METHODS - DATA EXTRACTION (NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH): The new statistical techniques identified were categorised into four subgroups of increasing complexity: exploratory data analysis; simple series data analysis; complex data structure analysis, generic techniques. METHODS - TESTING OF STATISTICAL METHODS: Some of the statistical methods identified in the systematic searches for single (simple) operator series data and for multiple (complex) operator series data were illustrated and explored using three data sets. The first was a case series of 190 consecutive laparoscopic fundoplication procedures performed by a single surgeon; the second was a case series of consecutive laparoscopic cholecystectomy procedures performed by ten surgeons; the third was randomised trial data derived from the laparoscopic procedure arm of a multicentre trial of groin hernia repair, supplemented by data from non-randomised operations performed during the trial. RESULTS - HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW: Of 4571 abstracts identified, 272 (6%) were later included in the study after review of the full paper. Some 51% of studies assessed a surgical minimal access technique and 95% were case series. The statistical method used most often (60%) was splitting the data into consecutive parts (such as halves or thirds), with only 14% attempting a more formal statistical analysis. The reporting of the studies was poor, with 31% giving no details of data collection methods. RESULTS - NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH: Of 9431 abstracts assessed, 115 (1%) were deemed appropriate for further investigation and, of these, 18 were included in the study. All of the methods for complex data sets were identified in the non-clinical literature. These were discriminant analysis, two-stage estimation of learning rates, generalised estimating equations, multilevel models, latent curve models, time series models and stochastic parameter models. In addition, eight new shapes of learning curves were identified. RESULTS - TESTING OF STATISTICAL METHODS: No one particular shape of learning curve performed significantly better than another. The performance of 'operation time' as a proxy for learning differed between the three procedures. Multilevel modelling using the laparoscopic cholecystectomy data demonstrated and measured surgeon-specific and confounding effects. The inclusion of non-randomised cases, despite the possible limitations of the method, enhanced the interpretation of learning effects. CONCLUSIONS - HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW: The statistical methods used for assessing learning effects in health technology assessment have been crude and the reporting of studies poor. CONCLUSIONS - NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH: A number of statistical methods for assessing learning effects were identified that had not hitherto been used in health technology assessment. There was a hierarchy of methods for the identification and measurement of learning, and the more sophisticated methods for both have had little if any use in health technology assessment. This demonstrated the value of considering fields outside clinical research when addressing methodological issues in health technology assessment. CONCLUSIONS - TESTING OF STATISTICAL METHODS: It has been demonstrated that the portfolio of techniques identified can enhance investigations of learning curve effects. (ABSTRACT TRUNCATED)

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.

Ground-Based Telescope Parametric Cost Model

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes

2004-01-01

A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis, The model includes both engineering and performance parameters. While diameter continues to be the dominant cost driver, other significant factors include primary mirror radius of curvature and diffraction limited wavelength. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e.. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter are derived. This analysis indicates that recent mirror technology advances have indeed reduced the historical telescope cost curve.

Power-up: A Reanalysis of 'Power Failure' in Neuroscience Using Mixture Modeling

PubMed Central

Wood, John

2017-01-01

Recently, evidence for endemically low statistical power has cast neuroscience findings into doubt. If low statistical power plagues neuroscience, then this reduces confidence in the reported effects. However, if statistical power is not uniformly low, then such blanket mistrust might not be warranted. Here, we provide a different perspective on this issue, analyzing data from an influential study reporting a median power of 21% across 49 meta-analyses (Button et al., 2013). We demonstrate, using Gaussian mixture modeling, that the sample of 730 studies included in that analysis comprises several subcomponents so the use of a single summary statistic is insufficient to characterize the nature of the distribution. We find that statistical power is extremely low for studies included in meta-analyses that reported a null result and that it varies substantially across subfields of neuroscience, with particularly low power in candidate gene association studies. Therefore, whereas power in neuroscience remains a critical issue, the notion that studies are systematically underpowered is not the full story: low power is far from a universal problem. SIGNIFICANCE STATEMENT Recently, researchers across the biomedical and psychological sciences have become concerned with the reliability of results. One marker for reliability is statistical power: the probability of finding a statistically significant result given that the effect exists. Previous evidence suggests that statistical power is low across the field of neuroscience. Our results present a more comprehensive picture of statistical power in neuroscience: on average, studies are indeed underpowered—some very seriously so—but many studies show acceptable or even exemplary statistical power. We show that this heterogeneity in statistical power is common across most subfields in neuroscience. This new, more nuanced picture of statistical power in neuroscience could affect not only scientific understanding, but potentially policy and funding decisions for neuroscience research. PMID:28706080

Statistical Learning is Related to Early Literacy-Related Skills

PubMed Central

Spencer, Mercedes; Kaschak, Michael P.; Jones, John L.; Lonigan, Christopher J.

2015-01-01

It has been demonstrated that statistical learning, or the ability to use statistical information to learn the structure of one’s environment, plays a role in young children’s acquisition of linguistic knowledge. Although most research on statistical learning has focused on language acquisition processes, such as the segmentation of words from fluent speech and the learning of syntactic structure, some recent studies have explored the extent to which individual differences in statistical learning are related to literacy-relevant knowledge and skills. The present study extends on this literature by investigating the relations between two measures of statistical learning and multiple measures of skills that are critical to the development of literacy—oral language, vocabulary knowledge, and phonological processing—within a single model. Our sample included a total of 553 typically developing children from prekindergarten through second grade. Structural equation modeling revealed that statistical learning accounted for a unique portion of the variance in these literacy-related skills. Practical implications for instruction and assessment are discussed. PMID:26478658

Modeling the spatial distribution of landslide-prone colluvium and shallow groundwater on hillslopes of Seattle, WA

USGS Publications Warehouse

Schulz, W.H.; Lidke, D.J.; Godt, J.W.

2008-01-01

Landslides in partially saturated colluvium on Seattle, WA, hillslopes have resulted in property damage and human casualties. We developed statistical models of colluvium and shallow-groundwater distributions to aid landslide hazard assessments. The models were developed using a geographic information system, digital geologic maps, digital topography, subsurface exploration results, the groundwater flow modeling software VS2DI and regression analyses. Input to the colluvium model includes slope, distance to a hillslope-crest escarpment, and escarpment slope and height. We developed different statistical relations for thickness of colluvium on four landforms. Groundwater model input includes colluvium basal slope and distance from the Fraser aquifer. This distance was used to estimate hydraulic conductivity based on the assumption that addition of finer-grained material from down-section would result in lower conductivity. Colluvial groundwater is perched so we estimated its saturated thickness. We used VS2DI to establish relations between saturated thickness and the hydraulic conductivity and basal slope of the colluvium. We developed different statistical relations for three groundwater flow regimes. All model results were validated using observational data that were excluded from calibration. Eighty percent of colluvium thickness predictions were within 25% of observed values and 88% of saturated thickness predictions were within 20% of observed values. The models are based on conditions common to many areas, so our method can provide accurate results for similar regions; relations in our statistical models require calibration for new regions. Our results suggest that Seattle landslides occur in native deposits and colluvium, ultimately in response to surface-water erosion of hillstope toes. Regional groundwater conditions do not appear to strongly affect the general distribution of Seattle landslides; historical landslides were equally dispersed within and outside of the area potentially affected by regional groundwater conditions.

Use of Selected Goodness-of-Fit Statistics to Assess the Accuracy of a Model of Henry Hagg Lake, Oregon

NASA Astrophysics Data System (ADS)

Rounds, S. A.; Sullivan, A. B.

2004-12-01

Assessing a model's ability to reproduce field data is a critical step in the modeling process. For any model, some method of determining goodness-of-fit to measured data is needed to aid in calibration and to evaluate model performance. Visualizations and graphical comparisons of model output are an excellent way to begin that assessment. At some point, however, model performance must be quantified. Goodness-of-fit statistics, including the mean error (ME), mean absolute error (MAE), root mean square error, and coefficient of determination, typically are used to measure model accuracy. Statistical tools such as the sign test or Wilcoxon test can be used to test for model bias. The runs test can detect phase errors in simulated time series. Each statistic is useful, but each has its limitations. None provides a complete quantification of model accuracy. In this study, a suite of goodness-of-fit statistics was applied to a model of Henry Hagg Lake in northwest Oregon. Hagg Lake is a man-made reservoir on Scoggins Creek, a tributary to the Tualatin River. Located on the west side of the Portland metropolitan area, the Tualatin Basin is home to more than 450,000 people. Stored water in Hagg Lake helps to meet the agricultural and municipal water needs of that population. Future water demands have caused water managers to plan for a potential expansion of Hagg Lake, doubling its storage to roughly 115,000 acre-feet. A model of the lake was constructed to evaluate the lake's water quality and estimate how that quality might change after raising the dam. The laterally averaged, two-dimensional, U.S. Army Corps of Engineers model CE-QUAL-W2 was used to construct the Hagg Lake model. Calibrated for the years 2000 and 2001 and confirmed with data from 2002 and 2003, modeled parameters included water temperature, ammonia, nitrate, phosphorus, algae, zooplankton, and dissolved oxygen. Several goodness-of-fit statistics were used to quantify model accuracy and bias. Model performance was judged to be excellent for water temperature (annual ME: -0.22 to 0.05 ° C; annual MAE: 0.62 to 0.68 ° C) and dissolved oxygen (annual ME: -0.28 to 0.18 mg/L; annual MAE: 0.43 to 0.92 mg/L), showing that the model is sufficiently accurate for future water resources planning and management.

Functional constraints on tooth morphology in carnivorous mammals

PubMed Central

2012-01-01

Background The range of potential morphologies resulting from evolution is limited by complex interacting processes, ranging from development to function. Quantifying these interactions is important for understanding adaptation and convergent evolution. Using three-dimensional reconstructions of carnivoran and dasyuromorph tooth rows, we compared statistical models of the relationship between tooth row shape and the opposing tooth row, a static feature, as well as measures of mandibular motion during chewing (occlusion), which are kinetic features. This is a new approach to quantifying functional integration because we use measures of movement and displacement, such as the amount the mandible translates laterally during occlusion, as opposed to conventional morphological measures, such as mandible length and geometric landmarks. By sampling two distantly related groups of ecologically similar mammals, we study carnivorous mammals in general rather than a specific group of mammals. Results Statistical model comparisons demonstrate that the best performing models always include some measure of mandibular motion, indicating that functional and statistical models of tooth shape as purely a function of the opposing tooth row are too simple and that increased model complexity provides a better understanding of tooth form. The predictors of the best performing models always included the opposing tooth row shape and a relative linear measure of mandibular motion. Conclusions Our results provide quantitative support of long-standing hypotheses of tooth row shape as being influenced by mandibular motion in addition to the opposing tooth row. Additionally, this study illustrates the utility and necessity of including kinetic features in analyses of morphological integration. PMID:22899809

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.

Exploring Contextual Models in Chemical Patent Search

NASA Astrophysics Data System (ADS)

Urbain, Jay; Frieder, Ophir

We explore the development of probabilistic retrieval models for integrating term statistics with entity search using multiple levels of document context to improve the performance of chemical patent search. A distributed indexing model was developed to enable efficient named entity search and aggregation of term statistics at multiple levels of patent structure including individual words, sentences, claims, descriptions, abstracts, and titles. The system can be scaled to an arbitrary number of compute instances in a cloud computing environment to support concurrent indexing and query processing operations on large patent collections.

J-adaptive estimation with estimated noise statistics

NASA Technical Reports Server (NTRS)

Jazwinski, A. H.; Hipkins, C.

1973-01-01

The J-adaptive sequential estimator is extended to include simultaneous estimation of the noise statistics in a model for system dynamics. This extension completely automates the estimator, eliminating the requirement of an analyst in the loop. Simulations in satellite orbit determination demonstrate the efficacy of the sequential estimation algorithm.

'Chain pooling' model selection as developed for the statistical analysis of a rotor burst protection experiment

NASA Technical Reports Server (NTRS)

Holms, A. G.

1977-01-01

A statistical decision procedure called chain pooling had been developed for model selection in fitting the results of a two-level fixed-effects full or fractional factorial experiment not having replication. The basic strategy included the use of one nominal level of significance for a preliminary test and a second nominal level of significance for the final test. The subject has been reexamined from the point of view of using as many as three successive statistical model deletion procedures in fitting the results of a single experiment. The investigation consisted of random number studies intended to simulate the results of a proposed aircraft turbine-engine rotor-burst-protection experiment. As a conservative approach, population model coefficients were chosen to represent a saturated 2 to the 4th power experiment with a distribution of parameter values unfavorable to the decision procedures. Three model selection strategies were developed.

Artificial neural network study on organ-targeting peptides

NASA Astrophysics Data System (ADS)

Jung, Eunkyoung; Kim, Junhyoung; Choi, Seung-Hoon; Kim, Minkyoung; Rhee, Hokyoung; Shin, Jae-Min; Choi, Kihang; Kang, Sang-Kee; Lee, Nam Kyung; Choi, Yun-Jaie; Jung, Dong Hyun

2010-01-01

We report a new approach to studying organ targeting of peptides on the basis of peptide sequence information. The positive control data sets consist of organ-targeting peptide sequences identified by the peroral phage-display technique for four organs, and the negative control data are prepared from random sequences. The capacity of our models to make appropriate predictions is validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC) curve (the ROC score). VHSE descriptor produces statistically significant training models and the models with simple neural network architectures show slightly greater predictive power than those with complex ones. The training and test set statistics indicate that our models could discriminate between organ-targeting and random sequences. We anticipate that our models will be applicable to the selection of organ-targeting peptides for generating peptide drugs or peptidomimetics.

Quantifying uncertainty in climate change science through empirical information theory.

PubMed

Majda, Andrew J; Gershgorin, Boris

2010-08-24

Quantifying the uncertainty for the present climate and the predictions of climate change in the suite of imperfect Atmosphere Ocean Science (AOS) computer models is a central issue in climate change science. Here, a systematic approach to these issues with firm mathematical underpinning is developed through empirical information theory. An information metric to quantify AOS model errors in the climate is proposed here which incorporates both coarse-grained mean model errors as well as covariance ratios in a transformation invariant fashion. The subtle behavior of model errors with this information metric is quantified in an instructive statistically exactly solvable test model with direct relevance to climate change science including the prototype behavior of tracer gases such as CO(2). Formulas for identifying the most sensitive climate change directions using statistics of the present climate or an AOS model approximation are developed here; these formulas just involve finding the eigenvector associated with the largest eigenvalue of a quadratic form computed through suitable unperturbed climate statistics. These climate change concepts are illustrated on a statistically exactly solvable one-dimensional stochastic model with relevance for low frequency variability of the atmosphere. Viable algorithms for implementation of these concepts are discussed throughout the paper.

Probability distributions of molecular observables computed from Markov models. II. Uncertainties in observables and their time-evolution

NASA Astrophysics Data System (ADS)

Chodera, John D.; Noé, Frank

2010-09-01

Discrete-state Markov (or master equation) models provide a useful simplified representation for characterizing the long-time statistical evolution of biomolecules in a manner that allows direct comparison with experiments as well as the elucidation of mechanistic pathways for an inherently stochastic process. A vital part of meaningful comparison with experiment is the characterization of the statistical uncertainty in the predicted experimental measurement, which may take the form of an equilibrium measurement of some spectroscopic signal, the time-evolution of this signal following a perturbation, or the observation of some statistic (such as the correlation function) of the equilibrium dynamics of a single molecule. Without meaningful error bars (which arise from both approximation and statistical error), there is no way to determine whether the deviations between model and experiment are statistically meaningful. Previous work has demonstrated that a Bayesian method that enforces microscopic reversibility can be used to characterize the statistical component of correlated uncertainties in state-to-state transition probabilities (and functions thereof) for a model inferred from molecular simulation data. Here, we extend this approach to include the uncertainty in observables that are functions of molecular conformation (such as surrogate spectroscopic signals) characterizing each state, permitting the full statistical uncertainty in computed spectroscopic experiments to be assessed. We test the approach in a simple model system to demonstrate that the computed uncertainties provide a useful indicator of statistical variation, and then apply it to the computation of the fluorescence autocorrelation function measured for a dye-labeled peptide previously studied by both experiment and simulation.

Empirical-statistical downscaling of reanalysis data to high-resolution air temperature and specific humidity above a glacier surface (Cordillera Blanca, Peru)

NASA Astrophysics Data System (ADS)

Hofer, Marlis; MöLg, Thomas; Marzeion, Ben; Kaser, Georg

2010-06-01

Recently initiated observation networks in the Cordillera Blanca (Peru) provide temporally high-resolution, yet short-term, atmospheric data. The aim of this study is to extend the existing time series into the past. We present an empirical-statistical downscaling (ESD) model that links 6-hourly National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data to air temperature and specific humidity, measured at the tropical glacier Artesonraju (northern Cordillera Blanca). The ESD modeling procedure includes combined empirical orthogonal function and multiple regression analyses and a double cross-validation scheme for model evaluation. Apart from the selection of predictor fields, the modeling procedure is automated and does not include subjective choices. We assess the ESD model sensitivity to the predictor choice using both single-field and mixed-field predictors. Statistical transfer functions are derived individually for different months and times of day. The forecast skill largely depends on month and time of day, ranging from 0 to 0.8. The mixed-field predictors perform better than the single-field predictors. The ESD model shows added value, at all time scales, against simpler reference models (e.g., the direct use of reanalysis grid point values). The ESD model forecast 1960-2008 clearly reflects interannual variability related to the El Niño/Southern Oscillation but is sensitive to the chosen predictor type.

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.

Grain-Size Based Additivity Models for Scaling Multi-rate Uranyl Surface Complexation in Subsurface Sediments

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

Zhang, Xiaoying; Liu, Chongxuan; Hu, Bill X.

This study statistically analyzed a grain-size based additivity model that has been proposed to scale reaction rates and parameters from laboratory to field. The additivity model assumed that reaction properties in a sediment including surface area, reactive site concentration, reaction rate, and extent can be predicted from field-scale grain size distribution by linearly adding reaction properties for individual grain size fractions. This study focused on the statistical analysis of the additivity model with respect to reaction rate constants using multi-rate uranyl (U(VI)) surface complexation reactions in a contaminated sediment as an example. Experimental data of rate-limited U(VI) desorption in amore » stirred flow-cell reactor were used to estimate the statistical properties of multi-rate parameters for individual grain size fractions. The statistical properties of the rate constants for the individual grain size fractions were then used to analyze the statistical properties of the additivity model to predict rate-limited U(VI) desorption in the composite sediment, and to evaluate the relative importance of individual grain size fractions to the overall U(VI) desorption. The result indicated that the additivity model provided a good prediction of the U(VI) desorption in the composite sediment. However, the rate constants were not directly scalable using the additivity model, and U(VI) desorption in individual grain size fractions have to be simulated in order to apply the additivity model. An approximate additivity model for directly scaling rate constants was subsequently proposed and evaluated. The result found that the approximate model provided a good prediction of the experimental results within statistical uncertainty. This study also found that a gravel size fraction (2-8mm), which is often ignored in modeling U(VI) sorption and desorption, is statistically significant to the U(VI) desorption in the sediment.« less

Regression modeling of ground-water flow

USGS Publications Warehouse

Cooley, R.L.; Naff, R.L.

1985-01-01

Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)

qFeature

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

2015-09-14

This package contains statistical routines for extracting features from multivariate time-series data which can then be used for subsequent multivariate statistical analysis to identify patterns and anomalous behavior. It calculates local linear or quadratic regression model fits to moving windows for each series and then summarizes the model coefficients across user-defined time intervals for each series. These methods are domain agnostic-but they have been successfully applied to a variety of domains, including commercial aviation and electric power grid data.

A Model for Math Modeling

ERIC Educational Resources Information Center

Lin, Tony; Erfan, Sasan

2016-01-01

Mathematical modeling is an open-ended research subject where no definite answers exist for any problem. Math modeling enables thinking outside the box to connect different fields of studies together including statistics, algebra, calculus, matrices, programming and scientific writing. As an integral part of society, it is the foundation for many…

Evaluation of Model Fit in Cognitive Diagnosis Models

ERIC Educational Resources Information Center

Hu, Jinxiang; Miller, M. David; Huggins-Manley, Anne Corinne; Chen, Yi-Hsin

2016-01-01

Cognitive diagnosis models (CDMs) estimate student ability profiles using latent attributes. Model fit to the data needs to be ascertained in order to determine whether inferences from CDMs are valid. This study investigated the usefulness of some popular model fit statistics to detect CDM fit including relative fit indices (AIC, BIC, and CAIC),…

Development and validation of a risk calculator predicting exercise-induced ventricular arrhythmia in patients with cardiovascular disease.

PubMed

Hermes, Ilarraza-Lomelí; Marianna, García-Saldivia; Jessica, Rojano-Castillo; Carlos, Barrera-Ramírez; Rafael, Chávez-Domínguez; María Dolores, Rius-Suárez; Pedro, Iturralde

2016-10-01

Mortality due to cardiovascular disease is often associated with ventricular arrhythmias. Nowadays, patients with cardiovascular disease are more encouraged to take part in physical training programs. Nevertheless, high-intensity exercise is associated to a higher risk for sudden death, even in apparently healthy people. During an exercise testing (ET), health care professionals provide patients, in a controlled scenario, an intense physiological stimulus that could precipitate cardiac arrhythmia in high risk individuals. There is still no clinical or statistical tool to predict this incidence. The aim of this study was to develop a statistical model to predict the incidence of exercise-induced potentially life-threatening ventricular arrhythmia (PLVA) during high intensity exercise. 6415 patients underwent a symptom-limited ET with a Balke ramp protocol. A multivariate logistic regression model where the primary outcome was PLVA was performed. Incidence of PLVA was 548 cases (8.5%). After a bivariate model, thirty one clinical or ergometric variables were statistically associated with PLVA and were included in the regression model. In the multivariate model, 13 of these variables were found to be statistically significant. A regression model (G) with a X(2) of 283.987 and a p<0.001, was constructed. Significant variables included: heart failure, antiarrhythmic drugs, myocardial lower-VD, age and use of digoxin, nitrates, among others. This study allows clinicians to identify patients at risk of ventricular tachycardia or couplets during exercise, and to take preventive measures or appropriate supervision. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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.

Multivariate Strategies in Functional Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Hansen, Lars Kai

2007-01-01

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.

Interactive classification and content-based retrieval of tissue images

NASA Astrophysics Data System (ADS)

Aksoy, Selim; Marchisio, Giovanni B.; Tusk, Carsten; Koperski, Krzysztof

2002-11-01

We describe a system for interactive classification and retrieval of microscopic tissue images. Our system models tissues in pixel, region and image levels. Pixel level features are generated using unsupervised clustering of color and texture values. Region level features include shape information and statistics of pixel level feature values. Image level features include statistics and spatial relationships of regions. To reduce the gap between low-level features and high-level expert knowledge, we define the concept of prototype regions. The system learns the prototype regions in an image collection using model-based clustering and density estimation. Different tissue types are modeled using spatial relationships of these regions. Spatial relationships are represented by fuzzy membership functions. The system automatically selects significant relationships from training data and builds models which can also be updated using user relevance feedback. A Bayesian framework is used to classify tissues based on these models. Preliminary experiments show that the spatial relationship models we developed provide a flexible and powerful framework for classification and retrieval of tissue images.

Characterizing uncertainty and variability in physiologically based pharmacokinetic models: state of the science and needs for research and implementation.

PubMed

Barton, Hugh A; Chiu, Weihsueh A; Setzer, R Woodrow; Andersen, Melvin E; Bailer, A John; Bois, Frédéric Y; Dewoskin, Robert S; Hays, Sean; Johanson, Gunnar; Jones, Nancy; Loizou, George; Macphail, Robert C; Portier, Christopher J; Spendiff, Martin; Tan, Yu-Mei

2007-10-01

Physiologically based pharmacokinetic (PBPK) models are used in mode-of-action based risk and safety assessments to estimate internal dosimetry in animals and humans. When used in risk assessment, these models can provide a basis for extrapolating between species, doses, and exposure routes or for justifying nondefault values for uncertainty factors. Characterization of uncertainty and variability is increasingly recognized as important for risk assessment; this represents a continuing challenge for both PBPK modelers and users. Current practices show significant progress in specifying deterministic biological models and nondeterministic (often statistical) models, estimating parameters using diverse data sets from multiple sources, using them to make predictions, and characterizing uncertainty and variability of model parameters and predictions. The International Workshop on Uncertainty and Variability in PBPK Models, held 31 Oct-2 Nov 2006, identified the state-of-the-science, needed changes in practice and implementation, and research priorities. For the short term, these include (1) multidisciplinary teams to integrate deterministic and nondeterministic/statistical models; (2) broader use of sensitivity analyses, including for structural and global (rather than local) parameter changes; and (3) enhanced transparency and reproducibility through improved documentation of model structure(s), parameter values, sensitivity and other analyses, and supporting, discrepant, or excluded data. Longer-term needs include (1) theoretical and practical methodological improvements for nondeterministic/statistical modeling; (2) better methods for evaluating alternative model structures; (3) peer-reviewed databases of parameters and covariates, and their distributions; (4) expanded coverage of PBPK models across chemicals with different properties; and (5) training and reference materials, such as cases studies, bibliographies/glossaries, model repositories, and enhanced software. The multidisciplinary dialogue initiated by this Workshop will foster the collaboration, research, data collection, and training necessary to make characterizing uncertainty and variability a standard practice in PBPK modeling and risk assessment.

Statistical Model of Dynamic Markers of the Alzheimer's Pathological Cascade.

PubMed

Balsis, Steve; Geraci, Lisa; Benge, Jared; Lowe, Deborah A; Choudhury, Tabina K; Tirso, Robert; Doody, Rachelle S

2018-05-05

Alzheimer's disease (AD) is a progressive disease reflected in markers across assessment modalities, including neuroimaging, cognitive testing, and evaluation of adaptive function. Identifying a single continuum of decline across assessment modalities in a single sample is statistically challenging because of the multivariate nature of the data. To address this challenge, we implemented advanced statistical analyses designed specifically to model complex data across a single continuum. We analyzed data from the Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 1,056), focusing on indicators from the assessments of magnetic resonance imaging (MRI) volume, fluorodeoxyglucose positron emission tomography (FDG-PET) metabolic activity, cognitive performance, and adaptive function. Item response theory was used to identify the continuum of decline. Then, through a process of statistical scaling, indicators across all modalities were linked to that continuum and analyzed. Findings revealed that measures of MRI volume, FDG-PET metabolic activity, and adaptive function added measurement precision beyond that provided by cognitive measures, particularly in the relatively mild range of disease severity. More specifically, MRI volume, and FDG-PET metabolic activity become compromised in the very mild range of severity, followed by cognitive performance and finally adaptive function. Our statistically derived models of the AD pathological cascade are consistent with existing theoretical models.

TRACX2: a connectionist autoencoder using graded chunks to model infant visual statistical learning.

PubMed

Mareschal, Denis; French, Robert M

2017-01-05

Even newborn infants are able to extract structure from a stream of sensory inputs; yet how this is achieved remains largely a mystery. We present a connectionist autoencoder model, TRACX2, that learns to extract sequence structure by gradually constructing chunks, storing these chunks in a distributed manner across its synaptic weights and recognizing these chunks when they re-occur in the input stream. Chunks are graded rather than all-or-nothing in nature. As chunks are learnt their component parts become more and more tightly bound together. TRACX2 successfully models the data from five experiments from the infant visual statistical learning literature, including tasks involving forward and backward transitional probabilities, low-salience embedded chunk items, part-sequences and illusory items. The model also captures performance differences across ages through the tuning of a single-learning rate parameter. These results suggest that infant statistical learning is underpinned by the same domain-general learning mechanism that operates in auditory statistical learning and, potentially, in adult artificial grammar learning.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

TRACX2: a connectionist autoencoder using graded chunks to model infant visual statistical learning

PubMed Central

French, Robert M.

2017-01-01

Even newborn infants are able to extract structure from a stream of sensory inputs; yet how this is achieved remains largely a mystery. We present a connectionist autoencoder model, TRACX2, that learns to extract sequence structure by gradually constructing chunks, storing these chunks in a distributed manner across its synaptic weights and recognizing these chunks when they re-occur in the input stream. Chunks are graded rather than all-or-nothing in nature. As chunks are learnt their component parts become more and more tightly bound together. TRACX2 successfully models the data from five experiments from the infant visual statistical learning literature, including tasks involving forward and backward transitional probabilities, low-salience embedded chunk items, part-sequences and illusory items. The model also captures performance differences across ages through the tuning of a single-learning rate parameter. These results suggest that infant statistical learning is underpinned by the same domain-general learning mechanism that operates in auditory statistical learning and, potentially, in adult artificial grammar learning. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872375

Implications of the methodological choices for hydrologic portrayals of climate change over the contiguous United States: Statistically downscaled forcing data and hydrologic models

USGS Publications Warehouse

Mizukami, Naoki; Clark, Martyn P.; Gutmann, Ethan D.; Mendoza, Pablo A.; Newman, Andrew J.; Nijssen, Bart; Livneh, Ben; Hay, Lauren E.; Arnold, Jeffrey R.; Brekke, Levi D.

2016-01-01

Continental-domain assessments of climate change impacts on water resources typically rely on statistically downscaled climate model outputs to force hydrologic models at a finer spatial resolution. This study examines the effects of four statistical downscaling methods [bias-corrected constructed analog (BCCA), bias-corrected spatial disaggregation applied at daily (BCSDd) and monthly scales (BCSDm), and asynchronous regression (AR)] on retrospective hydrologic simulations using three hydrologic models with their default parameters (the Community Land Model, version 4.0; the Variable Infiltration Capacity model, version 4.1.2; and the Precipitation–Runoff Modeling System, version 3.0.4) over the contiguous United States (CONUS). Biases of hydrologic simulations forced by statistically downscaled climate data relative to the simulation with observation-based gridded data are presented. Each statistical downscaling method produces different meteorological portrayals including precipitation amount, wet-day frequency, and the energy input (i.e., shortwave radiation), and their interplay affects estimations of precipitation partitioning between evapotranspiration and runoff, extreme runoff, and hydrologic states (i.e., snow and soil moisture). The analyses show that BCCA underestimates annual precipitation by as much as −250 mm, leading to unreasonable hydrologic portrayals over the CONUS for all models. Although the other three statistical downscaling methods produce a comparable precipitation bias ranging from −10 to 8 mm across the CONUS, BCSDd severely overestimates the wet-day fraction by up to 0.25, leading to different precipitation partitioning compared to the simulations with other downscaled data. Overall, the choice of downscaling method contributes to less spread in runoff estimates (by a factor of 1.5–3) than the choice of hydrologic model with use of the default parameters if BCCA is excluded.

Comment on "Social Contagion, Adolescent Sexual Behavior, and Pregnancy: A Nonlinear Dynamic EMOSA Model."

ERIC Educational Resources Information Center

Stoolmiller, Mike

1998-01-01

Examines the Rodgers, Rowe, and Buster (1998) epidemic model of the onset of social activities for adolescent sexuality. Maintains that its strengths include its theoretical potential to generate new hypotheses for further testing at the individual level. Asserts that its limitations include the lack of a well-developed statistical framework and…

Trans-dimensional matched-field geoacoustic inversion with hierarchical error models and interacting Markov chains.

PubMed

Dettmer, Jan; Dosso, Stan E

2012-10-01

This paper develops a trans-dimensional approach to matched-field geoacoustic inversion, including interacting Markov chains to improve efficiency and an autoregressive model to account for correlated errors. The trans-dimensional approach and hierarchical seabed model allows inversion without assuming any particular parametrization by relaxing model specification to a range of plausible seabed models (e.g., in this case, the number of sediment layers is an unknown parameter). Data errors are addressed by sampling statistical error-distribution parameters, including correlated errors (covariance), by applying a hierarchical autoregressive error model. The well-known difficulty of low acceptance rates for trans-dimensional jumps is addressed with interacting Markov chains, resulting in a substantial increase in efficiency. The trans-dimensional seabed model and the hierarchical error model relax the degree of prior assumptions required in the inversion, resulting in substantially improved (more realistic) uncertainty estimates and a more automated algorithm. In particular, the approach gives seabed parameter uncertainty estimates that account for uncertainty due to prior model choice (layering and data error statistics). The approach is applied to data measured on a vertical array in the Mediterranean Sea.

Clinical study of the Erlanger silver catheter--data management and biometry.

PubMed

Martus, P; Geis, C; Lugauer, S; Böswald, M; Guggenbichler, J P

1999-01-01

The clinical evaluation of venous catheters for catheter-induced infections must conform to a strict biometric methodology. The statistical planning of the study (target population, design, degree of blinding), data management (database design, definition of variables, coding), quality assurance (data inspection at several levels) and the biometric evaluation of the Erlanger silver catheter project are described. The three-step data flow included: 1) primary data from the hospital, 2) relational database, 3) files accessible for statistical evaluation. Two different statistical models were compared: analyzing the first catheter only of a patient in the analysis (independent data) and analyzing several catheters from the same patient (dependent data) by means of the generalized estimating equations (GEE) method. The main result of the study was based on the comparison of both statistical models.

A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields

DOE PAGES

Nosedal-Sanchez, Alvaro; Jackson, Charles S.; Huerta, Gabriel

2016-07-20

A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of fieldmore » and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.« less

A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields

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

Nosedal-Sanchez, Alvaro; Jackson, Charles S.; Huerta, Gabriel

A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of fieldmore » and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.« less

[Artificial neural networks for decision making in urologic oncology].

PubMed

Remzi, M; Djavan, B

2007-06-01

This chapter presents a detailed introduction regarding Artificial Neural Networks (ANNs) and their contribution to modern Urologic Oncology. It includes a description of ANNs methodology and points out the differences between Artifical Intelligence and traditional statistic models in terms of usefulness for patients and clinicians, and its advantages over current statistical analysis.

Prediction of Chemical Function: Model Development and Application

EPA Science Inventory

The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (...

Development of LACIE CCEA-1 weather/wheat yield models. [regression analysis

NASA Technical Reports Server (NTRS)

Strommen, N. D.; Sakamoto, C. M.; Leduc, S. K.; Umberger, D. E. (Principal Investigator)

1979-01-01

The advantages and disadvantages of the casual (phenological, dynamic, physiological), statistical regression, and analog approaches to modeling for grain yield are examined. Given LACIE's primary goal of estimating wheat production for the large areas of eight major wheat-growing regions, the statistical regression approach of correlating historical yield and climate data offered the Center for Climatic and Environmental Assessment the greatest potential return within the constraints of time and data sources. The basic equation for the first generation wheat-yield model is given. Topics discussed include truncation, trend variable, selection of weather variables, episodic events, strata selection, operational data flow, weighting, and model results.

Alpha1 LASSO data bundles Lamont, OK

DOE Data Explorer

Gustafson, William Jr; Vogelmann, Andrew; Endo, Satoshi; Toto, Tami; Xiao, Heng; Li, Zhijin; Cheng, Xiaoping; Krishna, Bhargavi (ORCID:000000018828528X)

2016-08-03

A data bundle is a unified package consisting of LASSO LES input and output, observations, evaluation diagnostics, and model skill scores. LES input includes model configuration information and forcing data. LES output includes profile statistics and full domain fields of cloud and environmental variables. Model evaluation data consists of LES output and ARM observations co-registered on the same grid and sampling frequency. Model performance is quantified by skill scores and diagnostics in terms of cloud and environmental variables.

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.

A statistical rain attenuation prediction model with application to the advanced communication technology satellite project. 1: Theoretical development and application to yearly predictions for selected cities in the United States

NASA Technical Reports Server (NTRS)

Manning, Robert M.

1986-01-01

A rain attenuation prediction model is described for use in calculating satellite communication link availability for any specific location in the world that is characterized by an extended record of rainfall. Such a formalism is necessary for the accurate assessment of such availability predictions in the case of the small user-terminal concept of the Advanced Communication Technology Satellite (ACTS) Project. The model employs the theory of extreme value statistics to generate the necessary statistical rainrate parameters from rain data in the form compiled by the National Weather Service. These location dependent rain statistics are then applied to a rain attenuation model to obtain a yearly prediction of the occurrence of attenuation on any satellite link at that location. The predictions of this model are compared to those of the Crane Two-Component Rain Model and some empirical data and found to be very good. The model is then used to calculate rain attenuation statistics at 59 locations in the United States (including Alaska and Hawaii) for the 20 GHz downlinks and 30 GHz uplinks of the proposed ACTS system. The flexibility of this modeling formalism is such that it allows a complete and unified treatment of the temporal aspects of rain attenuation that leads to the design of an optimum stochastic power control algorithm, the purpose of which is to efficiently counter such rain fades on a satellite link.

Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort.

PubMed

Fortner, Renée T; Hüsing, Anika; Kühn, Tilman; Konar, Meric; Overvad, Kim; Tjønneland, Anne; Hansen, Louise; Boutron-Ruault, Marie-Christine; Severi, Gianluca; Fournier, Agnès; Boeing, Heiner; Trichopoulou, Antonia; Benetou, Vasiliki; Orfanos, Philippos; Masala, Giovanna; Agnoli, Claudia; Mattiello, Amalia; Tumino, Rosario; Sacerdote, Carlotta; Bueno-de-Mesquita, H B As; Peeters, Petra H M; Weiderpass, Elisabete; Gram, Inger T; Gavrilyuk, Oxana; Quirós, J Ramón; Maria Huerta, José; Ardanaz, Eva; Larrañaga, Nerea; Lujan-Barroso, Leila; Sánchez-Cantalejo, Emilio; Butt, Salma Tunå; Borgquist, Signe; Idahl, Annika; Lundin, Eva; Khaw, Kay-Tee; Allen, Naomi E; Rinaldi, Sabina; Dossus, Laure; Gunter, Marc; Merritt, Melissa A; Tzoulaki, Ioanna; Riboli, Elio; Kaaks, Rudolf

2017-03-15

Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated for endometrial cancer. Using a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines were evaluated in a step-wise backward selection process; biomarkers were retained at p < 0.157 indicating improvement in the Akaike information criterion (AIC). Improvement in discrimination was assessed using the C-statistic for all biomarkers alone, and change in C-statistic from addition of biomarkers to preexisting absolute risk estimates. We used internal validation with bootstrapping (1000-fold) to adjust for over-fitting. Adiponectin, estrone, interleukin-1 receptor antagonist, tumor necrosis factor-alpha and triglycerides were selected into the model. After accounting for over-fitting, discrimination was improved by 2.0 percentage points when all evaluated biomarkers were included and 1.7 percentage points in the model including the selected biomarkers. Models including etiologic markers on independent pathways and genetic markers may further improve discrimination. © 2016 UICC.

Identifying the Source of Misfit in Item Response Theory Models.

PubMed

Liu, Yang; Maydeu-Olivares, Alberto

2014-01-01

When an item response theory model fails to fit adequately, the items for which the model provides a good fit and those for which it does not must be determined. To this end, we compare the performance of several fit statistics for item pairs with known asymptotic distributions under maximum likelihood estimation of the item parameters: (a) a mean and variance adjustment to bivariate Pearson's X(2), (b) a bivariate subtable analog to Reiser's (1996) overall goodness-of-fit test, (c) a z statistic for the bivariate residual cross product, and (d) Maydeu-Olivares and Joe's (2006) M2 statistic applied to bivariate subtables. The unadjusted Pearson's X(2) with heuristically determined degrees of freedom is also included in the comparison. For binary and ordinal data, our simulation results suggest that the z statistic has the best Type I error and power behavior among all the statistics under investigation when the observed information matrix is used in its computation. However, if one has to use the cross-product information, the mean and variance adjusted X(2) is recommended. We illustrate the use of pairwise fit statistics in 2 real-data examples and discuss possible extensions of the current research in various directions.

Statistical Modelling of Temperature and Moisture Uptake of Biochars Exposed to Selected Relative Humidity of Air.

PubMed

Bastistella, Luciane; Rousset, Patrick; Aviz, Antonio; Caldeira-Pires, Armando; Humbert, Gilles; Nogueira, Manoel

2018-02-09

New experimental techniques, as well as modern variants on known methods, have recently been employed to investigate the fundamental reactions underlying the oxidation of biochar. The purpose of this paper was to experimentally and statistically study how the relative humidity of air, mass, and particle size of four biochars influenced the adsorption of water and the increase in temperature. A random factorial design was employed using the intuitive statistical software Xlstat. A simple linear regression model and an analysis of variance with a pairwise comparison were performed. The experimental study was carried out on the wood of Quercus pubescens , Cyclobalanopsis glauca , Trigonostemon huangmosun , and Bambusa vulgaris , and involved five relative humidity conditions (22, 43, 75, 84, and 90%), two mass samples (0.1 and 1 g), and two particle sizes (powder and piece). Two response variables including water adsorption and temperature increase were analyzed and discussed. The temperature did not increase linearly with the adsorption of water. Temperature was modeled by nine explanatory variables, while water adsorption was modeled by eight. Five variables, including factors and their interactions, were found to be common to the two models. Sample mass and relative humidity influenced the two qualitative variables, while particle size and biochar type only influenced the temperature.

Effects of Instructional Design with Mental Model Analysis on Learning.

ERIC Educational Resources Information Center

Hong, Eunsook

This paper presents a model for systematic instructional design that includes mental model analysis together with the procedures used in developing computer-based instructional materials in the area of statistical hypothesis testing. The instructional design model is based on the premise that the objective for learning is to achieve expert-like…

Modeling Smoke Plume-Rise and Dispersion from Southern United States Prescribed Burns with Daysmoke

Treesearch

G L Achtemeier; S L Goodrick; Y Liu; F Garcia-Menendez; Y Hu; M. Odman

2011-01-01

We present Daysmoke, an empirical-statistical plume rise and dispersion model for simulating smoke from prescribed burns. Prescribed fires are characterized by complex plume structure including multiple-core updrafts which makes modeling with simple plume models difficult. Daysmoke accounts for plume structure in a three-dimensional veering/sheering atmospheric...

Representing Micro-Macro Linkages by Actor-Based Dynamic Network Models

ERIC Educational Resources Information Center

Snijders, Tom A. B.; Steglich, Christian E. G.

2015-01-01

Stochastic actor-based models for network dynamics have the primary aim of statistical inference about processes of network change, but may be regarded as a kind of agent-based models. Similar to many other agent-based models, they are based on local rules for actor behavior. Different from many other agent-based models, by including elements of…

Ten Years of Cloud Properties from MODIS: Global Statistics and Use in Climate Model Evaluation

NASA Technical Reports Server (NTRS)

Platnick, Steven E.

2011-01-01

The NASA Moderate Resolution Imaging Spectroradiometer (MODIS), launched onboard the Terra and Aqua spacecrafts, began Earth observations on February 24, 2000 and June 24,2002, respectively. Among the algorithms developed and applied to this sensor, a suite of cloud products includes cloud masking/detection, cloud-top properties (temperature, pressure), and optical properties (optical thickness, effective particle radius, water path, and thermodynamic phase). All cloud algorithms underwent numerous changes and enhancements between for the latest Collection 5 production version; this process continues with the current Collection 6 development. We will show example MODIS Collection 5 cloud climatologies derived from global spatial . and temporal aggregations provided in the archived gridded Level-3 MODIS atmosphere team product (product names MOD08 and MYD08 for MODIS Terra and Aqua, respectively). Data sets in this Level-3 product include scalar statistics as well as 1- and 2-D histograms of many cloud properties, allowing for higher order information and correlation studies. In addition to these statistics, we will show trends and statistical significance in annual and seasonal means for a variety of the MODIS cloud properties, as well as the time required for detection given assumed trends. To assist in climate model evaluation, we have developed a MODIS cloud simulator with an accompanying netCDF file containing subsetted monthly Level-3 statistical data sets that correspond to the simulator output. Correlations of cloud properties with ENSO offer the potential to evaluate model cloud sensitivity; initial results will be discussed.

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...

Performance Analysis of Live-Virtual-Constructive and Distributed Virtual Simulations: Defining Requirements in Terms of Temporal Consistency

DTIC Science & Technology

2009-12-01

events. Work associated with aperiodic tasks have the same statistical behavior and the same timing requirements. The timing deadlines are soft. • Sporadic...answers, but it is possible to calculate how precise the estimates are. Simulation-based performance analysis of a model includes a statistical ...to evaluate all pos- sible states in a timely manner. This is the principle reason for resorting to simulation and statistical analysis to evaluate

Analysis and Report on SD2000: A Workshop to Determine Structural Dynamics Research for the Millenium

DTIC Science & Technology

2000-04-10

interest. These include Statistical Energy Analysis (SEA), fuzzy structure theory, and approaches combining modal analysis and SEA. Non-determinism...34 arising with increasing frequency. This has led to Statistical Energy Analysis , in which a system is modelled as a collection of coupled subsystems...22. IUTAM Symposium on Statistical Energy Analysis . 1999 Ed. F.J. Fahy and W.G. Price. Kluwer Academic Publishing. • 23. R.S. Langley and P

Statistical considerations in the development of injury risk functions.

PubMed

McMurry, Timothy L; Poplin, Gerald S

2015-01-01

We address 4 frequently misunderstood and important statistical ideas in the construction of injury risk functions. These include the similarities of survival analysis and logistic regression, the correct scale on which to construct pointwise confidence intervals for injury risk, the ability to discern which form of injury risk function is optimal, and the handling of repeated tests on the same subject. The statistical models are explored through simulation and examination of the underlying mathematics. We provide recommendations for the statistically valid construction and correct interpretation of single-predictor injury risk functions. This article aims to provide useful and understandable statistical guidance to improve the practice in constructing injury risk functions.

Improving the Validity of Activity of Daily Living Dependency Risk Assessment

PubMed Central

Clark, Daniel O.; Stump, Timothy E.; Tu, Wanzhu; Miller, Douglas K.

2015-01-01

Objectives Efforts to prevent activity of daily living (ADL) dependency may be improved through models that assess older adults’ dependency risk. We evaluated whether cognition and gait speed measures improve the predictive validity of interview-based models. Method Participants were 8,095 self-respondents in the 2006 Health and Retirement Survey who were aged 65 years or over and independent in five ADLs. Incident ADL dependency was determined from the 2008 interview. Models were developed using random 2/3rd cohorts and validated in the remaining 1/3rd. Results Compared to a c-statistic of 0.79 in the best interview model, the model including cognitive measures had c-statistics of 0.82 and 0.80 while the best fitting gait speed model had c-statistics of 0.83 and 0.79 in the development and validation cohorts, respectively. Conclusion Two relatively brief models, one that requires an in-person assessment and one that does not, had excellent validity for predicting incident ADL dependency but did not significantly improve the predictive validity of the best fitting interview-based models. PMID:24652867

BCM: toolkit for Bayesian analysis of Computational Models using samplers.

PubMed

Thijssen, Bram; Dijkstra, Tjeerd M H; Heskes, Tom; Wessels, Lodewyk F A

2016-10-21

Computational models in biology are characterized by a large degree of uncertainty. This uncertainty can be analyzed with Bayesian statistics, however, the sampling algorithms that are frequently used for calculating Bayesian statistical estimates are computationally demanding, and each algorithm has unique advantages and disadvantages. It is typically unclear, before starting an analysis, which algorithm will perform well on a given computational model. We present BCM, a toolkit for the Bayesian analysis of Computational Models using samplers. It provides efficient, multithreaded implementations of eleven algorithms for sampling from posterior probability distributions and for calculating marginal likelihoods. BCM includes tools to simplify the process of model specification and scripts for visualizing the results. The flexible architecture allows it to be used on diverse types of biological computational models. In an example inference task using a model of the cell cycle based on ordinary differential equations, BCM is significantly more efficient than existing software packages, allowing more challenging inference problems to be solved. BCM represents an efficient one-stop-shop for computational modelers wishing to use sampler-based Bayesian statistics.

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.

External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort: prospective multicentre cohort study.

PubMed

Lamain-de Ruiter, Marije; Kwee, Anneke; Naaktgeboren, Christiana A; de Groot, Inge; Evers, Inge M; Groenendaal, Floris; Hering, Yolanda R; Huisjes, Anjoke J M; Kirpestein, Cornel; Monincx, Wilma M; Siljee, Jacqueline E; Van 't Zelfde, Annewil; van Oirschot, Charlotte M; Vankan-Buitelaar, Simone A; Vonk, Mariska A A W; Wiegers, Therese A; Zwart, Joost J; Franx, Arie; Moons, Karel G M; Koster, Maria P H

2016-08-30

 To perform an external validation and direct comparison of published prognostic models for early prediction of the risk of gestational diabetes mellitus, including predictors applicable in the first trimester of pregnancy.  External validation of all published prognostic models in large scale, prospective, multicentre cohort study.  31 independent midwifery practices and six hospitals in the Netherlands.  Women recruited in their first trimester (<14 weeks) of pregnancy between December 2012 and January 2014, at their initial prenatal visit. Women with pre-existing diabetes mellitus of any type were excluded.  Discrimination of the prognostic models was assessed by the C statistic, and calibration assessed by calibration plots.  3723 women were included for analysis, of whom 181 (4.9%) developed gestational diabetes mellitus in pregnancy. 12 prognostic models for the disorder could be validated in the cohort. C statistics ranged from 0.67 to 0.78. Calibration plots showed that eight of the 12 models were well calibrated. The four models with the highest C statistics included almost all of the following predictors: maternal age, maternal body mass index, history of gestational diabetes mellitus, ethnicity, and family history of diabetes. Prognostic models had a similar performance in a subgroup of nulliparous women only. Decision curve analysis showed that the use of these four models always had a positive net benefit.  In this external validation study, most of the published prognostic models for gestational diabetes mellitus show acceptable discrimination and calibration. The four models with the highest discriminative abilities in this study cohort, which also perform well in a subgroup of nulliparous women, are easy models to apply in clinical practice and therefore deserve further evaluation regarding their clinical impact. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Testing alternative ground water models using cross-validation and other methods

USGS Publications Warehouse

Foglia, L.; Mehl, S.W.; Hill, M.C.; Perona, P.; Burlando, P.

2007-01-01

Many methods can be used to test alternative ground water models. Of concern in this work are methods able to (1) rank alternative models (also called model discrimination) and (2) identify observations important to parameter estimates and predictions (equivalent to the purpose served by some types of sensitivity analysis). Some of the measures investigated are computationally efficient; others are computationally demanding. The latter are generally needed to account for model nonlinearity. The efficient model discrimination methods investigated include the information criteria: the corrected Akaike information criterion, Bayesian information criterion, and generalized cross-validation. The efficient sensitivity analysis measures used are dimensionless scaled sensitivity (DSS), composite scaled sensitivity, and parameter correlation coefficient (PCC); the other statistics are DFBETAS, Cook's D, and observation-prediction statistic. Acronyms are explained in the introduction. Cross-validation (CV) is a computationally intensive nonlinear method that is used for both model discrimination and sensitivity analysis. The methods are tested using up to five alternative parsimoniously constructed models of the ground water system of the Maggia Valley in southern Switzerland. The alternative models differ in their representation of hydraulic conductivity. A new method for graphically representing CV and sensitivity analysis results for complex models is presented and used to evaluate the utility of the efficient statistics. The results indicate that for model selection, the information criteria produce similar results at much smaller computational cost than CV. For identifying important observations, the only obviously inferior linear measure is DSS; the poor performance was expected because DSS does not include the effects of parameter correlation and PCC reveals large parameter correlations. ?? 2007 National Ground Water Association.

Random glucose is useful for individual prediction of type 2 diabetes: results of the Study of Health in Pomerania (SHIP).

PubMed

Kowall, Bernd; Rathmann, Wolfgang; Giani, Guido; Schipf, Sabine; Baumeister, Sebastian; Wallaschofski, Henri; Nauck, Matthias; Völzke, Henry

2013-04-01

Random glucose is widely used in routine clinical practice. We investigated whether this non-standardized glycemic measure is useful for individual diabetes prediction. The Study of Health in Pomerania (SHIP), a population-based cohort study in north-east Germany, included 3107 diabetes-free persons aged 31-81 years at baseline in 1997-2001. 2475 persons participated at 5-year follow-up and gave self-reports of incident diabetes. For the total sample and for subjects aged ≥50 years, statistical properties of prediction models with and without random glucose were compared. A basic model (including age, sex, diabetes of parents, hypertension and waist circumference) and a comprehensive model (additionally including various lifestyle variables and blood parameters, but not HbA1c) performed statistically significantly better after adding random glucose (e.g., the area under the receiver-operating curve (AROC) increased from 0.824 to 0.856 after adding random glucose to the comprehensive model in the total sample). Likewise, adding random glucose to prediction models which included HbA1c led to significant improvements of predictive ability (e.g., for subjects ≥50 years, AROC increased from 0.824 to 0.849 after adding random glucose to the comprehensive model+HbA1c). Random glucose is useful for individual diabetes prediction, and improves prediction models including HbA1c. Copyright © 2012 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

Watershed regressions for pesticides (warp) models for predicting atrazine concentrations in Corn Belt streams

USGS Publications Warehouse

Stone, Wesley W.; Gilliom, Robert J.

2012-01-01

Watershed Regressions for Pesticides (WARP) models, previously developed for atrazine at the national scale, are improved for application to the United States (U.S.) Corn Belt region by developing region-specific models that include watershed characteristics that are influential in predicting atrazine concentration statistics within the Corn Belt. WARP models for the Corn Belt (WARP-CB) were developed for annual maximum moving-average (14-, 21-, 30-, 60-, and 90-day durations) and annual 95th-percentile atrazine concentrations in streams of the Corn Belt region. The WARP-CB models accounted for 53 to 62% of the variability in the various concentration statistics among the model-development sites. Model predictions were within a factor of 5 of the observed concentration statistic for over 90% of the model-development sites. The WARP-CB residuals and uncertainty are lower than those of the National WARP model for the same sites. Although atrazine-use intensity is the most important explanatory variable in the National WARP models, it is not a significant variable in the WARP-CB models. The WARP-CB models provide improved predictions for Corn Belt streams draining watersheds with atrazine-use intensities of 17 kg/km2 of watershed area or greater.

Modeling Psychological Contract Violation using Dual Regime Models: An Event-based Approach.

PubMed

Hofmans, Joeri

2017-01-01

A good understanding of the dynamics of psychological contract violation requires theories, research methods and statistical models that explicitly recognize that violation feelings follow from an event that violates one's acceptance limits, after which interpretative processes are set into motion, determining the intensity of these violation feelings. Whereas theories-in the form of the dynamic model of the psychological contract-and research methods-in the form of daily diary research and experience sampling research-are available by now, the statistical tools to model such a two-stage process are still lacking. The aim of the present paper is to fill this gap in the literature by introducing two statistical models-the Zero-Inflated model and the Hurdle model-that closely mimic the theoretical process underlying the elicitation violation feelings via two model components: a binary distribution that models whether violation has occurred or not, and a count distribution that models how severe the negative impact is. Moreover, covariates can be included for both model components separately, which yields insight into their unique and shared antecedents. By doing this, the present paper offers a methodological-substantive synergy, showing how sophisticated methodology can be used to examine an important substantive issue.

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

TH-CD-202-07: A Methodology for Generating Numerical Phantoms for Radiation Therapy Using Geometric Attribute Distribution Models

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

Dolly, S; Chen, H; Mutic, S

Purpose: A persistent challenge for the quality assessment of radiation therapy treatments (e.g. contouring accuracy) is the absence of the known, ground truth for patient data. Moreover, assessment results are often patient-dependent. Computer simulation studies utilizing numerical phantoms can be performed for quality assessment with a known ground truth. However, previously reported numerical phantoms do not include the statistical properties of inter-patient variations, as their models are based on only one patient. In addition, these models do not incorporate tumor data. In this study, a methodology was developed for generating numerical phantoms which encapsulate the statistical variations of patients withinmore » radiation therapy, including tumors. Methods: Based on previous work in contouring assessment, geometric attribute distribution (GAD) models were employed to model both the deterministic and stochastic properties of individual organs via principle component analysis. Using pre-existing radiation therapy contour data, the GAD models are trained to model the shape and centroid distributions of each organ. Then, organs with different shapes and positions can be generated by assigning statistically sound weights to the GAD model parameters. Organ contour data from 20 retrospective prostate patient cases were manually extracted and utilized to train the GAD models. As a demonstration, computer-simulated CT images of generated numerical phantoms were calculated and assessed subjectively and objectively for realism. Results: A cohort of numerical phantoms of the male human pelvis was generated. CT images were deemed realistic both subjectively and objectively in terms of image noise power spectrum. Conclusion: A methodology has been developed to generate realistic numerical anthropomorphic phantoms using pre-existing radiation therapy data. The GAD models guarantee that generated organs span the statistical distribution of observed radiation therapy patients, according to the training dataset. The methodology enables radiation therapy treatment assessment with multi-modality imaging and a known ground truth, and without patient-dependent bias.« less

PSSMSearch: a server for modeling, visualization, proteome-wide discovery and annotation of protein motif specificity determinants.

PubMed

Krystkowiak, Izabella; Manguy, Jean; Davey, Norman E

2018-06-05

There is a pressing need for in silico tools that can aid in the identification of the complete repertoire of protein binding (SLiMs, MoRFs, miniMotifs) and modification (moiety attachment/removal, isomerization, cleavage) motifs. We have created PSSMSearch, an interactive web-based tool for rapid statistical modeling, visualization, discovery and annotation of protein motif specificity determinants to discover novel motifs in a proteome-wide manner. PSSMSearch analyses proteomes for regions with significant similarity to a motif specificity determinant model built from a set of aligned motif-containing peptides. Multiple scoring methods are available to build a position-specific scoring matrix (PSSM) describing the motif specificity determinant model. This model can then be modified by a user to add prior knowledge of specificity determinants through an interactive PSSM heatmap. PSSMSearch includes a statistical framework to calculate the significance of specificity determinant model matches against a proteome of interest. PSSMSearch also includes the SLiMSearch framework's annotation, motif functional analysis and filtering tools to highlight relevant discriminatory information. Additional tools to annotate statistically significant shared keywords and GO terms, or experimental evidence of interaction with a motif-recognizing protein have been added. Finally, PSSM-based conservation metrics have been created for taxonomic range analyses. The PSSMSearch web server is available at http://slim.ucd.ie/pssmsearch/.

Sensitivity of Fit Indices to Misspecification in Growth Curve Models

ERIC Educational Resources Information Center

Wu, Wei; West, Stephen G.

2010-01-01

This study investigated the sensitivity of fit indices to model misspecification in within-individual covariance structure, between-individual covariance structure, and marginal mean structure in growth curve models. Five commonly used fit indices were examined, including the likelihood ratio test statistic, root mean square error of…

A Statistical Analysis of Brain Morphology Using Wild Bootstrapping

PubMed Central

Ibrahim, Joseph G.; Tang, Niansheng; Rowe, Daniel B.; Hao, Xuejun; Bansal, Ravi; Peterson, Bradley S.

2008-01-01

Methods for the analysis of brain morphology, including voxel-based morphology and surface-based morphometries, have been used to detect associations between brain structure and covariates of interest, such as diagnosis, severity of disease, age, IQ, and genotype. The statistical analysis of morphometric measures usually involves two statistical procedures: 1) invoking a statistical model at each voxel (or point) on the surface of the brain or brain subregion, followed by mapping test statistics (e.g., t test) or their associated p values at each of those voxels; 2) correction for the multiple statistical tests conducted across all voxels on the surface of the brain region under investigation. We propose the use of new statistical methods for each of these procedures. We first use a heteroscedastic linear model to test the associations between the morphological measures at each voxel on the surface of the specified subregion (e.g., cortical or subcortical surfaces) and the covariates of interest. Moreover, we develop a robust test procedure that is based on a resampling method, called wild bootstrapping. This procedure assesses the statistical significance of the associations between a measure of given brain structure and the covariates of interest. The value of this robust test procedure lies in its computationally simplicity and in its applicability to a wide range of imaging data, including data from both anatomical and functional magnetic resonance imaging (fMRI). Simulation studies demonstrate that this robust test procedure can accurately control the family-wise error rate. We demonstrate the application of this robust test procedure to the detection of statistically significant differences in the morphology of the hippocampus over time across gender groups in a large sample of healthy subjects. PMID:17649909

United States Census 2000 Population with Bridged Race Categories. Vital and Health Statistics. Data Evaluation and Methods Research.

ERIC Educational Resources Information Center

Ingram, Deborah D.; Parker, Jennifer D.; Schenker, Nathaniel; Weed, James A.; Hamilton, Brady; Arias, Elizabeth; Madans, Jennifer H.

This report documents the National Center for Health Statistics' (NCHS) methods for bridging the Census 2000 multiple-race resident population to single-race categories and describing bridged race resident population estimates. Data came from the pooled 1997-2000 National Health Interview Surveys. The bridging models included demographic and…

An Empirical Investigation of Methods for Assessing Item Fit for Mixed Format Tests

ERIC Educational Resources Information Center

Chon, Kyong Hee; Lee, Won-Chan; Ansley, Timothy N.

2013-01-01

Empirical information regarding performance of model-fit procedures has been a persistent need in measurement practice. Statistical procedures for evaluating item fit were applied to real test examples that consist of both dichotomously and polytomously scored items. The item fit statistics used in this study included the PARSCALE's G[squared],…

Statistics of stable marriages

NASA Astrophysics Data System (ADS)

Dzierzawa, Michael; Oméro, Marie-José

2000-11-01

In the stable marriage problem N men and N women have to be matched by pairs under the constraint that the resulting matching is stable. We study the statistical properties of stable matchings in the large N limit using both numerical and analytical methods. Generalizations of the model including singles and unequal numbers of men and women are also investigated.

Statistical Analysis of CFD Solutions from the Fourth AIAA Drag Prediction Workshop

NASA Technical Reports Server (NTRS)

Morrison, Joseph H.

2010-01-01

A graphical framework is used for statistical analysis of the results from an extensive N-version test of a collection of Reynolds-averaged Navier-Stokes computational fluid dynamics codes. The solutions were obtained by code developers and users from the U.S., Europe, Asia, and Russia using a variety of grid systems and turbulence models for the June 2009 4th Drag Prediction Workshop sponsored by the AIAA Applied Aerodynamics Technical Committee. The aerodynamic configuration for this workshop was a new subsonic transport model, the Common Research Model, designed using a modern approach for the wing and included a horizontal tail. The fourth workshop focused on the prediction of both absolute and incremental drag levels for wing-body and wing-body-horizontal tail configurations. This work continues the statistical analysis begun in the earlier workshops and compares the results from the grid convergence study of the most recent workshop with earlier workshops using the statistical framework.

The Development of Statistics Textbook Supported with ICT and Portfolio-Based Assessment

NASA Astrophysics Data System (ADS)

Hendikawati, Putriaji; Yuni Arini, Florentina

2016-02-01

This research was development research that aimed to develop and produce a Statistics textbook model that supported with information and communication technology (ICT) and Portfolio-Based Assessment. This book was designed for students of mathematics at the college to improve students’ ability in mathematical connection and communication. There were three stages in this research i.e. define, design, and develop. The textbooks consisted of 10 chapters which each chapter contains introduction, core materials and include examples and exercises. The textbook developed phase begins with the early stages of designed the book (draft 1) which then validated by experts. Revision of draft 1 produced draft 2 which then limited test for readability test book. Furthermore, revision of draft 2 produced textbook draft 3 which simulated on a small sample to produce a valid model textbook. The data were analysed with descriptive statistics. The analysis showed that the Statistics textbook model that supported with ICT and Portfolio-Based Assessment valid and fill up the criteria of practicality.

Power-up: A Reanalysis of 'Power Failure' in Neuroscience Using Mixture Modeling.

PubMed

Nord, Camilla L; Valton, Vincent; Wood, John; Roiser, Jonathan P

2017-08-23

Recently, evidence for endemically low statistical power has cast neuroscience findings into doubt. If low statistical power plagues neuroscience, then this reduces confidence in the reported effects. However, if statistical power is not uniformly low, then such blanket mistrust might not be warranted. Here, we provide a different perspective on this issue, analyzing data from an influential study reporting a median power of 21% across 49 meta-analyses (Button et al., 2013). We demonstrate, using Gaussian mixture modeling, that the sample of 730 studies included in that analysis comprises several subcomponents so the use of a single summary statistic is insufficient to characterize the nature of the distribution. We find that statistical power is extremely low for studies included in meta-analyses that reported a null result and that it varies substantially across subfields of neuroscience, with particularly low power in candidate gene association studies. Therefore, whereas power in neuroscience remains a critical issue, the notion that studies are systematically underpowered is not the full story: low power is far from a universal problem. SIGNIFICANCE STATEMENT Recently, researchers across the biomedical and psychological sciences have become concerned with the reliability of results. One marker for reliability is statistical power: the probability of finding a statistically significant result given that the effect exists. Previous evidence suggests that statistical power is low across the field of neuroscience. Our results present a more comprehensive picture of statistical power in neuroscience: on average, studies are indeed underpowered-some very seriously so-but many studies show acceptable or even exemplary statistical power. We show that this heterogeneity in statistical power is common across most subfields in neuroscience. This new, more nuanced picture of statistical power in neuroscience could affect not only scientific understanding, but potentially policy and funding decisions for neuroscience research. Copyright © 2017 Nord, Valton et al.

A Comparison of the Performance of Advanced Statistical Techniques for the Refinement of Day-ahead and Longer NWP-based Wind Power Forecasts

NASA Astrophysics Data System (ADS)

Zack, J. W.

2015-12-01

Predictions from Numerical Weather Prediction (NWP) models are the foundation for wind power forecasts for day-ahead and longer forecast horizons. The NWP models directly produce three-dimensional wind forecasts on their respective computational grids. These can be interpolated to the location and time of interest. However, these direct predictions typically contain significant systematic errors ("biases"). This is due to a variety of factors including the limited space-time resolution of the NWP models and shortcomings in the model's representation of physical processes. It has become common practice to attempt to improve the raw NWP forecasts by statistically adjusting them through a procedure that is widely known as Model Output Statistics (MOS). The challenge is to identify complex patterns of systematic errors and then use this knowledge to adjust the NWP predictions. The MOS-based improvements are the basis for much of the value added by commercial wind power forecast providers. There are an enormous number of statistical approaches that can be used to generate the MOS adjustments to the raw NWP forecasts. In order to obtain insight into the potential value of some of the newer and more sophisticated statistical techniques often referred to as "machine learning methods" a MOS-method comparison experiment has been performed for wind power generation facilities in 6 wind resource areas of California. The underlying NWP models that provided the raw forecasts were the two primary operational models of the US National Weather Service: the GFS and NAM models. The focus was on 1- and 2-day ahead forecasts of the hourly wind-based generation. The statistical methods evaluated included: (1) screening multiple linear regression, which served as a baseline method, (2) artificial neural networks, (3) a decision-tree approach called random forests, (4) gradient boosted regression based upon an decision-tree algorithm, (5) support vector regression and (6) analog ensemble, which is a case-matching scheme. The presentation will provide (1) an overview of each method and the experimental design, (2) performance comparisons based on standard metrics such as bias, MAE and RMSE, (3) a summary of the performance characteristics of each approach and (4) a preview of further experiments to be conducted.

Multiplicative point process as a model of trading activity

NASA Astrophysics Data System (ADS)

Gontis, V.; Kaulakys, B.

2004-11-01

Signals consisting of a sequence of pulses show that inherent origin of the 1/ f noise is a Brownian fluctuation of the average interevent time between subsequent pulses of the pulse sequence. In this paper, we generalize the model of interevent time to reproduce a variety of self-affine time series exhibiting power spectral density S( f) scaling as a power of the frequency f. Furthermore, we analyze the relation between the power-law correlations and the origin of the power-law probability distribution of the signal intensity. We introduce a stochastic multiplicative model for the time intervals between point events and analyze the statistical properties of the signal analytically and numerically. Such model system exhibits power-law spectral density S( f)∼1/ fβ for various values of β, including β= {1}/{2}, 1 and {3}/{2}. Explicit expressions for the power spectra in the low-frequency limit and for the distribution density of the interevent time are obtained. The counting statistics of the events is analyzed analytically and numerically, as well. The specific interest of our analysis is related with the financial markets, where long-range correlations of price fluctuations largely depend on the number of transactions. We analyze the spectral density and counting statistics of the number of transactions. The model reproduces spectral properties of the real markets and explains the mechanism of power-law distribution of trading activity. The study provides evidence that the statistical properties of the financial markets are enclosed in the statistics of the time interval between trades. A multiplicative point process serves as a consistent model generating this statistics.

A Random Variable Approach to Nuclear Targeting and Survivability

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

Undem, Halvor A.

We demonstrate a common mathematical formalism for analyzing problems in nuclear survivability and targeting. This formalism, beginning with a random variable approach, can be used to interpret past efforts in nuclear-effects analysis, including targeting analysis. It can also be used to analyze new problems brought about by the post Cold War Era, such as the potential effects of yield degradation in a permanently untested nuclear stockpile. In particular, we illustrate the formalism through four natural case studies or illustrative problems, linking these to actual past data, modeling, and simulation, and suggesting future uses. In the first problem, we illustrate themore » case of a deterministically modeled weapon used against a deterministically responding target. Classic "Cookie Cutter" damage functions result. In the second problem, we illustrate, with actual target test data, the case of a deterministically modeled weapon used against a statistically responding target. This case matches many of the results of current nuclear targeting modeling and simulation tools, including the result of distance damage functions as complementary cumulative lognormal functions in the range variable. In the third problem, we illustrate the case of a statistically behaving weapon used against a deterministically responding target. In particular, we show the dependence of target damage on weapon yield for an untested nuclear stockpile experiencing yield degradation. Finally, and using actual unclassified weapon test data, we illustrate in the fourth problem the case of a statistically behaving weapon used against a statistically responding target.« less

The landscape of W± and Z bosons produced in pp collisions up to LHC energies

NASA Astrophysics Data System (ADS)

Basso, Eduardo; Bourrely, Claude; Pasechnik, Roman; Soffer, Jacques

2017-10-01

We consider a selection of recent experimental results on electroweak W± , Z gauge boson production in pp collisions at BNL RHIC and CERN LHC energies in comparison to prediction of perturbative QCD calculations based on different sets of NLO parton distribution functions including the statistical PDF model known from fits to the DIS data. We show that the current statistical PDF parametrization (fitted to the DIS data only) underestimates the LHC data on W± , Z gauge boson production cross sections at the NLO by about 20%. This suggests that there is a need to refit the parameters of the statistical PDF including the latest LHC data.

A statistical inference for concentrations of benzo[a]pyrene partially measured in the ambient air of an industrial city in Korea

NASA Astrophysics Data System (ADS)

Kim, Yongku; Seo, Young-Kyo; Baek, Sung-Ok

2013-12-01

Although large quantities of air pollutants are released into the atmosphere, they are partially monitored and routinely assessed for their health implications. This paper proposes a statistical model describing the temporal behavior of hazardous air pollutants (HAPs), which can have negative effects on human health. Benzo[a]pyrene (BaP) is selected for statistical modeling. The proposed model incorporates the linkage between BaP and meteorology and is specifically formulated to identify meteorological effects and allow for seasonal trends. The model is used to estimate and forecast temporal fields of BaP conditional on observed (or forecasted) meteorological conditions, including temperature, precipitation, wind speed, and air quality. The effects of BaP on human health are examined by characterizing health indicators, namely the cancer risk and the hazard quotient. The model provides useful information for the optimal monitoring period and projection of future BaP concentrations for both industrial and residential areas in Korea.

Modeling the subfilter scalar variance for large eddy simulation in forced isotropic turbulence

NASA Astrophysics Data System (ADS)

Cheminet, Adam; Blanquart, Guillaume

2011-11-01

Static and dynamic model for the subfilter scalar variance in homogeneous isotropic turbulence are investigated using direct numerical simulations (DNS) of a lineary forced passive scalar field. First, we introduce a new scalar forcing technique conditioned only on the scalar field which allows the fluctuating scalar field to reach a statistically stationary state. Statistical properties, including 2nd and 3rd statistical moments, spectra, and probability density functions of the scalar field have been analyzed. Using this technique, we performed constant density and variable density DNS of scalar mixing in isotropic turbulence. The results are used in an a-priori study of scalar variance models. Emphasis is placed on further studying the dynamic model introduced by G. Balarac, H. Pitsch and V. Raman [Phys. Fluids 20, (2008)]. Scalar variance models based on Bedford and Yeo's expansion are accurate for small filter width but errors arise in the inertial subrange. Results suggest that a constant coefficient computed from an assumed Kolmogorov spectrum is often sufficient to predict the subfilter scalar variance.

The evaluation of different forest structural indices to predict the stand aboveground biomass of even-aged Scotch pine (Pinus sylvestris L.) forests in Kunduz, Northern Turkey.

PubMed

Ercanli, İlker; Kahriman, Aydın

2015-03-01

We assessed the effect of stand structural diversity, including the Shannon, improved Shannon, Simpson, McIntosh, Margelef, and Berger-Parker indices, on stand aboveground biomass (AGB) and developed statistical prediction models for the stand AGB values, including stand structural diversity indices and some stand attributes. The AGB prediction model, including only stand attributes, accounted for 85 % of the total variance in AGB (R (2)) with an Akaike's information criterion (AIC) of 807.2407, Bayesian information criterion (BIC) of 809.5397, Schwarz Bayesian criterion (SBC) of 818.0426, and root mean square error (RMSE) of 38.529 Mg. After inclusion of the stand structural diversity into the model structure, considerable improvement was observed in statistical accuracy, including 97.5 % of the total variance in AGB, with an AIC of 614.1819, BIC of 617.1242, SBC of 633.0853, and RMSE of 15.8153 Mg. The predictive fitting results indicate that some indices describing the stand structural diversity can be employed as significant independent variables to predict the AGB production of the Scotch pine stand. Further, including the stand diversity indices in the AGB prediction model with the stand attributes provided important predictive contributions in estimating the total variance in AGB.

Estimating urban ground-level PM10 using MODIS 3km AOD product and meteorological parameters from WRF model

NASA Astrophysics Data System (ADS)

Ghotbi, Saba; Sotoudeheian, Saeed; Arhami, Mohammad

2016-09-01

Satellite remote sensing products of AOD from MODIS along with appropriate meteorological parameters were used to develop statistical models and estimate ground-level PM10. Most of previous studies obtained meteorological data from synoptic weather stations, with rather sparse spatial distribution, and used it along with 10 km AOD product to develop statistical models, applicable for PM variations in regional scale (resolution of ≥10 km). In the current study, meteorological parameters were simulated with 3 km resolution using WRF model and used along with the rather new 3 km AOD product (launched in 2014). The resulting PM statistical models were assessed for a polluted and largely variable urban area, Tehran, Iran. Despite the critical particulate pollution problem, very few PM studies were conducted in this area. The issue of rather poor direct PM-AOD associations existed, due to different factors such as variations in particles optical properties, in addition to bright background issue for satellite data, as the studied area located in the semi-arid areas of Middle East. Statistical approach of linear mixed effect (LME) was used, and three types of statistical models including single variable LME model (using AOD as independent variable) and multiple variables LME model by using meteorological data from two sources, WRF model and synoptic stations, were examined. Meteorological simulations were performed using a multiscale approach and creating an appropriate physic for the studied region, and the results showed rather good agreements with recordings of the synoptic stations. The single variable LME model was able to explain about 61%-73% of daily PM10 variations, reflecting a rather acceptable performance. Statistical models performance improved through using multivariable LME and incorporating meteorological data as auxiliary variables, particularly by using fine resolution outputs from WRF (R2 = 0.73-0.81). In addition, rather fine resolution for PM estimates was mapped for the studied city, and resulting concentration maps were consistent with PM recordings at the existing stations.

PyEvolve: a toolkit for statistical modelling of molecular evolution.

PubMed

Butterfield, Andrew; Vedagiri, Vivek; Lang, Edward; Lawrence, Cath; Wakefield, Matthew J; Isaev, Alexander; Huttley, Gavin A

2004-01-05

Examining the distribution of variation has proven an extremely profitable technique in the effort to identify sequences of biological significance. Most approaches in the field, however, evaluate only the conserved portions of sequences - ignoring the biological significance of sequence differences. A suite of sophisticated likelihood based statistical models from the field of molecular evolution provides the basis for extracting the information from the full distribution of sequence variation. The number of different problems to which phylogeny-based maximum likelihood calculations can be applied is extensive. Available software packages that can perform likelihood calculations suffer from a lack of flexibility and scalability, or employ error-prone approaches to model parameterisation. Here we describe the implementation of PyEvolve, a toolkit for the application of existing, and development of new, statistical methods for molecular evolution. We present the object architecture and design schema of PyEvolve, which includes an adaptable multi-level parallelisation schema. The approach for defining new methods is illustrated by implementing a novel dinucleotide model of substitution that includes a parameter for mutation of methylated CpG's, which required 8 lines of standard Python code to define. Benchmarking was performed using either a dinucleotide or codon substitution model applied to an alignment of BRCA1 sequences from 20 mammals, or a 10 species subset. Up to five-fold parallel performance gains over serial were recorded. Compared to leading alternative software, PyEvolve exhibited significantly better real world performance for parameter rich models with a large data set, reducing the time required for optimisation from approximately 10 days to approximately 6 hours. PyEvolve provides flexible functionality that can be used either for statistical modelling of molecular evolution, or the development of new methods in the field. The toolkit can be used interactively or by writing and executing scripts. The toolkit uses efficient processes for specifying the parameterisation of statistical models, and implements numerous optimisations that make highly parameter rich likelihood functions solvable within hours on multi-cpu hardware. PyEvolve can be readily adapted in response to changing computational demands and hardware configurations to maximise performance. PyEvolve is released under the GPL and can be downloaded from http://cbis.anu.edu.au/software.

Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

NASA Astrophysics Data System (ADS)

Minh, Vu Trieu; Katushin, Dmitri; Antonov, Maksim; Veinthal, Renno

2017-03-01

This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW), and the alpha angle (Alpha) between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP). Four (4) statistical regression models (two linear and two nonlinear) are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2) of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.

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

Bayesian change point analysis of abundance trends for pelagic fishes in the upper San Francisco Estuary.

PubMed

Thomson, James R; Kimmerer, Wim J; Brown, Larry R; Newman, Ken B; Mac Nally, Ralph; Bennett, William A; Feyrer, Frederick; Fleishman, Erica

2010-07-01

We examined trends in abundance of four pelagic fish species (delta smelt, longfin smelt, striped bass, and threadfin shad) in the upper San Francisco Estuary, California, USA, over 40 years using Bayesian change point models. Change point models identify times of abrupt or unusual changes in absolute abundance (step changes) or in rates of change in abundance (trend changes). We coupled Bayesian model selection with linear regression splines to identify biotic or abiotic covariates with the strongest associations with abundances of each species. We then refitted change point models conditional on the selected covariates to explore whether those covariates could explain statistical trends or change points in species abundances. We also fitted a multispecies change point model that identified change points common to all species. All models included hierarchical structures to model data uncertainties, including observation errors and missing covariate values. There were step declines in abundances of all four species in the early 2000s, with a likely common decline in 2002. Abiotic variables, including water clarity, position of the 2 per thousand isohaline (X2), and the volume of freshwater exported from the estuary, explained some variation in species' abundances over the time series, but no selected covariates could explain statistically the post-2000 change points for any species.

Estimation of Total Nitrogen and Phosphorus in New England Streams Using Spatially Referenced Regression Models

USGS Publications Warehouse

Moore, Richard Bridge; Johnston, Craig M.; Robinson, Keith W.; Deacon, Jeffrey R.

2004-01-01

The U.S. Geological Survey (USGS), in cooperation with the U.S. Environmental Protection Agency (USEPA) and the New England Interstate Water Pollution Control Commission (NEIWPCC), has developed a water-quality model, called SPARROW (Spatially Referenced Regressions on Watershed Attributes), to assist in regional total maximum daily load (TMDL) and nutrient-criteria activities in New England. SPARROW is a spatially detailed, statistical model that uses regression equations to relate total nitrogen and phosphorus (nutrient) stream loads to nutrient sources and watershed characteristics. The statistical relations in these equations are then used to predict nutrient loads in unmonitored streams. The New England SPARROW models are built using a hydrologic network of 42,000 stream reaches and associated watersheds. Watershed boundaries are defined for each stream reach in the network through the use of a digital elevation model and existing digitized watershed divides. Nutrient source data is from permitted wastewater discharge data from USEPA's Permit Compliance System (PCS), various land-use sources, and atmospheric deposition. Physical watershed characteristics include drainage area, land use, streamflow, time-of-travel, stream density, percent wetlands, slope of the land surface, and soil permeability. The New England SPARROW models for total nitrogen and total phosphorus have R-squared values of 0.95 and 0.94, with mean square errors of 0.16 and 0.23, respectively. Variables that were statistically significant in the total nitrogen model include permitted municipal-wastewater discharges, atmospheric deposition, agricultural area, and developed land area. Total nitrogen stream-loss rates were significant only in streams with average annual flows less than or equal to 2.83 cubic meters per second. In streams larger than this, there is nondetectable in-stream loss of annual total nitrogen in New England. Variables that were statistically significant in the total phosphorus model include discharges for municipal wastewater-treatment facilities and pulp and paper facilities, developed land area, agricultural area, and forested area. For total phosphorus, loss rates were significant for reservoirs with surface areas of 10 square kilometers or less, and in streams with flows less than or equal to 2.83 cubic meters per second. Applications of SPARROW for evaluating nutrient loading in New England waters include estimates of the spatial distributions of total nitrogen and phosphorus yields, sources of the nutrients, and the potential for delivery of those yields to receiving waters. This information can be used to (1) predict ranges in nutrient levels in surface waters, (2) identify the environmental variables that are statistically significant predictors of nutrient levels in streams, (3) evaluate monitoring efforts for better determination of nutrient loads, and (4) evaluate management options for reducing nutrient loads to achieve water-quality goals.

Comparing geological and statistical approaches for element selection in sediment tracing research

NASA Astrophysics Data System (ADS)

Laceby, J. Patrick; McMahon, Joe; Evrard, Olivier; Olley, Jon

2015-04-01

Elevated suspended sediment loads reduce reservoir capacity and significantly increase the cost of operating water treatment infrastructure, making the management of sediment supply to reservoirs of increasingly importance. Sediment fingerprinting techniques can be used to determine the relative contributions of different sources of sediment accumulating in reservoirs. The objective of this research is to compare geological and statistical approaches to element selection for sediment fingerprinting modelling. Time-integrated samplers (n=45) were used to obtain source samples from four major subcatchments flowing into the Baroon Pocket Dam in South East Queensland, Australia. The geochemistry of potential sources were compared to the geochemistry of sediment cores (n=12) sampled in the reservoir. The geochemical approach selected elements for modelling that provided expected, observed and statistical discrimination between sediment sources. Two statistical approaches selected elements for modelling with the Kruskal-Wallis H-test and Discriminatory Function Analysis (DFA). In particular, two different significance levels (0.05 & 0.35) for the DFA were included to investigate the importance of element selection on modelling results. A distribution model determined the relative contributions of different sources to sediment sampled in the Baroon Pocket Dam. Elemental discrimination was expected between one subcatchment (Obi Obi Creek) and the remaining subcatchments (Lexys, Falls and Bridge Creek). Six major elements were expected to provide discrimination. Of these six, only Fe2O3 and SiO2 provided expected, observed and statistical discrimination. Modelling results with this geological approach indicated 36% (+/- 9%) of sediment sampled in the reservoir cores were from mafic-derived sources and 64% (+/- 9%) were from felsic-derived sources. The geological and the first statistical approach (DFA0.05) differed by only 1% (σ 5%) for 5 out of 6 model groupings with only the Lexys Creek modelling results differing significantly (35%). The statistical model with expanded elemental selection (DFA0.35) differed from the geological model by an average of 30% for all 6 models. Elemental selection for sediment fingerprinting therefore has the potential to impact modeling results. Accordingly is important to incorporate both robust geological and statistical approaches when selecting elements for sediment fingerprinting. For the Baroon Pocket Dam, management should focus on reducing the supply of sediments derived from felsic sources in each of the subcatchments.

An overview of the mathematical and statistical analysis component of RICIS

NASA Technical Reports Server (NTRS)

Hallum, Cecil R.

1987-01-01

Mathematical and statistical analysis components of RICIS (Research Institute for Computing and Information Systems) can be used in the following problem areas: (1) quantification and measurement of software reliability; (2) assessment of changes in software reliability over time (reliability growth); (3) analysis of software-failure data; and (4) decision logic for whether to continue or stop testing software. Other areas of interest to NASA/JSC where mathematical and statistical analysis can be successfully employed include: math modeling of physical systems, simulation, statistical data reduction, evaluation methods, optimization, algorithm development, and mathematical methods in signal processing.

Development of a Stochastically-driven, Forward Predictive Performance Model for PEMFCs

NASA Astrophysics Data System (ADS)

Harvey, David Benjamin Paul

A one-dimensional multi-scale coupled, transient, and mechanistic performance model for a PEMFC membrane electrode assembly has been developed. The model explicitly includes each of the 5 layers within a membrane electrode assembly and solves for the transport of charge, heat, mass, species, dissolved water, and liquid water. Key features of the model include the use of a multi-step implementation of the HOR reaction on the anode, agglomerate catalyst sub-models for both the anode and cathode catalyst layers, a unique approach that links the composition of the catalyst layer to key properties within the agglomerate model and the implementation of a stochastic input-based approach for component material properties. The model employs a new methodology for validation using statistically varying input parameters and statistically-based experimental performance data; this model represents the first stochastic input driven unit cell performance model. The stochastic input driven performance model was used to identify optimal ionomer content within the cathode catalyst layer, demonstrate the role of material variation in potential low performing MEA materials, provide explanation for the performance of low-Pt loaded MEAs, and investigate the validity of transient-sweep experimental diagnostic methods.

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

Leads Detection Using Mixture Statistical Distribution Based CRF Algorithm from Sentinel-1 Dual Polarization SAR Imagery

NASA Astrophysics Data System (ADS)

Zhang, Yu; Li, Fei; Zhang, Shengkai; Zhu, Tingting

2017-04-01

Synthetic Aperture Radar (SAR) is significantly important for polar remote sensing since it can provide continuous observations in all days and all weather. SAR can be used for extracting the surface roughness information characterized by the variance of dielectric properties and different polarization channels, which make it possible to observe different ice types and surface structure for deformation analysis. In November, 2016, Chinese National Antarctic Research Expedition (CHINARE) 33rd cruise has set sails in sea ice zone in Antarctic. Accurate leads spatial distribution in sea ice zone for routine planning of ship navigation is essential. In this study, the semantic relationship between leads and sea ice categories has been described by the Conditional Random Fields (CRF) model, and leads characteristics have been modeled by statistical distributions in SAR imagery. In the proposed algorithm, a mixture statistical distribution based CRF is developed by considering the contexture information and the statistical characteristics of sea ice for improving leads detection in Sentinel-1A dual polarization SAR imagery. The unary potential and pairwise potential in CRF model is constructed by integrating the posteriori probability estimated from statistical distributions. For mixture statistical distribution parameter estimation, Method of Logarithmic Cumulants (MoLC) is exploited for single statistical distribution parameters estimation. The iteration based Expectation Maximal (EM) algorithm is investigated to calculate the parameters in mixture statistical distribution based CRF model. In the posteriori probability inference, graph-cut energy minimization method is adopted in the initial leads detection. The post-processing procedures including aspect ratio constrain and spatial smoothing approaches are utilized to improve the visual result. The proposed method is validated on Sentinel-1A SAR C-band Extra Wide Swath (EW) Ground Range Detected (GRD) imagery with a pixel spacing of 40 meters near Prydz Bay area, East Antarctica. Main work is listed as follows: 1) A mixture statistical distribution based CRF algorithm has been developed for leads detection from Sentinel-1A dual polarization images. 2) The assessment of the proposed mixture statistical distribution based CRF method and single distribution based CRF algorithm has been presented. 3) The preferable parameters sets including statistical distributions, the aspect ratio threshold and spatial smoothing window size have been provided. In the future, the proposed algorithm will be developed for the operational Sentinel series data sets processing due to its less time consuming cost and high accuracy in leads detection.

Prognostic value of coronary computed tomographic angiography findings in asymptomatic individuals: a 6-year follow-up from the prospective multicentre international CONFIRM study.

PubMed

Cho, Iksung; Al'Aref, Subhi J; Berger, Adam; Ó Hartaigh, Bríain; Gransar, Heidi; Valenti, Valentina; Lin, Fay Y; Achenbach, Stephan; Berman, Daniel S; Budoff, Matthew J; Callister, Tracy Q; Al-Mallah, Mouaz H; Cademartiri, Filippo; Chinnaiyan, Kavitha; Chow, Benjamin J W; DeLago, Augustin; Villines, Todd C; Hadamitzky, Martin; Hausleiter, Joerg; Leipsic, Jonathon; Shaw, Leslee J; Kaufmann, Philipp A; Feuchtner, Gudrun; Kim, Yong-Jin; Maffei, Erica; Raff, Gilbert; Pontone, Gianluca; Andreini, Daniele; Marques, Hugo; Rubinshtein, Ronen; Chang, Hyuk-Jae; Min, James K

2018-03-14

The long-term prognostic benefit of coronary computed tomographic angiography (CCTA) findings of coronary artery disease (CAD) in asymptomatic populations is unknown. From the prospective multicentre international CONFIRM long-term study, we evaluated asymptomatic subjects without known CAD who underwent both coronary artery calcium scoring (CACS) and CCTA (n = 1226). Coronary computed tomographic angiography findings included the severity of coronary artery stenosis, plaque composition, and coronary segment location. Using the C-statistic and likelihood ratio tests, we evaluated the incremental prognostic utility of CCTA findings over a base model that included a panel of traditional risk factors (RFs) as well as CACS to predict long-term all-cause mortality. During a mean follow-up of 5.9 ± 1.2 years, 78 deaths occurred. Compared with the traditional RF alone (C-statistic 0.64), CCTA findings including coronary stenosis severity, plaque composition, and coronary segment location demonstrated improved incremental prognostic utility beyond traditional RF alone (C-statistics range 0.71-0.73, all P < 0.05; incremental χ2 range 20.7-25.5, all P < 0.001). However, no added prognostic benefit was offered by CCTA findings when added to a base model containing both traditional RF and CACS (C-statistics P > 0.05, for all). Coronary computed tomographic angiography improved prognostication of 6-year all-cause mortality beyond a set of conventional RF alone, although, no further incremental value was offered by CCTA when CCTA findings were added to a model incorporating RF and CACS.

Modeling Psychological Contract Violation using Dual Regime Models: An Event-based Approach

PubMed Central

Hofmans, Joeri

2017-01-01

A good understanding of the dynamics of psychological contract violation requires theories, research methods and statistical models that explicitly recognize that violation feelings follow from an event that violates one's acceptance limits, after which interpretative processes are set into motion, determining the intensity of these violation feelings. Whereas theories—in the form of the dynamic model of the psychological contract—and research methods—in the form of daily diary research and experience sampling research—are available by now, the statistical tools to model such a two-stage process are still lacking. The aim of the present paper is to fill this gap in the literature by introducing two statistical models—the Zero-Inflated model and the Hurdle model—that closely mimic the theoretical process underlying the elicitation violation feelings via two model components: a binary distribution that models whether violation has occurred or not, and a count distribution that models how severe the negative impact is. Moreover, covariates can be included for both model components separately, which yields insight into their unique and shared antecedents. By doing this, the present paper offers a methodological-substantive synergy, showing how sophisticated methodology can be used to examine an important substantive issue. PMID:29163316

Can spatial statistical river temperature models be transferred between catchments?

NASA Astrophysics Data System (ADS)

Jackson, Faye L.; Fryer, Robert J.; Hannah, David M.; Malcolm, Iain A.

2017-09-01

There has been increasing use of spatial statistical models to understand and predict river temperature (Tw) from landscape covariates. However, it is not financially or logistically feasible to monitor all rivers and the transferability of such models has not been explored. This paper uses Tw data from four river catchments collected in August 2015 to assess how well spatial regression models predict the maximum 7-day rolling mean of daily maximum Tw (Twmax) within and between catchments. Models were fitted for each catchment separately using (1) landscape covariates only (LS models) and (2) landscape covariates and an air temperature (Ta) metric (LS_Ta models). All the LS models included upstream catchment area and three included a river network smoother (RNS) that accounted for unexplained spatial structure. The LS models transferred reasonably to other catchments, at least when predicting relative levels of Twmax. However, the predictions were biased when mean Twmax differed between catchments. The RNS was needed to characterise and predict finer-scale spatially correlated variation. Because the RNS was unique to each catchment and thus non-transferable, predictions were better within catchments than between catchments. A single model fitted to all catchments found no interactions between the landscape covariates and catchment, suggesting that the landscape relationships were transferable. The LS_Ta models transferred less well, with particularly poor performance when the relationship with the Ta metric was physically implausible or required extrapolation outside the range of the data. A single model fitted to all catchments found catchment-specific relationships between Twmax and the Ta metric, indicating that the Ta metric was not transferable. These findings improve our understanding of the transferability of spatial statistical river temperature models and provide a foundation for developing new approaches for predicting Tw at unmonitored locations across multiple catchments and larger spatial scales.

Towards a General Turbulence Model for Planetary Boundary Layers Based on Direct Statistical Simulation

NASA Astrophysics Data System (ADS)

Skitka, J.; Marston, B.; Fox-Kemper, B.

2016-02-01

Sub-grid turbulence models for planetary boundary layers are typically constructed additively, starting with local flow properties and including non-local (KPP) or higher order (Mellor-Yamada) parameters until a desired level of predictive capacity is achieved or a manageable threshold of complexity is surpassed. Such approaches are necessarily limited in general circumstances, like global circulation models, by their being optimized for particular flow phenomena. By building a model reductively, starting with the infinite hierarchy of turbulence statistics, truncating at a given order, and stripping degrees of freedom from the flow, we offer the prospect a turbulence model and investigative tool that is equally applicable to all flow types and able to take full advantage of the wealth of nonlocal information in any flow. Direct statistical simulation (DSS) that is based upon expansion in equal-time cumulants can be used to compute flow statistics of arbitrary order. We investigate the feasibility of a second-order closure (CE2) by performing simulations of the ocean boundary layer in a quasi-linear approximation for which CE2 is exact. As oceanographic examples, wind-driven Langmuir turbulence and thermal convection are studied by comparison of the quasi-linear and fully nonlinear statistics. We also characterize the computational advantages and physical uncertainties of CE2 defined on a reduced basis determined via proper orthogonal decomposition (POD) of the flow fields.

Statistical methods in personality assessment research.

PubMed

Schinka, J A; LaLone, L; Broeckel, J A

1997-06-01

Emerging models of personality structure and advances in the measurement of personality and psychopathology suggest that research in personality and personality assessment has entered a stage of advanced development, in this article we examine whether researchers in these areas have taken advantage of new and evolving statistical procedures. We conducted a review of articles published in the Journal of Personality, Assessment during the past 5 years. Of the 449 articles that included some form of data analysis, 12.7% used only descriptive statistics, most employed only univariate statistics, and fewer than 10% used multivariate methods of data analysis. We discuss the cost of using limited statistical methods, the possible reasons for the apparent reluctance to employ advanced statistical procedures, and potential solutions to this technical shortcoming.

A reliability study on brain activation during active and passive arm movements supported by an MRI-compatible robot.

PubMed

Estévez, Natalia; Yu, Ningbo; Brügger, Mike; Villiger, Michael; Hepp-Reymond, Marie-Claude; Riener, Robert; Kollias, Spyros

2014-11-01

In neurorehabilitation, longitudinal assessment of arm movement related brain function in patients with motor disability is challenging due to variability in task performance. MRI-compatible robots monitor and control task performance, yielding more reliable evaluation of brain function over time. The main goals of the present study were first to define the brain network activated while performing active and passive elbow movements with an MRI-compatible arm robot (MaRIA) in healthy subjects, and second to test the reproducibility of this activation over time. For the fMRI analysis two models were compared. In model 1 movement onset and duration were included, whereas in model 2 force and range of motion were added to the analysis. Reliability of brain activation was tested with several statistical approaches applied on individual and group activation maps and on summary statistics. The activated network included mainly the primary motor cortex, primary and secondary somatosensory cortex, superior and inferior parietal cortex, medial and lateral premotor regions, and subcortical structures. Reliability analyses revealed robust activation for active movements with both fMRI models and all the statistical methods used. Imposed passive movements also elicited mainly robust brain activation for individual and group activation maps, and reliability was improved by including additional force and range of motion using model 2. These findings demonstrate that the use of robotic devices, such as MaRIA, can be useful to reliably assess arm movement related brain activation in longitudinal studies and may contribute in studies evaluating therapies and brain plasticity following injury in the nervous system.

Quantifying variation in speciation and extinction rates with clade data.

PubMed

Paradis, Emmanuel; Tedesco, Pablo A; Hugueny, Bernard

2013-12-01

High-level phylogenies are very common in evolutionary analyses, although they are often treated as incomplete data. Here, we provide statistical tools to analyze what we name "clade data," which are the ages of clades together with their numbers of species. We develop a general approach for the statistical modeling of variation in speciation and extinction rates, including temporal variation, unknown variation, and linear and nonlinear modeling. We show how this approach can be generalized to a wide range of situations, including testing the effects of life-history traits and environmental variables on diversification rates. We report the results of an extensive simulation study to assess the performance of some statistical tests presented here as well as of the estimators of speciation and extinction rates. These latter results suggest the possibility to estimate correctly extinction rate in the absence of fossils. An example with data on fish is presented. © 2013 The Author(s). Evolution © 2013 The Society for the Study of Evolution.

A hybrid model for traffic flow and crowd dynamics with random individual properties.

PubMed

Schleper, Veronika

2015-04-01

Based on an established mathematical model for the behavior of large crowds, a new model is derived that is able to take into account the statistical variation of individual maximum walking speeds. The same model is shown to be valid also in traffic flow situations, where for instance the statistical variation of preferred maximum speeds can be considered. The model involves explicit bounds on the state variables, such that a special Riemann solver is derived that is proved to respect the state constraints. Some care is devoted to a valid construction of random initial data, necessary for the use of the new model. The article also includes a numerical method that is shown to respect the bounds on the state variables and illustrative numerical examples, explaining the properties of the new model in comparison with established models.

National Centers for Environmental Prediction

Science.gov Websites

Statistics Observational Data Processing Data Assimilation Monsoon Desk Model Transition Seminars Seminar The Mesoscale Modeling Branch conducts a program of research and development in support of the prediction. This research and development includes mesoscale four-dimensional data assimilation of domestic

IDENTIFICATION OF REGIME SHIFTS IN TIME SERIES USING NEIGHBORHOOD STATISTICS

EPA Science Inventory

The identification of alternative dynamic regimes in ecological systems requires several lines of evidence. Previous work on time series analysis of dynamic regimes includes mainly model-fitting methods. We introduce two methods that do not use models. These approaches use state-...

Scientific computations section monthly report, November 1993

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

Buckner, M.R.

1993-12-30

This progress report from the Savannah River Technology Center contains abstracts from papers from the computational modeling, applied statistics, applied physics, experimental thermal hydraulics, and packaging and transportation groups. Specific topics covered include: engineering modeling and process simulation, criticality methods and analysis, plutonium disposition.

Validation of periodontitis screening model using sociodemographic, systemic, and molecular information in a Korean population.

PubMed

Kim, Hyun-Duck; Sukhbaatar, Munkhzaya; Shin, Myungseop; Ahn, Yoo-Been; Yoo, Wook-Sung

2014-12-01

This study aims to evaluate and validate a periodontitis screening model that includes sociodemographic, metabolic syndrome (MetS), and molecular information, including gingival crevicular fluid (GCF), matrix metalloproteinase (MMP), and blood cytokines. The authors selected 506 participants from the Shiwha-Banwol cohort: 322 participants from the 2005 cohort for deriving the screening model and 184 participants from the 2007 cohort for its validation. Periodontitis was assessed by dentists using the community periodontal index. Interleukin (IL)-6, IL-8, and tumor necrosis factor-α in blood and MMP-8, -9, and -13 in GCF were assayed using enzyme-linked immunosorbent assay. MetS was assessed by physicians using physical examination and blood laboratory data. Information about age, sex, income, smoking, and drinking was obtained by interview. Logistic regression analysis was applied to finalize the best-fitting model and validate the model using sensitivity, specificity, and c-statistics. The derived model for periodontitis screening had a sensitivity of 0.73, specificity of 0.85, and c-statistic of 0.86 (P <0.001); those of the validated model were 0.64, 0.91, and 0.83 (P <0.001), respectively. The model that included age, sex, income, smoking, drinking, and blood and GCF biomarkers could be useful in screening for periodontitis. A future prospective study is indicated for evaluating this model's ability to predict the occurrence of periodontitis.

A new model of physical evolution of Jupiter-family comets

NASA Astrophysics Data System (ADS)

Rickman, H.; Szutowicz, S.; Wójcikowski, K.

2014-07-01

We aim to find the statistical physical lifetimes of Jupiter Family comets. For this purpose, we try to model the processes that govern the dynamical and physical evolution of comets. We pay special attention to physical evolution; attempts at such modelling have been made before, but we propose a more accurate model, which will include more physical effects. The model is tested on a sample of fictitious comets based on real Jupiter Family comets with some orbital elements changed to a state before the capture by Jupiter. We model four different physical effects: erosion by sublimation, dust mantling, rejuvenation (mantle blow-off), and splitting. While for sublimation and splitting there already are some models, like di Sisto et. al. (2009), and we only wish to make them more accurate, dust mantling and rejuvenation have not been included in previous, statistical physical evolution models. Each of these effects depends on one or more tunable parameters, which we establish by choosing the model that best fits the observed comet sample in a way similar to di Sisto et. al. (2009). In contrast to di Sisto et. al., our comparison also involves the observed active fractions vs. nuclear radii.

Evaluation of the ecological relevance of mysid toxicity tests using population modeling techniques

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

Kuhn-Hines, A.; Munns, W.R. Jr.; Lussier, S.

1995-12-31

A number of acute and chronic bioassay statistics are used to evaluate the toxicity and risks of chemical stressors to the mysid shrimp, Mysidopsis bahia. These include LC{sub 50}S from acute tests, NOECs from 7-day and life-cycle tests, and the US EPA Water Quality Criteria Criterion Continuous Concentrations (CCC). Because these statistics are generated from endpoints which focus upon the responses of individual organisms, their relationships to significant effects at higher levels of ecological organization are unknown. This study was conducted to evaluate the quantitative relationships between toxicity test statistics and a concentration-based statistic derived from exposure-response models describing populationmore » growth rate ({lambda}) to stressor concentration. This statistic, C{sup {sm_bullet}} (concentration where {lambda} = I, zero population growth) describes the concentration above which mysid populations are projected to decline in abundance as determined using population modeling techniques. An analysis of M. bahia responses to 9 metals and 9 organic contaminants indicated the NOEC from life-cycle tests to be the best predictor of C{sup {sm_bullet}}, although the acute LC{sub 50} predicted population-level response surprisingly well. These analyses provide useful information regarding uncertainties of extrapolation among test statistics in assessments of ecological risk.« less

Assessing groundwater vulnerability to agrichemical contamination in the Midwest US

USGS Publications Warehouse

Burkart, M.R.; Kolpin, D.W.; James, D.E.

1999-01-01

Agrichemicals (herbicides and nitrate) are significant sources of diffuse pollution to groundwater. Indirect methods are needed to assess the potential for groundwater contamination by diffuse sources because groundwater monitoring is too costly to adequately define the geographic extent of contamination at a regional or national scale. This paper presents examples of the application of statistical, overlay and index, and process-based modeling methods for groundwater vulnerability assessments to a variety of data from the Midwest U.S. The principles for vulnerability assessment include both intrinsic (pedologic, climatologic, and hydrogeologic factors) and specific (contaminant and other anthropogenic factors) vulnerability of a location. Statistical methods use the frequency of contaminant occurrence, contaminant concentration, or contamination probability as a response variable. Statistical assessments are useful for defining the relations among explanatory and response variables whether they define intrinsic or specific vulnerability. Multivariate statistical analyses are useful for ranking variables critical to estimating water quality responses of interest. Overlay and index methods involve intersecting maps of intrinsic and specific vulnerability properties and indexing the variables by applying appropriate weights. Deterministic models use process-based equations to simulate contaminant transport and are distinguished from the other methods in their potential to predict contaminant transport in both space and time. An example of a one-dimensional leaching model linked to a geographic information system (GIS) to define a regional metamodel for contamination in the Midwest is included.

Summary goodness-of-fit statistics for binary generalized linear models with noncanonical link functions.

PubMed

Canary, Jana D; Blizzard, Leigh; Barry, Ronald P; Hosmer, David W; Quinn, Stephen J

2016-05-01

Generalized linear models (GLM) with a canonical logit link function are the primary modeling technique used to relate a binary outcome to predictor variables. However, noncanonical links can offer more flexibility, producing convenient analytical quantities (e.g., probit GLMs in toxicology) and desired measures of effect (e.g., relative risk from log GLMs). Many summary goodness-of-fit (GOF) statistics exist for logistic GLM. Their properties make the development of GOF statistics relatively straightforward, but it can be more difficult under noncanonical links. Although GOF tests for logistic GLM with continuous covariates (GLMCC) have been applied to GLMCCs with log links, we know of no GOF tests in the literature specifically developed for GLMCCs that can be applied regardless of link function chosen. We generalize the Tsiatis GOF statistic originally developed for logistic GLMCCs, (TG), so that it can be applied under any link function. Further, we show that the algebraically related Hosmer-Lemeshow (HL) and Pigeon-Heyse (J(2) ) statistics can be applied directly. In a simulation study, TG, HL, and J(2) were used to evaluate the fit of probit, log-log, complementary log-log, and log models, all calculated with a common grouping method. The TG statistic consistently maintained Type I error rates, while those of HL and J(2) were often lower than expected if terms with little influence were included. Generally, the statistics had similar power to detect an incorrect model. An exception occurred when a log GLMCC was incorrectly fit to data generated from a logistic GLMCC. In this case, TG had more power than HL or J(2) . © 2015 John Wiley & Sons Ltd/London School of Economics.

A first principles calculation and statistical mechanics modeling of defects in Al-H system

NASA Astrophysics Data System (ADS)

Ji, Min; Wang, Cai-Zhuang; Ho, Kai-Ming

2007-03-01

The behavior of defects and hydrogen in Al was investigated by first principles calculations and statistical mechanics modeling. The formation energy of different defects in Al+H system such as Al vacancy, H in institution and multiple H in Al vacancy were calculated by first principles method. Defect concentration in thermodynamical equilibrium was studied by total free energy calculation including configuration entropy and defect-defect interaction from low concentration limit to hydride limit. In our grand canonical ensemble model, hydrogen chemical potential under different environment plays an important role in determing the defect concentration and properties in Al-H system.

A generalized Benford's law for JPEG coefficients and its applications in image forensics

NASA Astrophysics Data System (ADS)

Fu, Dongdong; Shi, Yun Q.; Su, Wei

2007-02-01

In this paper, a novel statistical model based on Benford's law for the probability distributions of the first digits of the block-DCT and quantized JPEG coefficients is presented. A parametric logarithmic law, i.e., the generalized Benford's law, is formulated. Furthermore, some potential applications of this model in image forensics are discussed in this paper, which include the detection of JPEG compression for images in bitmap format, the estimation of JPEG compression Qfactor for JPEG compressed bitmap image, and the detection of double compressed JPEG image. The results of our extensive experiments demonstrate the effectiveness of the proposed statistical model.

Power Analysis for Complex Mediational Designs Using Monte Carlo Methods

ERIC Educational Resources Information Center

Thoemmes, Felix; MacKinnon, David P.; Reiser, Mark R.

2010-01-01

Applied researchers often include mediation effects in applications of advanced methods such as latent variable models and linear growth curve models. Guidance on how to estimate statistical power to detect mediation for these models has not yet been addressed in the literature. We describe a general framework for power analyses for complex…

Acceleration techniques for dependability simulation. M.S. Thesis

NASA Technical Reports Server (NTRS)

Barnette, James David

1995-01-01

As computer systems increase in complexity, the need to project system performance from the earliest design and development stages increases. We have to employ simulation for detailed dependability studies of large systems. However, as the complexity of the simulation model increases, the time required to obtain statistically significant results also increases. This paper discusses an approach that is application independent and can be readily applied to any process-based simulation model. Topics include background on classical discrete event simulation and techniques for random variate generation and statistics gathering to support simulation.

Reciprocity in directed networks

NASA Astrophysics Data System (ADS)

Yin, Mei; Zhu, Lingjiong

2016-04-01

Reciprocity is an important characteristic of directed networks and has been widely used in the modeling of World Wide Web, email, social, and other complex networks. In this paper, we take a statistical physics point of view and study the limiting entropy and free energy densities from the microcanonical ensemble, the canonical ensemble, and the grand canonical ensemble whose sufficient statistics are given by edge and reciprocal densities. The sparse case is also studied for the grand canonical ensemble. Extensions to more general reciprocal models including reciprocal triangle and star densities will likewise be discussed.

Bayesian Decision Support

NASA Astrophysics Data System (ADS)

Berliner, M.

2017-12-01

Bayesian statistical decision theory offers a natural framework for decision-policy making in the presence of uncertainty. Key advantages of the approach include efficient incorporation of information and observations. However, in complicated settings it is very difficult, perhaps essentially impossible, to formalize the mathematical inputs needed in the approach. Nevertheless, using the approach as a template is useful for decision support; that is, organizing and communicating our analyses. Bayesian hierarchical modeling is valuable in quantifying and managing uncertainty such cases. I review some aspects of the idea emphasizing statistical model development and use in the context of sea-level rise.

Walking through the statistical black boxes of plant breeding.

PubMed

Xavier, Alencar; Muir, William M; Craig, Bruce; Rainey, Katy Martin

2016-10-01

The main statistical procedures in plant breeding are based on Gaussian process and can be computed through mixed linear models. Intelligent decision making relies on our ability to extract useful information from data to help us achieve our goals more efficiently. Many plant breeders and geneticists perform statistical analyses without understanding the underlying assumptions of the methods or their strengths and pitfalls. In other words, they treat these statistical methods (software and programs) like black boxes. Black boxes represent complex pieces of machinery with contents that are not fully understood by the user. The user sees the inputs and outputs without knowing how the outputs are generated. By providing a general background on statistical methodologies, this review aims (1) to introduce basic concepts of machine learning and its applications to plant breeding; (2) to link classical selection theory to current statistical approaches; (3) to show how to solve mixed models and extend their application to pedigree-based and genomic-based prediction; and (4) to clarify how the algorithms of genome-wide association studies work, including their assumptions and limitations.

A statistical shape model of the human second cervical vertebra.

PubMed

Clogenson, Marine; Duff, John M; Luethi, Marcel; Levivier, Marc; Meuli, Reto; Baur, Charles; Henein, Simon

2015-07-01

Statistical shape and appearance models play an important role in reducing the segmentation processing time of a vertebra and in improving results for 3D model development. Here, we describe the different steps in generating a statistical shape model (SSM) of the second cervical vertebra (C2) and provide the shape model for general use by the scientific community. The main difficulties in its construction are the morphological complexity of the C2 and its variability in the population. The input dataset is composed of manually segmented anonymized patient computerized tomography (CT) scans. The alignment of the different datasets is done with the procrustes alignment on surface models, and then, the registration is cast as a model-fitting problem using a Gaussian process. A principal component analysis (PCA)-based model is generated which includes the variability of the C2. The SSM was generated using 92 CT scans. The resulting SSM was evaluated for specificity, compactness and generalization ability. The SSM of the C2 is freely available to the scientific community in Slicer (an open source software for image analysis and scientific visualization) with a module created to visualize the SSM using Statismo, a framework for statistical shape modeling. The SSM of the vertebra allows the shape variability of the C2 to be represented. Moreover, the SSM will enable semi-automatic segmentation and 3D model generation of the vertebra, which would greatly benefit surgery planning.

Wireless Channel Characterization: Modeling the 5 GHz Microwave Landing System Extension Band for Future Airport Surface Communications

NASA Technical Reports Server (NTRS)

Matolak, D. W.; Apaza, Rafael; Foore, Lawrence R.

2006-01-01

We describe a recently completed wideband wireless channel characterization project for the 5 GHz Microwave Landing System (MLS) extension band, for airport surface areas. This work included mobile measurements at large and small airports, and fixed point-to-point measurements. Mobile measurements were made via transmission from the air traffic control tower (ATCT), or from an airport field site (AFS), to a receiving ground vehicle on the airport surface. The point-to-point measurements were between ATCT and AFSs. Detailed statistical channel models were developed from all these measurements. Measured quantities include propagation path loss and power delay profiles, from which we obtain delay spreads, frequency domain correlation (coherence bandwidths), fading amplitude statistics, and channel parameter correlations. In this paper we review the project motivation, measurement coordination, and illustrate measurement results. Example channel modeling results for several propagation conditions are also provided, highlighting new findings.

Magnetospheric space plasma investigations

NASA Technical Reports Server (NTRS)

Comfort, Richard H.; Horwitz, James L.

1994-01-01

A time dependent semi-kinetic model that includes self collisions and ion-neutral collisions and chemistry was developed. Light ion outflow in the polar cap transition region was modeled and compared with data results. A model study of wave heating of O+ ions in the topside transition region was carried out using a code which does local calculations that include ion-neutral and Coulomb self collisions as well as production and loss of O+. Another project is a statistical study of hydrogen spin curve characteristics in the polar cap. A statistical study of the latitudinal distribution of core plasmas along the L=4.6 field line using DE-1/RIMS data was completed. A short paper on dual spacecraft estimates of ion temperature profiles and heat flows in the plasmasphere ionosphere system was prepared. An automated processing code was used to process RIMS data from 1981 to 1984.

Modeling radiation belt electron dynamics during GEM challenge intervals with the DREAM3D diffusion model

NASA Astrophysics Data System (ADS)

Tu, Weichao; Cunningham, G. S.; Chen, Y.; Henderson, M. G.; Camporeale, E.; Reeves, G. D.

2013-10-01

a response to the Geospace Environment Modeling (GEM) "Global Radiation Belt Modeling Challenge," a 3D diffusion model is used to simulate the radiation belt electron dynamics during two intervals of the Combined Release and Radiation Effects Satellite (CRRES) mission, 15 August to 15 October 1990 and 1 February to 31 July 1991. The 3D diffusion model, developed as part of the Dynamic Radiation Environment Assimilation Model (DREAM) project, includes radial, pitch angle, and momentum diffusion and mixed pitch angle-momentum diffusion, which are driven by dynamic wave databases from the statistical CRRES wave data, including plasmaspheric hiss, lower-band, and upper-band chorus. By comparing the DREAM3D model outputs to the CRRES electron phase space density (PSD) data, we find that, with a data-driven boundary condition at Lmax = 5.5, the electron enhancements can generally be explained by radial diffusion, though additional local heating from chorus waves is required. Because the PSD reductions are included in the boundary condition at Lmax = 5.5, our model captures the fast electron dropouts over a large L range, producing better model performance compared to previous published results. Plasmaspheric hiss produces electron losses inside the plasmasphere, but the model still sometimes overestimates the PSD there. Test simulations using reduced radial diffusion coefficients or increased pitch angle diffusion coefficients inside the plasmasphere suggest that better wave models and more realistic radial diffusion coefficients, both inside and outside the plasmasphere, are needed to improve the model performance. Statistically, the results show that, with the data-driven outer boundary condition, including radial diffusion and plasmaspheric hiss is sufficient to model the electrons during geomagnetically quiet times, but to best capture the radiation belt variations during active times, pitch angle and momentum diffusion from chorus waves are required.

On-line estimation of error covariance parameters for atmospheric data assimilation

NASA Technical Reports Server (NTRS)

Dee, Dick P.

1995-01-01

A simple scheme is presented for on-line estimation of covariance parameters in statistical data assimilation systems. The scheme is based on a maximum-likelihood approach in which estimates are produced on the basis of a single batch of simultaneous observations. Simple-sample covariance estimation is reasonable as long as the number of available observations exceeds the number of tunable parameters by two or three orders of magnitude. Not much is known at present about model error associated with actual forecast systems. Our scheme can be used to estimate some important statistical model error parameters such as regionally averaged variances or characteristic correlation length scales. The advantage of the single-sample approach is that it does not rely on any assumptions about the temporal behavior of the covariance parameters: time-dependent parameter estimates can be continuously adjusted on the basis of current observations. This is of practical importance since it is likely to be the case that both model error and observation error strongly depend on the actual state of the atmosphere. The single-sample estimation scheme can be incorporated into any four-dimensional statistical data assimilation system that involves explicit calculation of forecast error covariances, including optimal interpolation (OI) and the simplified Kalman filter (SKF). The computational cost of the scheme is high but not prohibitive; on-line estimation of one or two covariance parameters in each analysis box of an operational bozed-OI system is currently feasible. A number of numerical experiments performed with an adaptive SKF and an adaptive version of OI, using a linear two-dimensional shallow-water model and artificially generated model error are described. The performance of the nonadaptive versions of these methods turns out to depend rather strongly on correct specification of model error parameters. These parameters are estimated under a variety of conditions, including uniformly distributed model error and time-dependent model error statistics.

Body size affects the strength of social interactions and spatial organization of a schooling fish (Pseudomugil signifer)

NASA Astrophysics Data System (ADS)

Romenskyy, Maksym; Herbert-Read, James E.; Ward, Ashley J. W.; Sumpter, David J. T.

2017-04-01

While a rich variety of self-propelled particle models propose to explain the collective motion of fish and other animals, rigorous statistical comparison between models and data remains a challenge. Plausible models should be flexible enough to capture changes in the collective behaviour of animal groups at their different developmental stages and group sizes. Here, we analyse the statistical properties of schooling fish (Pseudomugil signifer) through a combination of experiments and simulations. We make novel use of a Boltzmann inversion method, usually applied in molecular dynamics, to identify the effective potential of the mean force of fish interactions. Specifically, we show that larger fish have a larger repulsion zone, but stronger attraction, resulting in greater alignment in their collective motion. We model the collective dynamics of schools using a self-propelled particle model, modified to include varying particle speed and a local repulsion rule. We demonstrate that the statistical properties of the fish schools are reproduced by our model, thereby capturing a number of features of the behaviour and development of schooling fish.

Quantitative investigation of inappropriate regression model construction and the importance of medical statistics experts in observational medical research: a cross-sectional study.

PubMed

Nojima, Masanori; Tokunaga, Mutsumi; Nagamura, Fumitaka

2018-05-05

To investigate under what circumstances inappropriate use of 'multivariate analysis' is likely to occur and to identify the population that needs more support with medical statistics. The frequency of inappropriate regression model construction in multivariate analysis and related factors were investigated in observational medical research publications. The inappropriate algorithm of using only variables that were significant in univariate analysis was estimated to occur at 6.4% (95% CI 4.8% to 8.5%). This was observed in 1.1% of the publications with a medical statistics expert (hereinafter 'expert') as the first author, 3.5% if an expert was included as coauthor and in 12.2% if experts were not involved. In the publications where the number of cases was 50 or less and the study did not include experts, inappropriate algorithm usage was observed with a high proportion of 20.2%. The OR of the involvement of experts for this outcome was 0.28 (95% CI 0.15 to 0.53). A further, nation-level, analysis showed that the involvement of experts and the implementation of unfavourable multivariate analysis are associated at the nation-level analysis (R=-0.652). Based on the results of this study, the benefit of participation of medical statistics experts is obvious. Experts should be involved for proper confounding adjustment and interpretation of statistical models. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Effectiveness of feature and classifier algorithms in character recognition systems

NASA Astrophysics Data System (ADS)

Wilson, Charles L.

1993-04-01

At the first Census Optical Character Recognition Systems Conference, NIST generated accuracy data for more than character recognition systems. Most systems were tested on the recognition of isolated digits and upper and lower case alphabetic characters. The recognition experiments were performed on sample sizes of 58,000 digits, and 12,000 upper and lower case alphabetic characters. The algorithms used by the 26 conference participants included rule-based methods, image-based methods, statistical methods, and neural networks. The neural network methods included Multi-Layer Perceptron's, Learned Vector Quantitization, Neocognitrons, and cascaded neural networks. In this paper 11 different systems are compared using correlations between the answers of different systems, comparing the decrease in error rate as a function of confidence of recognition, and comparing the writer dependence of recognition. This comparison shows that methods that used different algorithms for feature extraction and recognition performed with very high levels of correlation. This is true for neural network systems, hybrid systems, and statistically based systems, and leads to the conclusion that neural networks have not yet demonstrated a clear superiority to more conventional statistical methods. Comparison of these results with the models of Vapnick (for estimation problems), MacKay (for Bayesian statistical models), Moody (for effective parameterization), and Boltzmann models (for information content) demonstrate that as the limits of training data variance are approached, all classifier systems have similar statistical properties. The limiting condition can only be approached for sufficiently rich feature sets because the accuracy limit is controlled by the available information content of the training set, which must pass through the feature extraction process prior to classification.

Turbulent scaling laws as solutions of the multi-point correlation equation using statistical symmetries

NASA Astrophysics Data System (ADS)

Oberlack, Martin; Rosteck, Andreas; Avsarkisov, Victor

2013-11-01

Text-book knowledge proclaims that Lie symmetries such as Galilean transformation lie at the heart of fluid dynamics. These important properties also carry over to the statistical description of turbulence, i.e. to the Reynolds stress transport equations and its generalization, the multi-point correlation equations (MPCE). Interesting enough, the MPCE admit a much larger set of symmetries, in fact infinite dimensional, subsequently named statistical symmetries. Most important, theses new symmetries have important consequences for our understanding of turbulent scaling laws. The symmetries form the essential foundation to construct exact solutions to the infinite set of MPCE, which in turn are identified as classical and new turbulent scaling laws. Examples on various classical and new shear flow scaling laws including higher order moments will be presented. Even new scaling have been forecasted from these symmetries and in turn validated by DNS. Turbulence modellers have implicitly recognized at least one of the statistical symmetries as this is the basis for the usual log-law which has been employed for calibrating essentially all engineering turbulence models. An obvious conclusion is to generally make turbulence models consistent with the new statistical symmetries.

Adopting adequate leaching requirement for practical response models of basil to salinity

NASA Astrophysics Data System (ADS)

Babazadeh, Hossein; Tabrizi, Mahdi Sarai; Darvishi, Hossein Hassanpour

2016-07-01

Several mathematical models are being used for assessing plant response to salinity of the root zone. Objectives of this study included quantifying the yield salinity threshold value of basil plants to irrigation water salinity and investigating the possibilities of using irrigation water salinity instead of saturated extract salinity in the available mathematical models for estimating yield. To achieve the above objectives, an extensive greenhouse experiment was conducted with 13 irrigation water salinity levels, namely 1.175 dS m-1 (control treatment) and 1.8 to 10 dS m-1. The result indicated that, among these models, the modified discount model (one of the most famous root water uptake model which is based on statistics) produced more accurate results in simulating the basil yield reduction function using irrigation water salinities. Overall the statistical model of Steppuhn et al. on the modified discount model and the math-empirical model of van Genuchten and Hoffman provided the best results. In general, all of the statistical models produced very similar results and their results were better than math-empirical models. It was also concluded that if enough leaching was present, there was no significant difference between the soil salinity saturated extract models and the models using irrigation water salinity.

A Statistical Model for Estimation of Fish Density Including Correlation in Size, Space, Time and between Species from Research Survey Data

PubMed Central

Bastardie, Francois

2014-01-01

Trawl survey data with high spatial and seasonal coverage were analysed using a variant of the Log Gaussian Cox Process (LGCP) statistical model to estimate unbiased relative fish densities. The model estimates correlations between observations according to time, space, and fish size and includes zero observations and over-dispersion. The model utilises the fact the correlation between numbers of fish caught increases when the distance in space and time between the fish decreases, and the correlation between size groups in a haul increases when the difference in size decreases. Here the model is extended in two ways. Instead of assuming a natural scale size correlation, the model is further developed to allow for a transformed length scale. Furthermore, in the present application, the spatial- and size-dependent correlation between species was included. For cod (Gadus morhua) and whiting (Merlangius merlangus), a common structured size correlation was fitted, and a separable structure between the time and space-size correlation was found for each species, whereas more complex structures were required to describe the correlation between species (and space-size). The within-species time correlation is strong, whereas the correlations between the species are weaker over time but strong within the year. PMID:24911631

Magnification Bias in Gravitational Arc Statistics

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

Caminha, G. B.; Estrada, J.; Makler, M.

2013-08-29

The statistics of gravitational arcs in galaxy clusters is a powerful probe of cluster structure and may provide complementary cosmological constraints. Despite recent progresses, discrepancies still remain among modelling and observations of arc abundance, specially regarding the redshift distribution of strong lensing clusters. Besides, fast "semi-analytic" methods still have to incorporate the success obtained with simulations. In this paper we discuss the contribution of the magnification in gravitational arc statistics. Although lensing conserves surface brightness, the magnification increases the signal-to-noise ratio of the arcs, enhancing their detectability. We present an approach to include this and other observational effects in semi-analyticmore » calculations for arc statistics. The cross section for arc formation ({\\sigma}) is computed through a semi-analytic method based on the ratio of the eigenvalues of the magnification tensor. Using this approach we obtained the scaling of {\\sigma} with respect to the magnification, and other parameters, allowing for a fast computation of the cross section. We apply this method to evaluate the expected number of arcs per cluster using an elliptical Navarro--Frenk--White matter distribution. Our results show that the magnification has a strong effect on the arc abundance, enhancing the fraction of arcs, moving the peak of the arc fraction to higher redshifts, and softening its decrease at high redshifts. We argue that the effect of magnification should be included in arc statistics modelling and that it could help to reconcile arcs statistics predictions with the observational data.« less

MODELING FISH AND SHELLFISH DISTRIBUTIONS IN THE MOBILE BAY ESTUARY, USA

EPA Science Inventory

Estuaries in the Gulf of Mexico provide rich habitat for many fish and shellfish, including those that have been identified as economically and ecologically important. For the Mobile Bay estuary, we developed statistical models to relate distributions of individual species and sp...

Response statistics of rotating shaft with non-linear elastic restoring forces by path integration

NASA Astrophysics Data System (ADS)

Gaidai, Oleg; Naess, Arvid; Dimentberg, Michael

2017-07-01

Extreme statistics of random vibrations is studied for a Jeffcott rotor under uniaxial white noise excitation. Restoring force is modelled as elastic non-linear; comparison is done with linearized restoring force to see the force non-linearity effect on the response statistics. While for the linear model analytical solutions and stability conditions are available, it is not generally the case for non-linear system except for some special cases. The statistics of non-linear case is studied by applying path integration (PI) method, which is based on the Markov property of the coupled dynamic system. The Jeffcott rotor response statistics can be obtained by solving the Fokker-Planck (FP) equation of the 4D dynamic system. An efficient implementation of PI algorithm is applied, namely fast Fourier transform (FFT) is used to simulate dynamic system additive noise. The latter allows significantly reduce computational time, compared to the classical PI. Excitation is modelled as Gaussian white noise, however any kind distributed white noise can be implemented with the same PI technique. Also multidirectional Markov noise can be modelled with PI in the same way as unidirectional. PI is accelerated by using Monte Carlo (MC) estimated joint probability density function (PDF) as initial input. Symmetry of dynamic system was utilized to afford higher mesh resolution. Both internal (rotating) and external damping are included in mechanical model of the rotor. The main advantage of using PI rather than MC is that PI offers high accuracy in the probability distribution tail. The latter is of critical importance for e.g. extreme value statistics, system reliability, and first passage probability.

The impact on midlevel vision of statistically optimal divisive normalization in V1.

PubMed

Coen-Cagli, Ruben; Schwartz, Odelia

2013-07-15

The first two areas of the primate visual cortex (V1, V2) provide a paradigmatic example of hierarchical computation in the brain. However, neither the functional properties of V2 nor the interactions between the two areas are well understood. One key aspect is that the statistics of the inputs received by V2 depend on the nonlinear response properties of V1. Here, we focused on divisive normalization, a canonical nonlinear computation that is observed in many neural areas and modalities. We simulated V1 responses with (and without) different forms of surround normalization derived from statistical models of natural scenes, including canonical normalization and a statistically optimal extension that accounted for image nonhomogeneities. The statistics of the V1 population responses differed markedly across models. We then addressed how V2 receptive fields pool the responses of V1 model units with different tuning. We assumed this is achieved by learning without supervision a linear representation that removes correlations, which could be accomplished with principal component analysis. This approach revealed V2-like feature selectivity when we used the optimal normalization and, to a lesser extent, the canonical one but not in the absence of both. We compared the resulting two-stage models on two perceptual tasks; while models encompassing V1 surround normalization performed better at object recognition, only statistically optimal normalization provided systematic advantages in a task more closely matched to midlevel vision, namely figure/ground judgment. Our results suggest that experiments probing midlevel areas might benefit from using stimuli designed to engage the computations that characterize V1 optimality.

Preparing systems engineering and computing science students in disciplined methods, quantitative, and advanced statistical techniques to improve process performance

NASA Astrophysics Data System (ADS)

McCray, Wilmon Wil L., Jr.

The research was prompted by a need to conduct a study that assesses process improvement, quality management and analytical techniques taught to students in U.S. colleges and universities undergraduate and graduate systems engineering and the computing science discipline (e.g., software engineering, computer science, and information technology) degree programs during their academic training that can be applied to quantitatively manage processes for performance. Everyone involved in executing repeatable processes in the software and systems development lifecycle processes needs to become familiar with the concepts of quantitative management, statistical thinking, process improvement methods and how they relate to process-performance. Organizations are starting to embrace the de facto Software Engineering Institute (SEI) Capability Maturity Model Integration (CMMI RTM) Models as process improvement frameworks to improve business processes performance. High maturity process areas in the CMMI model imply the use of analytical, statistical, quantitative management techniques, and process performance modeling to identify and eliminate sources of variation, continually improve process-performance; reduce cost and predict future outcomes. The research study identifies and provides a detail discussion of the gap analysis findings of process improvement and quantitative analysis techniques taught in U.S. universities systems engineering and computing science degree programs, gaps that exist in the literature, and a comparison analysis which identifies the gaps that exist between the SEI's "healthy ingredients " of a process performance model and courses taught in U.S. universities degree program. The research also heightens awareness that academicians have conducted little research on applicable statistics and quantitative techniques that can be used to demonstrate high maturity as implied in the CMMI models. The research also includes a Monte Carlo simulation optimization model and dashboard that demonstrates the use of statistical methods, statistical process control, sensitivity analysis, quantitative and optimization techniques to establish a baseline and predict future customer satisfaction index scores (outcomes). The American Customer Satisfaction Index (ACSI) model and industry benchmarks were used as a framework for the simulation model.

Discharge destination following lower limb fracture: development of a prediction model to assist with decision making.

PubMed

Kimmel, Lara A; Holland, Anne E; Edwards, Elton R; Cameron, Peter A; De Steiger, Richard; Page, Richard S; Gabbe, Belinda

2012-06-01

Accurate prediction of the likelihood of discharge to inpatient rehabilitation following lower limb fracture made on admission to hospital may assist patient discharge planning and decrease the burden on the hospital system caused by delays in decision making. To develop a prognostic model for discharge to inpatient rehabilitation. Isolated lower extremity fracture cases (excluding fractured neck of femur), captured by the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), were extracted for analysis. A training data set was created for model development and validation data set for evaluation. A multivariable logistic regression model was developed based on patient and injury characteristics. Models were assessed using measures of discrimination (C-statistic) and calibration (Hosmer-Lemeshow (H-L) statistic). A total of 1429 patients met the inclusion criteria and were randomly split into training and test data sets. Increasing age, more proximal fracture type, compensation or private fund source for the admission, metropolitan location of residence, not working prior to injury and having a self-reported pre-injury disability were included in the final prediction model. The C-statistic for the model was 0.92 (95% confidence interval (CI) 0.88, 0.95) with an H-L statistic of χ(2)=11.62, p=0.17. For the test data set, the C-statistic was 0.86 (95% CI 0.83, 0.90) with an H-L statistic of χ(2)=37.98, p<0.001. A model to predict discharge to inpatient rehabilitation following lower limb fracture was developed with excellent discrimination although the calibration was reduced in the test data set. This model requires prospective testing but could form an integral part of decision making in regards to discharge disposition to facilitate timely and accurate referral to rehabilitation and optimise resource allocation. Copyright © 2011 Elsevier Ltd. All rights reserved.

Statistical procedures for evaluating daily and monthly hydrologic model predictions

USGS Publications Warehouse

Coffey, M.E.; Workman, S.R.; Taraba, J.L.; Fogle, A.W.

2004-01-01

The overall study objective was to evaluate the applicability of different qualitative and quantitative methods for comparing daily and monthly SWAT computer model hydrologic streamflow predictions to observed data, and to recommend statistical methods for use in future model evaluations. Statistical methods were tested using daily streamflows and monthly equivalent runoff depths. The statistical techniques included linear regression, Nash-Sutcliffe efficiency, nonparametric tests, t-test, objective functions, autocorrelation, and cross-correlation. None of the methods specifically applied to the non-normal distribution and dependence between data points for the daily predicted and observed data. Of the tested methods, median objective functions, sign test, autocorrelation, and cross-correlation were most applicable for the daily data. The robust coefficient of determination (CD*) and robust modeling efficiency (EF*) objective functions were the preferred methods for daily model results due to the ease of comparing these values with a fixed ideal reference value of one. Predicted and observed monthly totals were more normally distributed, and there was less dependence between individual monthly totals than was observed for the corresponding predicted and observed daily values. More statistical methods were available for comparing SWAT model-predicted and observed monthly totals. The 1995 monthly SWAT model predictions and observed data had a regression Rr2 of 0.70, a Nash-Sutcliffe efficiency of 0.41, and the t-test failed to reject the equal data means hypothesis. The Nash-Sutcliffe coefficient and the R r2 coefficient were the preferred methods for monthly results due to the ability to compare these coefficients to a set ideal value of one.

Two statistics for evaluating parameter identifiability and error reduction

USGS Publications Warehouse

Doherty, John; Hunt, Randall J.

2009-01-01

Two statistics are presented that can be used to rank input parameters utilized by a model in terms of their relative identifiability based on a given or possible future calibration dataset. Identifiability is defined here as the capability of model calibration to constrain parameters used by a model. Both statistics require that the sensitivity of each model parameter be calculated for each model output for which there are actual or presumed field measurements. Singular value decomposition (SVD) of the weighted sensitivity matrix is then undertaken to quantify the relation between the parameters and observations that, in turn, allows selection of calibration solution and null spaces spanned by unit orthogonal vectors. The first statistic presented, "parameter identifiability", is quantitatively defined as the direction cosine between a parameter and its projection onto the calibration solution space. This varies between zero and one, with zero indicating complete non-identifiability and one indicating complete identifiability. The second statistic, "relative error reduction", indicates the extent to which the calibration process reduces error in estimation of a parameter from its pre-calibration level where its value must be assigned purely on the basis of prior expert knowledge. This is more sophisticated than identifiability, in that it takes greater account of the noise associated with the calibration dataset. Like identifiability, it has a maximum value of one (which can only be achieved if there is no measurement noise). Conceptually it can fall to zero; and even below zero if a calibration problem is poorly posed. An example, based on a coupled groundwater/surface-water model, is included that demonstrates the utility of the statistics. ?? 2009 Elsevier B.V.

Statistical-Dynamical Seasonal Forecasts of Central-Southwest Asian Winter Precipitation.

NASA Astrophysics Data System (ADS)

Tippett, Michael K.; Goddard, Lisa; Barnston, Anthony G.

2005-06-01

Interannual precipitation variability in central-southwest (CSW) Asia has been associated with East Asian jet stream variability and western Pacific tropical convection. However, atmospheric general circulation models (AGCMs) forced by observed sea surface temperature (SST) poorly simulate the region's interannual precipitation variability. The statistical-dynamical approach uses statistical methods to correct systematic deficiencies in the response of AGCMs to SST forcing. Statistical correction methods linking model-simulated Indo-west Pacific precipitation and observed CSW Asia precipitation result in modest, but statistically significant, cross-validated simulation skill in the northeast part of the domain for the period from 1951 to 1998. The statistical-dynamical method is also applied to recent (winter 1998/99 to 2002/03) multimodel, two-tier December-March precipitation forecasts initiated in October. This period includes 4 yr (winter of 1998/99 to 2001/02) of severe drought. Tercile probability forecasts are produced using ensemble-mean forecasts and forecast error estimates. The statistical-dynamical forecasts show enhanced probability of below-normal precipitation for the four drought years and capture the return to normal conditions in part of the region during the winter of 2002/03.May Kabul be without gold, but not without snow.—Traditional Afghan proverb

A call to improve methods for estimating tree biomass for regional and national assessments

Treesearch

Aaron R. Weiskittel; David W. MacFarlane; Philip J. Radtke; David L.R. Affleck; Hailemariam Temesgen; Christopher W. Woodall; James A. Westfall; John W. Coulston

2015-01-01

Tree biomass is typically estimated using statistical models. This review highlights five limitations of most tree biomass models, which include the following: (1) biomass data are costly to collect and alternative sampling methods are used; (2) belowground data and models are generally lacking; (3) models are often developed from small and geographically limited data...

A Comparison of Item Fit Statistics for Mixed IRT Models

ERIC Educational Resources Information Center

Chon, Kyong Hee; Lee, Won-Chan; Dunbar, Stephen B.

2010-01-01

In this study we examined procedures for assessing model-data fit of item response theory (IRT) models for mixed format data. The model fit indices used in this study include PARSCALE's G[superscript 2], Orlando and Thissen's S-X[superscript 2] and S-G[superscript 2], and Stone's chi[superscript 2*] and G[superscript 2*]. To investigate the…

The Impact of Model Misspecification on Parameter Estimation and Item-Fit Assessment in Log-Linear Diagnostic Classification Models

ERIC Educational Resources Information Center

Kunina-Habenicht, Olga; Rupp, Andre A.; Wilhelm, Oliver

2012-01-01

Using a complex simulation study we investigated parameter recovery, classification accuracy, and performance of two item-fit statistics for correct and misspecified diagnostic classification models within a log-linear modeling framework. The basic manipulated test design factors included the number of respondents (1,000 vs. 10,000), attributes (3…

Intercomparison of four regional climate models for the German State of Saxonia

NASA Astrophysics Data System (ADS)

Kreienkamp, F.; Spekat, A.; Enke, W.

2009-09-01

Results from four regional climate models which focus on Central Europe are presented: CCLM, the climate version of the German Weather Service's Local Model - REMO, the regional dynamic model from the Max Planck Institute for Meteorology in Hamburg - STAR, the statistical model developed at the PIK Potsdam Institute and WETTREG, the statistic-dynamic model developed by the company CEC Potsdam. For the area of the German State of Saxonia a host of properties and indicators were analyzed aiming to show the models' abilities to reconstruct the current climate and compare climate model scenarios. These include a group of thermal indicators, such as the number of ice, frost, summer and hot days, the number of tropical nights; then there are hydrometeorological indicators such as the exceedance of low and high precipitation thresholds; humidity, cloudiness and wind indicators complement the array. A selection of them showing similarities and differences of the models investigated will be presented.

Indiana chronic disease management program risk stratification analysis.

PubMed

Li, Jingjin; Holmes, Ann M; Rosenman, Marc B; Katz, Barry P; Downs, Stephen M; Murray, Michael D; Ackermann, Ronald T; Inui, Thomas S

2005-10-01

The objective of this study was to compare the ability of risk stratification models derived from administrative data to classify groups of patients for enrollment in a tailored chronic disease management program. This study included 19,548 Medicaid patients with chronic heart failure or diabetes in the Indiana Medicaid data warehouse during 2001 and 2002. To predict costs (total claims paid) in FY 2002, we considered candidate predictor variables available in FY 2001, including patient characteristics, the number and type of prescription medications, laboratory tests, pharmacy charges, and utilization of primary, specialty, inpatient, emergency department, nursing home, and home health care. We built prospective models to identify patients with different levels of expenditure. Model fit was assessed using R statistics, whereas discrimination was assessed using the weighted kappa statistic, predictive ratios, and the area under the receiver operating characteristic curve. We found a simple least-squares regression model in which logged total charges in FY 2002 were regressed on the log of total charges in FY 2001, the number of prescriptions filled in FY 2001, and the FY 2001 eligibility category, performed as well as more complex models. This simple 3-parameter model had an R of 0.30 and, in terms in classification efficiency, had a sensitivity of 0.57, a specificity of 0.90, an area under the receiver operator curve of 0.80, and a weighted kappa statistic of 0.51. This simple model based on readily available administrative data stratified Medicaid members according to predicted future utilization as well as more complicated models.

Analysis of model development strategies: predicting ventral hernia recurrence.

PubMed

Holihan, Julie L; Li, Linda T; Askenasy, Erik P; Greenberg, Jacob A; Keith, Jerrod N; Martindale, Robert G; Roth, J Scott; Liang, Mike K

2016-11-01

There have been many attempts to identify variables associated with ventral hernia recurrence; however, it is unclear which statistical modeling approach results in models with greatest internal and external validity. We aim to assess the predictive accuracy of models developed using five common variable selection strategies to determine variables associated with hernia recurrence. Two multicenter ventral hernia databases were used. Database 1 was randomly split into "development" and "internal validation" cohorts. Database 2 was designated "external validation". The dependent variable for model development was hernia recurrence. Five variable selection strategies were used: (1) "clinical"-variables considered clinically relevant, (2) "selective stepwise"-all variables with a P value <0.20 were assessed in a step-backward model, (3) "liberal stepwise"-all variables were included and step-backward regression was performed, (4) "restrictive internal resampling," and (5) "liberal internal resampling." Variables were included with P < 0.05 for the Restrictive model and P < 0.10 for the Liberal model. A time-to-event analysis using Cox regression was performed using these strategies. The predictive accuracy of the developed models was tested on the internal and external validation cohorts using Harrell's C-statistic where C > 0.70 was considered "reasonable". The recurrence rate was 32.9% (n = 173/526; median/range follow-up, 20/1-58 mo) for the development cohort, 36.0% (n = 95/264, median/range follow-up 20/1-61 mo) for the internal validation cohort, and 12.7% (n = 155/1224, median/range follow-up 9/1-50 mo) for the external validation cohort. Internal validation demonstrated reasonable predictive accuracy (C-statistics = 0.772, 0.760, 0.767, 0.757, 0.763), while on external validation, predictive accuracy dipped precipitously (C-statistic = 0.561, 0.557, 0.562, 0.553, 0.560). Predictive accuracy was equally adequate on internal validation among models; however, on external validation, all five models failed to demonstrate utility. Future studies should report multiple variable selection techniques and demonstrate predictive accuracy on external data sets for model validation. Copyright © 2016 Elsevier Inc. All rights reserved.

Investigating the impact of design characteristics on statistical efficiency within discrete choice experiments: A systematic survey.

PubMed

Vanniyasingam, Thuva; Daly, Caitlin; Jin, Xuejing; Zhang, Yuan; Foster, Gary; Cunningham, Charles; Thabane, Lehana

2018-06-01

This study reviews simulation studies of discrete choice experiments to determine (i) how survey design features affect statistical efficiency, (ii) and to appraise their reporting quality. Statistical efficiency was measured using relative design (D-) efficiency, D-optimality, or D-error. For this systematic survey, we searched Journal Storage (JSTOR), Since Direct, PubMed, and OVID which included a search within EMBASE. Searches were conducted up to year 2016 for simulation studies investigating the impact of DCE design features on statistical efficiency. Studies were screened and data were extracted independently and in duplicate. Results for each included study were summarized by design characteristic. Previously developed criteria for reporting quality of simulation studies were also adapted and applied to each included study. Of 371 potentially relevant studies, 9 were found to be eligible, with several varying in study objectives. Statistical efficiency improved when increasing the number of choice tasks or alternatives; decreasing the number of attributes, attribute levels; using an unrestricted continuous "manipulator" attribute; using model-based approaches with covariates incorporating response behaviour; using sampling approaches that incorporate previous knowledge of response behaviour; incorporating heterogeneity in a model-based design; correctly specifying Bayesian priors; minimizing parameter prior variances; and using an appropriate method to create the DCE design for the research question. The simulation studies performed well in terms of reporting quality. Improvement is needed in regards to clearly specifying study objectives, number of failures, random number generators, starting seeds, and the software used. These results identify the best approaches to structure a DCE. An investigator can manipulate design characteristics to help reduce response burden and increase statistical efficiency. Since studies varied in their objectives, conclusions were made on several design characteristics, however, the validity of each conclusion was limited. Further research should be conducted to explore all conclusions in various design settings and scenarios. Additional reviews to explore other statistical efficiency outcomes and databases can also be performed to enhance the conclusions identified from this review.

A menu-driven software package of Bayesian nonparametric (and parametric) mixed models for regression analysis and density estimation.

PubMed

Karabatsos, George

2017-02-01

Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected functionals and values of covariates. The software is illustrated through the BNP regression analysis of real data.

Generalized linear and generalized additive models in studies of species distributions: Setting the scene

USGS Publications Warehouse

Guisan, Antoine; Edwards, T.C.; Hastie, T.

2002-01-01

An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001. We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling. ?? 2002 Elsevier Science B.V. All rights reserved.

Predicting trauma patient mortality: ICD [or ICD-10-AM] versus AIS based approaches.

PubMed

Willis, Cameron D; Gabbe, Belinda J; Jolley, Damien; Harrison, James E; Cameron, Peter A

2010-11-01

The International Classification of Diseases Injury Severity Score (ICISS) has been proposed as an International Classification of Diseases (ICD)-10-based alternative to mortality prediction tools that use Abbreviated Injury Scale (AIS) data, including the Trauma and Injury Severity Score (TRISS). To date, studies have not examined the performance of ICISS using Australian trauma registry data. This study aimed to compare the performance of ICISS with other mortality prediction tools in an Australian trauma registry. This was a retrospective review of prospectively collected data from the Victorian State Trauma Registry. A training dataset was created for model development and a validation dataset for evaluation. The multiplicative ICISS model was compared with a worst injury ICISS approach, Victorian TRISS (V-TRISS, using local coefficients), maximum AIS severity and a multivariable model including ICD-10-AM codes as predictors. Models were investigated for discrimination (C-statistic) and calibration (Hosmer-Lemeshow statistic). The multivariable approach had the highest level of discrimination (C-statistic 0.90) and calibration (H-L 7.65, P= 0.468). Worst injury ICISS, V-TRISS and maximum AIS had similar performance. The multiplicative ICISS produced the lowest level of discrimination (C-statistic 0.80) and poorest calibration (H-L 50.23, P < 0.001). The performance of ICISS may be affected by the data used to develop estimates, the ICD version employed, the methods for deriving estimates and the inclusion of covariates. In this analysis, a multivariable approach using ICD-10-AM codes was the best-performing method. A multivariable ICISS approach may therefore be a useful alternative to AIS-based methods and may have comparable predictive performance to locally derived TRISS models. © 2010 The Authors. ANZ Journal of Surgery © 2010 Royal Australasian College of Surgeons.

Quantifying and Generalizing Hydrologic Responses to Dam Regulation using a Statistical Modeling Approach

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

McManamay, Ryan A

2014-01-01

Despite the ubiquitous existence of dams within riverscapes, much of our knowledge about dams and their environmental effects remains context-specific. Hydrology, more than any other environmental variable, has been studied in great detail with regard to dam regulation. While much progress has been made in generalizing the hydrologic effects of regulation by large dams, many aspects of hydrology show site-specific fidelity to dam operations, small dams (including diversions), and regional hydrologic regimes. A statistical modeling framework is presented to quantify and generalize hydrologic responses to varying degrees of dam regulation. Specifically, the objectives were to 1) compare the effects ofmore » local versus cumulative dam regulation, 2) determine the importance of different regional hydrologic regimes in influencing hydrologic responses to dams, and 3) evaluate how different regulation contexts lead to error in predicting hydrologic responses to dams. Overall, model performance was poor in quantifying the magnitude of hydrologic responses, but performance was sufficient in classifying hydrologic responses as negative or positive. Responses of some hydrologic indices to dam regulation were highly dependent upon hydrologic class membership and the purpose of the dam. The opposing coefficients between local and cumulative-dam predictors suggested that hydrologic responses to cumulative dam regulation are complex, and predicting the hydrology downstream of individual dams, as opposed to multiple dams, may be more easy accomplished using statistical approaches. Results also suggested that particular contexts, including multipurpose dams, high cumulative regulation by multiple dams, diversions, close proximity to dams, and certain hydrologic classes are all sources of increased error when predicting hydrologic responses to dams. Statistical models, such as the ones presented herein, show promise in their ability to model the effects of dam regulation effects at large spatial scales as to generalize the directionality of hydrologic responses.« less

The potential of statistical shape modelling for geometric morphometric analysis of human teeth in archaeological research

PubMed Central

Fernee, Christianne; Browne, Martin; Zakrzewski, Sonia

2017-01-01

This paper introduces statistical shape modelling (SSM) for use in osteoarchaeology research. SSM is a full field, multi-material analytical technique, and is presented as a supplementary geometric morphometric (GM) tool. Lower mandibular canines from two archaeological populations and one modern population were sampled, digitised using micro-CT, aligned, registered to a baseline and statistically modelled using principal component analysis (PCA). Sample material properties were incorporated as a binary enamel/dentin parameter. Results were assessed qualitatively and quantitatively using anatomical landmarks. Finally, the technique’s application was demonstrated for inter-sample comparison through analysis of the principal component (PC) weights. It was found that SSM could provide high detail qualitative and quantitative insight with respect to archaeological inter- and intra-sample variability. This technique has value for archaeological, biomechanical and forensic applications including identification, finite element analysis (FEA) and reconstruction from partial datasets. PMID:29216199

Annual Research Briefs, 1987

NASA Technical Reports Server (NTRS)

Moin, Parviz; Reynolds, William C.

1988-01-01

Lagrangian techniques have found widespread application to the prediction and understanding of turbulent transport phenomena and have yielded satisfactory results for different cases of shear flow problems. However, it must be kept in mind that in most experiments what is really available are Eulerian statistics, and it is far from obvious how to extract from them the information relevant to the Lagrangian behavior of the flow; in consequence, Lagrangian models still include some hypothesis for which no adequate supporting evidence was until now available. Direct numerical simulation of turbulence offers a new way to obtain Lagrangian statistics and so verify the validity of the current predictive models and the accuracy of their results. After the pioneering work of Riley (Riley and Patterson, 1974) in the 70's, some such results have just appeared in the literature (Lee et al, Yeung and Pope). The present contribution follows in part similar lines, but focuses on two particle statistics and comparison with existing models.

Predicting the Ability of Marine Mammal Populations to Compensate for Behavioral Disturbances

DTIC Science & Technology

2015-09-30

approaches, including simple theoretical models as well as statistical analysis of data rich conditions. Building on models developed for PCoD [2,3], we...conditions is population trajectory most likely to be affected (the central aim of PCoD ). For the revised model presented here, we include a population...averaged condition individuals (here used as a proxy for individual health as defined in PCoD ), and E is the quality of the environment in which the

Establishment of a center of excellence for applied mathematical and statistical research

NASA Technical Reports Server (NTRS)

Woodward, W. A.; Gray, H. L.

1983-01-01

The state of the art was assessed with regards to efforts in support of the crop production estimation problem and alternative generic proportion estimation techniques were investigated. Topics covered include modeling the greeness profile (Badhwarmos model), parameter estimation using mixture models such as CLASSY, and minimum distance estimation as an alternative to maximum likelihood estimation. Approaches to the problem of obtaining proportion estimates when the underlying distributions are asymmetric are examined including the properties of Weibull distribution.

Identifying trends in climate: an application to the cenozoic

NASA Astrophysics Data System (ADS)

Richards, Gordon R.

1998-05-01

The recent literature on trending in climate has raised several issues, whether trends should be modeled as deterministic or stochastic, whether trends are nonlinear, and the relative merits of statistical models versus models based on physics. This article models trending since the late Cretaceous. This 68 million-year interval is selected because the reliability of tests for trending is critically dependent on the length of time spanned by the data. Two main hypotheses are tested, that the trend has been caused primarily by CO2 forcing, and that it reflects a variety of forcing factors which can be approximated by statistical methods. The CO2 data is obtained from model simulations. Several widely-used statistical models are found to be inadequate. ARIMA methods parameterize too much of the short-term variation, and do not identify low frequency movements. Further, the unit root in the ARIMA process does not predict the long-term path of temperature. Spectral methods also have little ability to predict temperature at long horizons. Instead, the statistical trend is estimated using a nonlinear smoothing filter. Both of these paradigms make it possible to model climate as a cointegrated process, in which temperature can wander quite far from the trend path in the intermediate term, but converges back over longer horizons. Comparing the forecasting properties of the two trend models demonstrates that the optimal forecasting model includes CO2 forcing and a parametric representation of the nonlinear variability in climate.

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes

PubMed Central

2017-01-01

Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274, 1926–1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105, 2745–2750; Thiessen & Yee 2010 Child Development 81, 1287–1303; Saffran 2002 Journal of Memory and Language 47, 172–196; Misyak & Christiansen 2012 Language Learning 62, 302–331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39, 246–263; Thiessen et al. 2013 Psychological Bulletin 139, 792–814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik 2013 Cognitive Science 37, 310–343). This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences'. PMID:27872374

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes.

PubMed

Thiessen, Erik D

2017-01-05

Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274: , 1926-1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105: , 2745-2750; Thiessen & Yee 2010 Child Development 81: , 1287-1303; Saffran 2002 Journal of Memory and Language 47: , 172-196; Misyak & Christiansen 2012 Language Learning 62: , 302-331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39: , 246-263; Thiessen et al. 2013 Psychological Bulletin 139: , 792-814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik 2013 Cognitive Science 37: , 310-343).This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

Community air pollution and mortality: Analysis of 1980 data from US metropolitan areas. 1: Particulate air pollution

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

Lipfert, F.W.

1992-11-01

1980 data from up to 149 metropolitan areas were used to define cross-sectional associations between community air pollution and excess human mortality. The regression model proposed by Oezkaynak and Thurston, which accounted for age, race, education, poverty, and population density, was evaluated and several new models were developed. The new models also accounted for population change, drinking water hardness, and smoking, and included a more detailed description of race. Cause-of-death categories analyzed include all causes, all non-external causes, major cardiovascular diseases, and chronic obstructive pulmonary diseases (COPD). Both annual mortality rates and their logarithms were analyzed. The data on particulatesmore » were averaged across all monitoring stations available for each SMSA and the TSP data were restricted to the year 1980. The associations between mortality and air pollution were found to be dependent on the socioeconomic factors included in the models, the specific locations included din the data set, and the type of statistical model used. Statistically significant associations were found between TSP and mortality due to non-external causes with log-linear models, but not with a linear model, and between TS and COPD mortality for both linear and log-linear models. When the sulfate contribution to TSP was subtracted, the relationship with COPD mortality was strengthened. Scatter plots and quintile analyses suggested a TSP threshold for COPD mortality at around 65 ug/m{sup 3} (annual average). SO{sub 4}{sup {minus}2}, Mn, PM{sup 15}, and PM{sub 2.5} were not significantly associated with mortality using the new models.« less

Enhancing predictive accuracy and reproducibility in clinical evaluation research: Commentary on the special section of the Journal of Evaluation in Clinical Practice.

PubMed

Bryant, Fred B

2016-12-01

This paper introduces a special section of the current issue of the Journal of Evaluation in Clinical Practice that includes a set of 6 empirical articles showcasing a versatile, new machine-learning statistical method, known as optimal data (or discriminant) analysis (ODA), specifically designed to produce statistical models that maximize predictive accuracy. As this set of papers clearly illustrates, ODA offers numerous important advantages over traditional statistical methods-advantages that enhance the validity and reproducibility of statistical conclusions in empirical research. This issue of the journal also includes a review of a recently published book that provides a comprehensive introduction to the logic, theory, and application of ODA in empirical research. It is argued that researchers have much to gain by using ODA to analyze their data. © 2016 John Wiley & Sons, Ltd.

Predictive Model for the Design of Zwitterionic Polymer Brushes: A Statistical Design of Experiments Approach.

PubMed

Kumar, Ramya; Lahann, Joerg

2016-07-06

The performance of polymer interfaces in biology is governed by a wide spectrum of interfacial properties. With the ultimate goal of identifying design parameters for stem cell culture coatings, we developed a statistical model that describes the dependence of brush properties on surface-initiated polymerization (SIP) parameters. Employing a design of experiments (DOE) approach, we identified operating boundaries within which four gel architecture regimes can be realized, including a new regime of associated brushes in thin films. Our statistical model can accurately predict the brush thickness and the degree of intermolecular association of poly[{2-(methacryloyloxy) ethyl} dimethyl-(3-sulfopropyl) ammonium hydroxide] (PMEDSAH), a previously reported synthetic substrate for feeder-free and xeno-free culture of human embryonic stem cells. DOE-based multifunctional predictions offer a powerful quantitative framework for designing polymer interfaces. For example, model predictions can be used to decrease the critical thickness at which the wettability transition occurs by simply increasing the catalyst quantity from 1 to 3 mol %.

Fragment size distribution statistics in dynamic fragmentation of laser shock-loaded tin

NASA Astrophysics Data System (ADS)

He, Weihua; Xin, Jianting; Zhao, Yongqiang; Chu, Genbai; Xi, Tao; Shui, Min; Lu, Feng; Gu, Yuqiu

2017-06-01

This work investigates the geometric statistics method to characterize the size distribution of tin fragments produced in the laser shock-loaded dynamic fragmentation process. In the shock experiments, the ejection of the tin sample with etched V-shape groove in the free surface are collected by the soft recovery technique. Subsequently, the produced fragments are automatically detected with the fine post-shot analysis techniques including the X-ray micro-tomography and the improved watershed method. To characterize the size distributions of the fragments, a theoretical random geometric statistics model based on Poisson mixtures is derived for dynamic heterogeneous fragmentation problem, which reveals linear combinational exponential distribution. The experimental data related to fragment size distributions of the laser shock-loaded tin sample are examined with the proposed theoretical model, and its fitting performance is compared with that of other state-of-the-art fragment size distribution models. The comparison results prove that our proposed model can provide far more reasonable fitting result for the laser shock-loaded tin.

Are well functioning civil registration and vital statistics systems associated with better health outcomes?

PubMed

Phillips, David E; AbouZahr, Carla; Lopez, Alan D; Mikkelsen, Lene; de Savigny, Don; Lozano, Rafael; Wilmoth, John; Setel, Philip W

2015-10-03

In this Series paper, we examine whether well functioning civil registration and vital statistics (CRVS) systems are associated with improved population health outcomes. We present a conceptual model connecting CRVS to wellbeing, and describe an ecological association between CRVS and health outcomes. The conceptual model posits that the legal identity that civil registration provides to individuals is key to access entitlements and services. Vital statistics produced by CRVS systems provide essential information for public health policy and prevention. These outcomes benefit individuals and societies, including improved health. We use marginal linear models and lag-lead analysis to measure ecological associations between a composite metric of CRVS performance and three health outcomes. Results are consistent with the conceptual model: improved CRVS performance coincides with improved health outcomes worldwide in a temporally consistent manner. Investment to strengthen CRVS systems is not only an important goal for individuals and societies, but also a development imperative that is good for health. Copyright © 2015 Elsevier Ltd. All rights reserved.

On use of the multistage dose-response model for assessing laboratory animal carcinogenicity

PubMed Central

Nitcheva, Daniella; Piegorsch, Walter W.; West, R. Webster

2007-01-01

We explore how well a statistical multistage model describes dose-response patterns in laboratory animal carcinogenicity experiments from a large database of quantal response data. The data are collected from the U.S. EPA’s publicly available IRIS data warehouse and examined statistically to determine how often higher-order values in the multistage predictor yield significant improvements in explanatory power over lower-order values. Our results suggest that the addition of a second-order parameter to the model only improves the fit about 20% of the time, while adding even higher-order terms apparently does not contribute to the fit at all, at least with the study designs we captured in the IRIS database. Also included is an examination of statistical tests for assessing significance of higher-order terms in a multistage dose-response model. It is noted that bootstrap testing methodology appears to offer greater stability for performing the hypothesis tests than a more-common, but possibly unstable, “Wald” test. PMID:17490794

A Statistical Skull Geometry Model for Children 0-3 Years Old

PubMed Central

Li, Zhigang; Park, Byoung-Keon; Liu, Weiguo; Zhang, Jinhuan; Reed, Matthew P.; Rupp, Jonathan D.; Hoff, Carrie N.; Hu, Jingwen

2015-01-01

Head injury is the leading cause of fatality and long-term disability for children. Pediatric heads change rapidly in both size and shape during growth, especially for children under 3 years old (YO). To accurately assess the head injury risks for children, it is necessary to understand the geometry of the pediatric head and how morphologic features influence injury causation within the 0–3 YO population. In this study, head CT scans from fifty-six 0–3 YO children were used to develop a statistical model of pediatric skull geometry. Geometric features important for injury prediction, including skull size and shape, skull thickness and suture width, along with their variations among the sample population, were quantified through a series of image and statistical analyses. The size and shape of the pediatric skull change significantly with age and head circumference. The skull thickness and suture width vary with age, head circumference and location, which will have important effects on skull stiffness and injury prediction. The statistical geometry model developed in this study can provide a geometrical basis for future development of child anthropomorphic test devices and pediatric head finite element models. PMID:25992998

A statistical skull geometry model for children 0-3 years old.

PubMed

Li, Zhigang; Park, Byoung-Keon; Liu, Weiguo; Zhang, Jinhuan; Reed, Matthew P; Rupp, Jonathan D; Hoff, Carrie N; Hu, Jingwen

2015-01-01

Head injury is the leading cause of fatality and long-term disability for children. Pediatric heads change rapidly in both size and shape during growth, especially for children under 3 years old (YO). To accurately assess the head injury risks for children, it is necessary to understand the geometry of the pediatric head and how morphologic features influence injury causation within the 0-3 YO population. In this study, head CT scans from fifty-six 0-3 YO children were used to develop a statistical model of pediatric skull geometry. Geometric features important for injury prediction, including skull size and shape, skull thickness and suture width, along with their variations among the sample population, were quantified through a series of image and statistical analyses. The size and shape of the pediatric skull change significantly with age and head circumference. The skull thickness and suture width vary with age, head circumference and location, which will have important effects on skull stiffness and injury prediction. The statistical geometry model developed in this study can provide a geometrical basis for future development of child anthropomorphic test devices and pediatric head finite element models.

Statistical physics of vehicular traffic and some related systems

NASA Astrophysics Data System (ADS)

Chowdhury, Debashish; Santen, Ludger; Schadschneider, Andreas

2000-05-01

In the so-called “microscopic” models of vehicular traffic, attention is paid explicitly to each individual vehicle each of which is represented by a “particle”; the nature of the “interactions” among these particles is determined by the way the vehicles influence each others’ movement. Therefore, vehicular traffic, modeled as a system of interacting “particles” driven far from equilibrium, offers the possibility to study various fundamental aspects of truly nonequilibrium systems which are of current interest in statistical physics. Analytical as well as numerical techniques of statistical physics are being used to study these models to understand rich variety of physical phenomena exhibited by vehicular traffic. Some of these phenomena, observed in vehicular traffic under different circumstances, include transitions from one dynamical phase to another, criticality and self-organized criticality, metastability and hysteresis, phase-segregation, etc. In this critical review, written from the perspective of statistical physics, we explain the guiding principles behind all the main theoretical approaches. But we present detailed discussions on the results obtained mainly from the so-called “particle-hopping” models, particularly emphasizing those which have been formulated in recent years using the language of cellular automata.

Causal modelling applied to the risk assessment of a wastewater discharge.

PubMed

Paul, Warren L; Rokahr, Pat A; Webb, Jeff M; Rees, Gavin N; Clune, Tim S

2016-03-01

Bayesian networks (BNs), or causal Bayesian networks, have become quite popular in ecological risk assessment and natural resource management because of their utility as a communication and decision-support tool. Since their development in the field of artificial intelligence in the 1980s, however, Bayesian networks have evolved and merged with structural equation modelling (SEM). Unlike BNs, which are constrained to encode causal knowledge in conditional probability tables, SEMs encode this knowledge in structural equations, which is thought to be a more natural language for expressing causal information. This merger has clarified the causal content of SEMs and generalised the method such that it can now be performed using standard statistical techniques. As it was with BNs, the utility of this new generation of SEM in ecological risk assessment will need to be demonstrated with examples to foster an understanding and acceptance of the method. Here, we applied SEM to the risk assessment of a wastewater discharge to a stream, with a particular focus on the process of translating a causal diagram (conceptual model) into a statistical model which might then be used in the decision-making and evaluation stages of the risk assessment. The process of building and testing a spatial causal model is demonstrated using data from a spatial sampling design, and the implications of the resulting model are discussed in terms of the risk assessment. It is argued that a spatiotemporal causal model would have greater external validity than the spatial model, enabling broader generalisations to be made regarding the impact of a discharge, and greater value as a tool for evaluating the effects of potential treatment plant upgrades. Suggestions are made on how the causal model could be augmented to include temporal as well as spatial information, including suggestions for appropriate statistical models and analyses.

Development of Accommodation Models for Soldiers in Vehicles: Squad

DTIC Science & Technology

2014-09-01

average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed...unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT Data from a previous study of Soldier posture and position were analyzed to develop statistical...range of seat height and seat back angle. All of the models include the effects of body armor and body borne gear. 15. SUBJECT TERMS Anthropometry

Ensemble engineering and statistical modeling for parameter calibration towards optimal design of microbial fuel cells

NASA Astrophysics Data System (ADS)

Sun, Hongyue; Luo, Shuai; Jin, Ran; He, Zhen

2017-07-01

Mathematical modeling is an important tool to investigate the performance of microbial fuel cell (MFC) towards its optimized design. To overcome the shortcoming of traditional MFC models, an ensemble model is developed through integrating both engineering model and statistical analytics for the extrapolation scenarios in this study. Such an ensemble model can reduce laboring effort in parameter calibration and require fewer measurement data to achieve comparable accuracy to traditional statistical model under both the normal and extreme operation regions. Based on different weight between current generation and organic removal efficiency, the ensemble model can give recommended input factor settings to achieve the best current generation and organic removal efficiency. The model predicts a set of optimal design factors for the present tubular MFCs including the anode flow rate of 3.47 mL min-1, organic concentration of 0.71 g L-1, and catholyte pumping flow rate of 14.74 mL min-1 to achieve the peak current at 39.2 mA. To maintain 100% organic removal efficiency, the anode flow rate and organic concentration should be controlled lower than 1.04 mL min-1 and 0.22 g L-1, respectively. The developed ensemble model can be potentially modified to model other types of MFCs or bioelectrochemical systems.

Bose--Einstein Correlations and Thermal Cluster Formation in High-energy Collisions

NASA Astrophysics Data System (ADS)

Bialas, A.; Florkowski, W.; Zalewski, K.

The blast wave model is generalized to include the production of thermal clusters, as suggested by the apparent success of the statistical model of particle production at high energies. The formulae for the HBT correlation functions and the corresponding HBT radii are derived.

Cardiac arrest risk standardization using administrative data compared to registry data.

PubMed

Grossestreuer, Anne V; Gaieski, David F; Donnino, Michael W; Nelson, Joshua I M; Mutter, Eric L; Carr, Brendan G; Abella, Benjamin S; Wiebe, Douglas J

2017-01-01

Methods for comparing hospitals regarding cardiac arrest (CA) outcomes, vital for improving resuscitation performance, rely on data collected by cardiac arrest registries. However, most CA patients are treated at hospitals that do not participate in such registries. This study aimed to determine whether CA risk standardization modeling based on administrative data could perform as well as that based on registry data. Two risk standardization logistic regression models were developed using 2453 patients treated from 2000-2015 at three hospitals in an academic health system. Registry and administrative data were accessed for all patients. The outcome was death at hospital discharge. The registry model was considered the "gold standard" with which to compare the administrative model, using metrics including comparing areas under the curve, calibration curves, and Bland-Altman plots. The administrative risk standardization model had a c-statistic of 0.891 (95% CI: 0.876-0.905) compared to a registry c-statistic of 0.907 (95% CI: 0.895-0.919). When limited to only non-modifiable factors, the administrative model had a c-statistic of 0.818 (95% CI: 0.799-0.838) compared to a registry c-statistic of 0.810 (95% CI: 0.788-0.831). All models were well-calibrated. There was no significant difference between c-statistics of the models, providing evidence that valid risk standardization can be performed using administrative data. Risk standardization using administrative data performs comparably to standardization using registry data. This methodology represents a new tool that can enable opportunities to compare hospital performance in specific hospital systems or across the entire US in terms of survival after CA.

Cardiac arrest risk standardization using administrative data compared to registry data

PubMed Central

Gaieski, David F.; Donnino, Michael W.; Nelson, Joshua I. M.; Mutter, Eric L.; Carr, Brendan G.; Abella, Benjamin S.; Wiebe, Douglas J.

2017-01-01

Background Methods for comparing hospitals regarding cardiac arrest (CA) outcomes, vital for improving resuscitation performance, rely on data collected by cardiac arrest registries. However, most CA patients are treated at hospitals that do not participate in such registries. This study aimed to determine whether CA risk standardization modeling based on administrative data could perform as well as that based on registry data. Methods and results Two risk standardization logistic regression models were developed using 2453 patients treated from 2000–2015 at three hospitals in an academic health system. Registry and administrative data were accessed for all patients. The outcome was death at hospital discharge. The registry model was considered the “gold standard” with which to compare the administrative model, using metrics including comparing areas under the curve, calibration curves, and Bland-Altman plots. The administrative risk standardization model had a c-statistic of 0.891 (95% CI: 0.876–0.905) compared to a registry c-statistic of 0.907 (95% CI: 0.895–0.919). When limited to only non-modifiable factors, the administrative model had a c-statistic of 0.818 (95% CI: 0.799–0.838) compared to a registry c-statistic of 0.810 (95% CI: 0.788–0.831). All models were well-calibrated. There was no significant difference between c-statistics of the models, providing evidence that valid risk standardization can be performed using administrative data. Conclusions Risk standardization using administrative data performs comparably to standardization using registry data. This methodology represents a new tool that can enable opportunities to compare hospital performance in specific hospital systems or across the entire US in terms of survival after CA. PMID:28783754

Functional status predicts acute care readmission in the traumatic spinal cord injury population.

PubMed

Huang, Donna; Slocum, Chloe; Silver, Julie K; Morgan, James W; Goldstein, Richard; Zafonte, Ross; Schneider, Jeffrey C

2018-03-29

Context/objective Acute care readmission has been identified as an important marker of healthcare quality. Most previous models assessing risk prediction of readmission incorporate variables for medical comorbidity. We hypothesized that functional status is a more robust predictor of readmission in the spinal cord injury population than medical comorbidities. Design Retrospective cross-sectional analysis. Setting Inpatient rehabilitation facilities, Uniform Data System for Medical Rehabilitation data from 2002 to 2012 Participants traumatic spinal cord injury patients. Outcome measures A logistic regression model for predicting acute care readmission based on demographic variables and functional status (Functional Model) was compared with models incorporating demographics, functional status, and medical comorbidities (Functional-Plus) or models including demographics and medical comorbidities (Demographic-Comorbidity). The primary outcomes were 3- and 30-day readmission, and the primary measure of model performance was the c-statistic. Results There were a total of 68,395 patients with 1,469 (2.15%) readmitted at 3 days and 7,081 (10.35%) readmitted at 30 days. The c-statistics for the Functional Model were 0.703 and 0.654 for 3 and 30 days. The Functional Model outperformed Demographic-Comorbidity models at 3 days (c-statistic difference: 0.066-0.096) and outperformed two of the three Demographic-Comorbidity models at 30 days (c-statistic difference: 0.029-0.056). The Functional-Plus models exhibited negligible improvements (0.002-0.010) in model performance compared to the Functional models. Conclusion Readmissions are used as a marker of hospital performance. Function-based readmission models in the spinal cord injury population outperform models incorporating medical comorbidities. Readmission risk models for this population would benefit from the inclusion of functional status.

Statistics of Smoothed Cosmic Fields in Perturbation Theory. I. Formulation and Useful Formulae in Second-Order Perturbation Theory

NASA Astrophysics Data System (ADS)

Matsubara, Takahiko

2003-02-01

We formulate a general method for perturbative evaluations of statistics of smoothed cosmic fields and provide useful formulae for application of the perturbation theory to various statistics. This formalism is an extensive generalization of the method used by Matsubara, who derived a weakly nonlinear formula of the genus statistic in a three-dimensional density field. After describing the general method, we apply the formalism to a series of statistics, including genus statistics, level-crossing statistics, Minkowski functionals, and a density extrema statistic, regardless of the dimensions in which each statistic is defined. The relation between the Minkowski functionals and other geometrical statistics is clarified. These statistics can be applied to several cosmic fields, including three-dimensional density field, three-dimensional velocity field, two-dimensional projected density field, and so forth. The results are detailed for second-order theory of the formalism. The effect of the bias is discussed. The statistics of smoothed cosmic fields as functions of rescaled threshold by volume fraction are discussed in the framework of second-order perturbation theory. In CDM-like models, their functional deviations from linear predictions plotted against the rescaled threshold are generally much smaller than that plotted against the direct threshold. There is still a slight meatball shift against rescaled threshold, which is characterized by asymmetry in depths of troughs in the genus curve. A theory-motivated asymmetry factor in the genus curve is proposed.

Improved Statistics for Genome-Wide Interaction Analysis

PubMed Central

Ueki, Masao; Cordell, Heather J.

2012-01-01

Recently, Wu and colleagues [1] proposed two novel statistics for genome-wide interaction analysis using case/control or case-only data. In computer simulations, their proposed case/control statistic outperformed competing approaches, including the fast-epistasis option in PLINK and logistic regression analysis under the correct model; however, reasons for its superior performance were not fully explored. Here we investigate the theoretical properties and performance of Wu et al.'s proposed statistics and explain why, in some circumstances, they outperform competing approaches. Unfortunately, we find minor errors in the formulae for their statistics, resulting in tests that have higher than nominal type 1 error. We also find minor errors in PLINK's fast-epistasis and case-only statistics, although theory and simulations suggest that these errors have only negligible effect on type 1 error. We propose adjusted versions of all four statistics that, both theoretically and in computer simulations, maintain correct type 1 error rates under the null hypothesis. We also investigate statistics based on correlation coefficients that maintain similar control of type 1 error. Although designed to test specifically for interaction, we show that some of these previously-proposed statistics can, in fact, be sensitive to main effects at one or both loci, particularly in the presence of linkage disequilibrium. We propose two new “joint effects” statistics that, provided the disease is rare, are sensitive only to genuine interaction effects. In computer simulations we find, in most situations considered, that highest power is achieved by analysis under the correct genetic model. Such an analysis is unachievable in practice, as we do not know this model. However, generally high power over a wide range of scenarios is exhibited by our joint effects and adjusted Wu statistics. We recommend use of these alternative or adjusted statistics and urge caution when using Wu et al.'s originally-proposed statistics, on account of the inflated error rate that can result. PMID:22496670

The Society of Thoracic Surgeons Congenital Heart Surgery Database Mortality Risk Model: Part 1—Statistical Methodology

PubMed Central

O’Brien, Sean M.; Jacobs, Jeffrey P.; Pasquali, Sara K.; Gaynor, J. William; Karamlou, Tara; Welke, Karl F.; Filardo, Giovanni; Han, Jane M.; Kim, Sunghee; Shahian, David M.; Jacobs, Marshall L.

2016-01-01

Background This study’s objective was to develop a risk model incorporating procedure type and patient factors to be used for case-mix adjustment in the analysis of hospital-specific operative mortality rates after congenital cardiac operations. Methods Included were patients of all ages undergoing cardiac operations, with or without cardiopulmonary bypass, at centers participating in The Society of Thoracic Surgeons Congenital Heart Surgery Database during January 1, 2010, to December 31, 2013. Excluded were isolated patent ductus arteriosus closures in patients weighing less than or equal to 2.5 kg, centers with more than 10% missing data, and patients with missing data for key variables. Data from the first 3.5 years were used for model development, and data from the last 0.5 year were used for assessing model discrimination and calibration. Potential risk factors were proposed based on expert consensus and selected after empirically comparing a variety of modeling options. Results The study cohort included 52,224 patients from 86 centers with 1,931 deaths (3.7%). Covariates included in the model were primary procedure, age, weight, and 11 additional patient factors reflecting acuity status and comorbidities. The C statistic in the validation sample was 0.858. Plots of observed-vs-expected mortality rates revealed good calibration overall and within subgroups, except for a slight overestimation of risk in the highest decile of predicted risk. Removing patient preoperative factors from the model reduced the C statistic to 0.831 and affected the performance classification for 12 of 86 hospitals. Conclusions The risk model is well suited to adjust for case mix in the analysis and reporting of hospital-specific mortality for congenital heart operations. Inclusion of patient factors added useful discriminatory power and reduced bias in the calculation of hospital-specific mortality metrics. PMID:26245502

Improving the long-lead predictability of El Niño using a novel forecasting scheme based on a dynamic components model

NASA Astrophysics Data System (ADS)

Petrova, Desislava; Koopman, Siem Jan; Ballester, Joan; Rodó, Xavier

2017-02-01

El Niño (EN) is a dominant feature of climate variability on inter-annual time scales driving changes in the climate throughout the globe, and having wide-spread natural and socio-economic consequences. In this sense, its forecast is an important task, and predictions are issued on a regular basis by a wide array of prediction schemes and climate centres around the world. This study explores a novel method for EN forecasting. In the state-of-the-art the advantageous statistical technique of unobserved components time series modeling, also known as structural time series modeling, has not been applied. Therefore, we have developed such a model where the statistical analysis, including parameter estimation and forecasting, is based on state space methods, and includes the celebrated Kalman filter. The distinguishing feature of this dynamic model is the decomposition of a time series into a range of stochastically time-varying components such as level (or trend), seasonal, cycles of different frequencies, irregular, and regression effects incorporated as explanatory covariates. These components are modeled separately and ultimately combined in a single forecasting scheme. Customary statistical models for EN prediction essentially use SST and wind stress in the equatorial Pacific. In addition to these, we introduce a new domain of regression variables accounting for the state of the subsurface ocean temperature in the western and central equatorial Pacific, motivated by our analysis, as well as by recent and classical research, showing that subsurface processes and heat accumulation there are fundamental for the genesis of EN. An important feature of the scheme is that different regression predictors are used at different lead months, thus capturing the dynamical evolution of the system and rendering more efficient forecasts. The new model has been tested with the prediction of all warm events that occurred in the period 1996-2015. Retrospective forecasts of these events were made for long lead times of at least two and a half years. Hence, the present study demonstrates that the theoretical limit of ENSO prediction should be sought much longer than the commonly accepted "Spring Barrier". The high correspondence between the forecasts and observations indicates that the proposed model outperforms all current operational statistical models, and behaves comparably to the best dynamical models used for EN prediction. Thus, the novel way in which the modeling scheme has been structured could also be used for improving other statistical and dynamical modeling systems.

Accounting for individualized competing mortality risks in estimating postmenopausal breast cancer risk.

PubMed

Schonberg, Mara A; Li, Vicky W; Eliassen, A Heather; Davis, Roger B; LaCroix, Andrea Z; McCarthy, Ellen P; Rosner, Bernard A; Chlebowski, Rowan T; Hankinson, Susan E; Marcantonio, Edward R; Ngo, Long H

2016-12-01

Accurate risk assessment is necessary for decision-making around breast cancer prevention. We aimed to develop a breast cancer prediction model for postmenopausal women that would take into account their individualized competing risk of non-breast cancer death. We included 73,066 women who completed the 2004 Nurses' Health Study (NHS) questionnaire (all ≥57 years) and followed participants until May 2014. We considered 17 breast cancer risk factors (health behaviors, demographics, family history, reproductive factors) and 7 risk factors for non-breast cancer death (comorbidities, functional dependency) and mammography use. We used competing risk regression to identify factors independently associated with breast cancer. We validated the final model by examining calibration (expected-to-observed ratio of breast cancer incidence, E/O) and discrimination (c-statistic) using 74,887 subjects from the Women's Health Initiative Extension Study (WHI-ES; all were ≥55 years and followed for 5 years). Within 5 years, 1.8 % of NHS participants were diagnosed with breast cancer (vs. 2.0 % in WHI-ES, p = 0.02), and 6.6 % experienced non-breast cancer death (vs. 5.2 % in WHI-ES, p < 0.001). Using a model selection procedure which incorporated the Akaike Information Criterion, c-statistic, statistical significance, and clinical judgement, our final model included 9 breast cancer risk factors, 5 comorbidities, functional dependency, and mammography use. The model's c-statistic was 0.61 (95 % CI [0.60-0.63]) in NHS and 0.57 (0.55-0.58) in WHI-ES. On average, our model under predicted breast cancer in WHI-ES (E/O 0.92 [0.88-0.97]). We developed a novel prediction model that factors in postmenopausal women's individualized competing risks of non-breast cancer death when estimating breast cancer risk.

Passage relevance models for genomics search.

PubMed

Urbain, Jay; Frieder, Ophir; Goharian, Nazli

2009-03-19

We present a passage relevance model for integrating syntactic and semantic evidence of biomedical concepts and topics using a probabilistic graphical model. Component models of topics, concepts, terms, and document are represented as potential functions within a Markov Random Field. The probability of a passage being relevant to a biologist's information need is represented as the joint distribution across all potential functions. Relevance model feedback of top ranked passages is used to improve distributional estimates of query concepts and topics in context, and a dimensional indexing strategy is used for efficient aggregation of concept and term statistics. By integrating multiple sources of evidence including dependencies between topics, concepts, and terms, we seek to improve genomics literature passage retrieval precision. Using this model, we are able to demonstrate statistically significant improvements in retrieval precision using a large genomics literature corpus.

Subject-enabled analytics model on measurement statistics in health risk expert system for public health informatics.

PubMed

Chung, Chi-Jung; Kuo, Yu-Chen; Hsieh, Yun-Yu; Li, Tsai-Chung; Lin, Cheng-Chieh; Liang, Wen-Miin; Liao, Li-Na; Li, Chia-Ing; Lin, Hsueh-Chun

2017-11-01

This study applied open source technology to establish a subject-enabled analytics model that can enhance measurement statistics of case studies with the public health data in cloud computing. The infrastructure of the proposed model comprises three domains: 1) the health measurement data warehouse (HMDW) for the case study repository, 2) the self-developed modules of online health risk information statistics (HRIStat) for cloud computing, and 3) the prototype of a Web-based process automation system in statistics (PASIS) for the health risk assessment of case studies with subject-enabled evaluation. The system design employed freeware including Java applications, MySQL, and R packages to drive a health risk expert system (HRES). In the design, the HRIStat modules enforce the typical analytics methods for biomedical statistics, and the PASIS interfaces enable process automation of the HRES for cloud computing. The Web-based model supports both modes, step-by-step analysis and auto-computing process, respectively for preliminary evaluation and real time computation. The proposed model was evaluated by computing prior researches in relation to the epidemiological measurement of diseases that were caused by either heavy metal exposures in the environment or clinical complications in hospital. The simulation validity was approved by the commercial statistics software. The model was installed in a stand-alone computer and in a cloud-server workstation to verify computing performance for a data amount of more than 230K sets. Both setups reached efficiency of about 10 5 sets per second. The Web-based PASIS interface can be used for cloud computing, and the HRIStat module can be flexibly expanded with advanced subjects for measurement statistics. The analytics procedure of the HRES prototype is capable of providing assessment criteria prior to estimating the potential risk to public health. Copyright © 2017 Elsevier B.V. All rights reserved.

Technical Topic 3.2.2.d Bayesian and Non-Parametric Statistics: Integration of Neural Networks with Bayesian Networks for Data Fusion and Predictive Modeling

DTIC Science & Technology

2016-05-31

and included explosives such as TATP, HMTD, RDX, RDX, ammonium nitrate , potassium perchlorate, potassium nitrate , sugar, and TNT. The approach...Distribution Unlimited UU UU UU UU 31-05-2016 15-Apr-2014 14-Jan-2015 Final Report: Technical Topic 3.2.2. d Bayesian and Non- parametric Statistics...of Papers published in non peer-reviewed journals: Final Report: Technical Topic 3.2.2. d Bayesian and Non-parametric Statistics: Integration of Neural

The Power Prior: Theory and Applications

PubMed Central

Ibrahim, Joseph G.; Chen, Ming-Hui; Gwon, Yeongjin; Chen, Fang

2015-01-01

The power prior has been widely used in many applications covering a large number of disciplines. The power prior is intended to be an informative prior constructed from historical data. It has been used in clinical trials, genetics, health care, psychology, environmental health, engineering, economics, and business. It has also been applied for a wide variety of models and settings, both in the experimental design and analysis contexts. In this review article, we give an A to Z exposition of the power prior and its applications to date. We review its theoretical properties, variations in its formulation, statistical contexts for which it has been used, applications, and its advantages over other informative priors. We review models for which it has been used, including generalized linear models, survival models, and random effects models. Statistical areas where the power prior has been used include model selection, experimental design, hierarchical modeling, and conjugate priors. Prequentist properties of power priors in posterior inference are established and a simulation study is conducted to further examine the empirical performance of the posterior estimates with power priors. Real data analyses are given illustrating the power prior as well as the use of the power prior in the Bayesian design of clinical trials. PMID:26346180

Tracing the source of numerical climate model uncertainties in precipitation simulations using a feature-oriented statistical model

NASA Astrophysics Data System (ADS)

Xu, Y.; Jones, A. D.; Rhoades, A.

2017-12-01

Precipitation is a key component in hydrologic cycles, and changing precipitation regimes contribute to more intense and frequent drought and flood events around the world. Numerical climate modeling is a powerful tool to study climatology and to predict future changes. Despite the continuous improvement in numerical models, long-term precipitation prediction remains a challenge especially at regional scales. To improve numerical simulations of precipitation, it is important to find out where the uncertainty in precipitation simulations comes from. There are two types of uncertainty in numerical model predictions. One is related to uncertainty in the input data, such as model's boundary and initial conditions. These uncertainties would propagate to the final model outcomes even if the numerical model has exactly replicated the true world. But a numerical model cannot exactly replicate the true world. Therefore, the other type of model uncertainty is related the errors in the model physics, such as the parameterization of sub-grid scale processes, i.e., given precise input conditions, how much error could be generated by the in-precise model. Here, we build two statistical models based on a neural network algorithm to predict long-term variation of precipitation over California: one uses "true world" information derived from observations, and the other uses "modeled world" information using model inputs and outputs from the North America Coordinated Regional Downscaling Project (NA CORDEX). We derive multiple climate feature metrics as the predictors for the statistical model to represent the impact of global climate on local hydrology, and include topography as a predictor to represent the local control. We first compare the predictors between the true world and the modeled world to determine the errors contained in the input data. By perturbing the predictors in the statistical model, we estimate how much uncertainty in the model's final outcomes is accounted for by each predictor. By comparing the statistical model derived from true world information and modeled world information, we assess the errors lying in the physics of the numerical models. This work provides a unique insight to assess the performance of numerical climate models, and can be used to guide improvement of precipitation prediction.

A new in silico classification model for ready biodegradability, based on molecular fragments.

PubMed

Lombardo, Anna; Pizzo, Fabiola; Benfenati, Emilio; Manganaro, Alberto; Ferrari, Thomas; Gini, Giuseppina

2014-08-01

Regulations such as the European REACH (Registration, Evaluation, Authorization and restriction of Chemicals) often require chemicals to be evaluated for ready biodegradability, to assess the potential risk for environmental and human health. Because not all chemicals can be tested, there is an increasing demand for tools for quick and inexpensive biodegradability screening, such as computer-based (in silico) theoretical models. We developed an in silico model starting from a dataset of 728 chemicals with ready biodegradability data (MITI-test Ministry of International Trade and Industry). We used the novel software SARpy to automatically extract, through a structural fragmentation process, a set of substructures statistically related to ready biodegradability. Then, we analysed these substructures in order to build some general rules. The model consists of a rule-set made up of the combination of the statistically relevant fragments and of the expert-based rules. The model gives good statistical performance with 92%, 82% and 76% accuracy on the training, test and external set respectively. These results are comparable with other in silico models like BIOWIN developed by the United States Environmental Protection Agency (EPA); moreover this new model includes an easily understandable explanation. Copyright © 2014 Elsevier Ltd. All rights reserved.

OSSOS: X. How to use a Survey Simulator: Statistical Testing of Dynamical Models Against the Real Kuiper Belt

NASA Astrophysics Data System (ADS)

Lawler, Samantha M.; Kavelaars, J. J.; Alexandersen, Mike; Bannister, Michele T.; Gladman, Brett; Petit, Jean-Marc; Shankman, Cory

2018-05-01

All surveys include observational biases, which makes it impossible to directly compare properties of discovered trans-Neptunian Objects (TNOs) with dynamical models. However, by carefully keeping track of survey pointings on the sky, detection limits, tracking fractions, and rate cuts, the biases from a survey can be modelled in Survey Simulator software. A Survey Simulator takes an intrinsic orbital model (from, for example, the output of a dynamical Kuiper belt emplacement simulation) and applies the survey biases, so that the biased simulated objects can be directly compared with real discoveries. This methodology has been used with great success in the Outer Solar System Origins Survey (OSSOS) and its predecessor surveys. In this chapter, we give four examples of ways to use the OSSOS Survey Simulator to gain knowledge about the true structure of the Kuiper Belt. We demonstrate how to statistically compare different dynamical model outputs with real TNO discoveries, how to quantify detection biases within a TNO population, how to measure intrinsic population sizes, and how to use upper limits from non-detections. We hope this will provide a framework for dynamical modellers to statistically test the validity of their models.

Statistical mechanics of simple models of protein folding and design.

PubMed Central

Pande, V S; Grosberg, A Y; Tanaka, T

1997-01-01

It is now believed that the primary equilibrium aspects of simple models of protein folding are understood theoretically. However, current theories often resort to rather heavy mathematics to overcome some technical difficulties inherent in the problem or start from a phenomenological model. To this end, we take a new approach in this pedagogical review of the statistical mechanics of protein folding. The benefit of our approach is a drastic mathematical simplification of the theory, without resort to any new approximations or phenomenological prescriptions. Indeed, the results we obtain agree precisely with previous calculations. Because of this simplification, we are able to present here a thorough and self contained treatment of the problem. Topics discussed include the statistical mechanics of the random energy model (REM), tests of the validity of REM as a model for heteropolymer freezing, freezing transition of random sequences, phase diagram of designed ("minimally frustrated") sequences, and the degree to which errors in the interactions employed in simulations of either folding and design can still lead to correct folding behavior. Images FIGURE 2 FIGURE 3 FIGURE 4 FIGURE 6 PMID:9414231

Students' Emergent Articulations of Statistical Models and Modeling in Making Informal Statistical Inferences

ERIC Educational Resources Information Center

Braham, Hana Manor; Ben-Zvi, Dani

2017-01-01

A fundamental aspect of statistical inference is representation of real-world data using statistical models. This article analyzes students' articulations of statistical models and modeling during their first steps in making informal statistical inferences. An integrated modeling approach (IMA) was designed and implemented to help students…

Estimation of Quasi-Stiffness and Propulsive Work of the Human Ankle in the Stance Phase of Walking

PubMed Central

Shamaei, Kamran; Sawicki, Gregory S.; Dollar, Aaron M.

2013-01-01

Characterizing the quasi-stiffness and work of lower extremity joints is critical for evaluating human locomotion and designing assistive devices such as prostheses and orthoses intended to emulate the biological behavior of human legs. This work aims to establish statistical models that allow us to predict the ankle quasi-stiffness and net mechanical work for adults walking on level ground. During the stance phase of walking, the ankle joint propels the body through three distinctive phases of nearly constant stiffness known as the quasi-stiffness of each phase. Using a generic equation for the ankle moment obtained through an inverse dynamics analysis, we identify key independent parameters needed to predict ankle quasi-stiffness and propulsive work and also the functional form of each correlation. These parameters include gait speed, ankle excursion, and subject height and weight. Based on the identified form of the correlation and key variables, we applied linear regression on experimental walking data for 216 gait trials across 26 subjects (speeds from 0.75–2.63 m/s) to obtain statistical models of varying complexity. The most general forms of the statistical models include all the key parameters and have an R2 of 75% to 81% in the prediction of the ankle quasi-stiffnesses and propulsive work. The most specific models include only subject height and weight and could predict the ankle quasi-stiffnesses and work for optimal walking speed with average error of 13% to 30%. We discuss how these models provide a useful framework and foundation for designing subject- and gait-specific prosthetic and exoskeletal devices designed to emulate biological ankle function during level ground walking. PMID:23555839

Reconstructing constructivism: causal models, Bayesian learning mechanisms, and the theory theory.

PubMed

Gopnik, Alison; Wellman, Henry M

2012-11-01

We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and nontechnical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and the psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists.

Logistic Regression with Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages.

PubMed

Kim, Yoonsang; Choi, Young-Ku; Emery, Sherry

2013-08-01

Several statistical packages are capable of estimating generalized linear mixed models and these packages provide one or more of three estimation methods: penalized quasi-likelihood, Laplace, and Gauss-Hermite. Many studies have investigated these methods' performance for the mixed-effects logistic regression model. However, the authors focused on models with one or two random effects and assumed a simple covariance structure between them, which may not be realistic. When there are multiple correlated random effects in a model, the computation becomes intensive, and often an algorithm fails to converge. Moreover, in our analysis of smoking status and exposure to anti-tobacco advertisements, we have observed that when a model included multiple random effects, parameter estimates varied considerably from one statistical package to another even when using the same estimation method. This article presents a comprehensive review of the advantages and disadvantages of each estimation method. In addition, we compare the performances of the three methods across statistical packages via simulation, which involves two- and three-level logistic regression models with at least three correlated random effects. We apply our findings to a real dataset. Our results suggest that two packages-SAS GLIMMIX Laplace and SuperMix Gaussian quadrature-perform well in terms of accuracy, precision, convergence rates, and computing speed. We also discuss the strengths and weaknesses of the two packages in regard to sample sizes.

Logistic Regression with Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages

PubMed Central

Kim, Yoonsang; Emery, Sherry

2013-01-01

Several statistical packages are capable of estimating generalized linear mixed models and these packages provide one or more of three estimation methods: penalized quasi-likelihood, Laplace, and Gauss-Hermite. Many studies have investigated these methods’ performance for the mixed-effects logistic regression model. However, the authors focused on models with one or two random effects and assumed a simple covariance structure between them, which may not be realistic. When there are multiple correlated random effects in a model, the computation becomes intensive, and often an algorithm fails to converge. Moreover, in our analysis of smoking status and exposure to anti-tobacco advertisements, we have observed that when a model included multiple random effects, parameter estimates varied considerably from one statistical package to another even when using the same estimation method. This article presents a comprehensive review of the advantages and disadvantages of each estimation method. In addition, we compare the performances of the three methods across statistical packages via simulation, which involves two- and three-level logistic regression models with at least three correlated random effects. We apply our findings to a real dataset. Our results suggest that two packages—SAS GLIMMIX Laplace and SuperMix Gaussian quadrature—perform well in terms of accuracy, precision, convergence rates, and computing speed. We also discuss the strengths and weaknesses of the two packages in regard to sample sizes. PMID:24288415

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

Gear Fatigue Crack Diagnosis by Vibration Analysis Using Embedded Modeling

DTIC Science & Technology

2001-04-05

gave references on Wigner - Ville Distribution ( WVD ) and some statistical based methods including FM4, NA4 and NB4. There are limitations for vibration...Embedded Modeling DISTRIBUTION : Approved for public release, distribution unlimited This paper is part of the following report: TITLE: New Frontiers in

How to Use Value-Added Measures Right

ERIC Educational Resources Information Center

Di Carlo, Matthew

2012-01-01

Value-added models are a specific type of "growth model," a diverse group of statistical techniques to isolate a teacher's impact on his or her students' testing progress while controlling for other measurable factors, such as student and school characteristics, that are outside that teacher's control. Opponents, including many teachers, argue…

Model Uncertainty Quantification Methods In Data Assimilation

NASA Astrophysics Data System (ADS)

Pathiraja, S. D.; Marshall, L. A.; Sharma, A.; Moradkhani, H.

2017-12-01

Data Assimilation involves utilising observations to improve model predictions in a seamless and statistically optimal fashion. Its applications are wide-ranging; from improving weather forecasts to tracking targets such as in the Apollo 11 mission. The use of Data Assimilation methods in high dimensional complex geophysical systems is an active area of research, where there exists many opportunities to enhance existing methodologies. One of the central challenges is in model uncertainty quantification; the outcome of any Data Assimilation study is strongly dependent on the uncertainties assigned to both observations and models. I focus on developing improved model uncertainty quantification methods that are applicable to challenging real world scenarios. These include developing methods for cases where the system states are only partially observed, where there is little prior knowledge of the model errors, and where the model error statistics are likely to be highly non-Gaussian.

Analysis/forecast experiments with a multivariate statistical analysis scheme using FGGE data

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1985-01-01

A three-dimensional, multivariate, statistical analysis method, optimal interpolation (OI) is described for modeling meteorological data from widely dispersed sites. The model was developed to analyze FGGE data at the NASA-Goddard Laboratory of Atmospherics. The model features a multivariate surface analysis over the oceans, including maintenance of the Ekman balance and a geographically dependent correlation function. Preliminary comparisons are made between the OI model and similar schemes employed at the European Center for Medium Range Weather Forecasts and the National Meteorological Center. The OI scheme is used to provide input to a GCM, and model error correlations are calculated for forecasts of 500 mb vertical water mixing ratios and the wind profiles. Comparisons are made between the predictions and measured data. The model is shown to be as accurate as a successive corrections model out to 4.5 days.

Modelling 1-minute directional observations of the global irradiance.

NASA Astrophysics Data System (ADS)

Thejll, Peter; Pagh Nielsen, Kristian; Andersen, Elsa; Furbo, Simon

2016-04-01

Direct and diffuse irradiances from the sky has been collected at 1-minute intervals for about a year from the experimental station at the Technical University of Denmark for the IEA project "Solar Resource Assessment and Forecasting". These data were gathered by pyrheliometers tracking the Sun, as well as with apertured pyranometers gathering 1/8th and 1/16th of the light from the sky in 45 degree azimuthal ranges pointed around the compass. The data are gathered in order to develop detailed models of the potentially available solar energy and its variations at high temporal resolution in order to gain a more detailed understanding of the solar resource. This is important for a better understanding of the sub-grid scale cloud variation that cannot be resolved with climate and weather models. It is also important for optimizing the operation of active solar energy systems such as photovoltaic plants and thermal solar collector arrays, and for passive solar energy and lighting to buildings. We present regression-based modelling of the observed data, and focus, here, on the statistical properties of the model fits. Using models based on the one hand on what is found in the literature and on physical expectations, and on the other hand on purely statistical models, we find solutions that can explain up to 90% of the variance in global radiation. The models leaning on physical insights include terms for the direct solar radiation, a term for the circum-solar radiation, a diffuse term and a term for the horizon brightening/darkening. The purely statistical model is found using data- and formula-validation approaches picking model expressions from a general catalogue of possible formulae. The method allows nesting of expressions, and the results found are dependent on and heavily constrained by the cross-validation carried out on statistically independent testing and training data-sets. Slightly better fits -- in terms of variance explained -- is found using the purely statistical fitting/searching approach. We describe the methods applied, results found, and discuss the different potentials of the physics- and statistics-only based model-searches.

Clustering, randomness and regularity in cloud fields. I - Theoretical considerations. II - Cumulus cloud fields

NASA Technical Reports Server (NTRS)

Weger, R. C.; Lee, J.; Zhu, Tianri; Welch, R. M.

1992-01-01

The current controversy existing in reference to the regularity vs. clustering in cloud fields is examined by means of analysis and simulation studies based upon nearest-neighbor cumulative distribution statistics. It is shown that the Poisson representation of random point processes is superior to pseudorandom-number-generated models and that pseudorandom-number-generated models bias the observed nearest-neighbor statistics towards regularity. Interpretation of this nearest-neighbor statistics is discussed for many cases of superpositions of clustering, randomness, and regularity. A detailed analysis is carried out of cumulus cloud field spatial distributions based upon Landsat, AVHRR, and Skylab data, showing that, when both large and small clouds are included in the cloud field distributions, the cloud field always has a strong clustering signal.

Non-linear scaling of a musculoskeletal model of the lower limb using statistical shape models.

PubMed

Nolte, Daniel; Tsang, Chui Kit; Zhang, Kai Yu; Ding, Ziyun; Kedgley, Angela E; Bull, Anthony M J

2016-10-03

Accurate muscle geometry for musculoskeletal models is important to enable accurate subject-specific simulations. Commonly, linear scaling is used to obtain individualised muscle geometry. More advanced methods include non-linear scaling using segmented bone surfaces and manual or semi-automatic digitisation of muscle paths from medical images. In this study, a new scaling method combining non-linear scaling with reconstructions of bone surfaces using statistical shape modelling is presented. Statistical Shape Models (SSMs) of femur and tibia/fibula were used to reconstruct bone surfaces of nine subjects. Reference models were created by morphing manually digitised muscle paths to mean shapes of the SSMs using non-linear transformations and inter-subject variability was calculated. Subject-specific models of muscle attachment and via points were created from three reference models. The accuracy was evaluated by calculating the differences between the scaled and manually digitised models. The points defining the muscle paths showed large inter-subject variability at the thigh and shank - up to 26mm; this was found to limit the accuracy of all studied scaling methods. Errors for the subject-specific muscle point reconstructions of the thigh could be decreased by 9% to 20% by using the non-linear scaling compared to a typical linear scaling method. We conclude that the proposed non-linear scaling method is more accurate than linear scaling methods. Thus, when combined with the ability to reconstruct bone surfaces from incomplete or scattered geometry data using statistical shape models our proposed method is an alternative to linear scaling methods. Copyright © 2016 The Author. Published by Elsevier Ltd.. All rights reserved.

Loop Braiding Statistics and Interacting Fermionic Symmetry-Protected Topological Phases in Three Dimensions

NASA Astrophysics Data System (ADS)

Cheng, Meng; Tantivasadakarn, Nathanan; Wang, Chenjie

2018-01-01

We study Abelian braiding statistics of loop excitations in three-dimensional gauge theories with fermionic particles and the closely related problem of classifying 3D fermionic symmetry-protected topological (FSPT) phases with unitary symmetries. It is known that the two problems are related by turning FSPT phases into gauge theories through gauging the global symmetry of the former. We show that there exist certain types of Abelian loop braiding statistics that are allowed only in the presence of fermionic particles, which correspond to 3D "intrinsic" FSPT phases, i.e., those that do not stem from bosonic SPT phases. While such intrinsic FSPT phases are ubiquitous in 2D systems and in 3D systems with antiunitary symmetries, their existence in 3D systems with unitary symmetries was not confirmed previously due to the fact that strong interaction is necessary to realize them. We show that the simplest unitary symmetry to support 3D intrinsic FSPT phases is Z2×Z4. To establish the results, we first derive a complete set of physical constraints on Abelian loop braiding statistics. Solving the constraints, we obtain all possible Abelian loop braiding statistics in 3D gauge theories, including those that correspond to intrinsic FSPT phases. Then, we construct exactly soluble state-sum models to realize the loop braiding statistics. These state-sum models generalize the well-known Crane-Yetter and Dijkgraaf-Witten models.

Quest for consistent modelling of statistical decay of the compound nucleus

NASA Astrophysics Data System (ADS)

Banerjee, Tathagata; Nath, S.; Pal, Santanu

2018-01-01

A statistical model description of heavy ion induced fusion-fission reactions is presented where shell effects, collective enhancement of level density, tilting away effect of compound nuclear spin and dissipation are included. It is shown that the inclusion of all these effects provides a consistent picture of fission where fission hindrance is required to explain the experimental values of both pre-scission neutron multiplicities and evaporation residue cross-sections in contrast to some of the earlier works where a fission hindrance is required for pre-scission neutrons but a fission enhancement for evaporation residue cross-sections.

The Influence of Vacuum Circuit Breakers and Different Motor Models on Switching Overvoltages in Motor Circuits

NASA Astrophysics Data System (ADS)

Wong, Cat S. M.; Snider, L. A.; Lo, Edward W. C.; Chung, T. S.

Switching of induction motors with vacuum circuit breakers continues to be a concern. In this paper the influence on statistical overvoltages of the stochastic characteristics of vacuum circuit breakers, high frequency models of motors and transformers, and network characteristics, including cable lengths and network topology are evaluated and a general view of the overvoltages phenomena is presented. Finally, a real case study on the statistical voltage levels and risk-of-failure resulting from switching of a vacuum circuit breaker in an industrial installation in Hong Kong is presented.

Analysis Monthly Import of Palm Oil Products Using Box-Jenkins Model

NASA Astrophysics Data System (ADS)

Ahmad, Nurul F. Y.; Khalid, Kamil; Saifullah Rusiman, Mohd; Ghazali Kamardan, M.; Roslan, Rozaini; Che-Him, Norziha

2018-04-01

The palm oil industry has been an important component of the national economy especially the agriculture sector. The aim of this study is to identify the pattern of import of palm oil products, to model the time series using Box-Jenkins model and to forecast the monthly import of palm oil products. The method approach is included in the statistical test for verifying the equivalence model and statistical measurement of three models, namely Autoregressive (AR) model, Moving Average (MA) model and Autoregressive Moving Average (ARMA) model. The model identification of all product import palm oil is different in which the AR(1) was found to be the best model for product import palm oil while MA(3) was found to be the best model for products import palm kernel oil. For the palm kernel, MA(4) was found to be the best model. The results forecast for the next four months for products import palm oil, palm kernel oil and palm kernel showed the most significant decrease compared to the actual data.

AGR-1 Thermocouple Data Analysis

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

Jeff Einerson

2012-05-01

This report documents an effort to analyze measured and simulated data obtained in the Advanced Gas Reactor (AGR) fuel irradiation test program conducted in the INL's Advanced Test Reactor (ATR) to support the Next Generation Nuclear Plant (NGNP) R&D program. The work follows up on a previous study (Pham and Einerson, 2010), in which statistical analysis methods were applied for AGR-1 thermocouple data qualification. The present work exercises the idea that, while recognizing uncertainties inherent in physics and thermal simulations of the AGR-1 test, results of the numerical simulations can be used in combination with the statistical analysis methods tomore » further improve qualification of measured data. Additionally, the combined analysis of measured and simulation data can generate insights about simulation model uncertainty that can be useful for model improvement. This report also describes an experimental control procedure to maintain fuel target temperature in the future AGR tests using regression relationships that include simulation results. The report is organized into four chapters. Chapter 1 introduces the AGR Fuel Development and Qualification program, AGR-1 test configuration and test procedure, overview of AGR-1 measured data, and overview of physics and thermal simulation, including modeling assumptions and uncertainties. A brief summary of statistical analysis methods developed in (Pham and Einerson 2010) for AGR-1 measured data qualification within NGNP Data Management and Analysis System (NDMAS) is also included for completeness. Chapters 2-3 describe and discuss cases, in which the combined use of experimental and simulation data is realized. A set of issues associated with measurement and modeling uncertainties resulted from the combined analysis are identified. This includes demonstration that such a combined analysis led to important insights for reducing uncertainty in presentation of AGR-1 measured data (Chapter 2) and interpretation of simulation results (Chapter 3). The statistics-based simulation-aided experimental control procedure described for the future AGR tests is developed and demonstrated in Chapter 4. The procedure for controlling the target fuel temperature (capsule peak or average) is based on regression functions of thermocouple readings and other relevant parameters and accounting for possible changes in both physical and thermal conditions and in instrument performance.« less

The impact on midlevel vision of statistically optimal divisive normalization in V1

PubMed Central

Coen-Cagli, Ruben; Schwartz, Odelia

2013-01-01

The first two areas of the primate visual cortex (V1, V2) provide a paradigmatic example of hierarchical computation in the brain. However, neither the functional properties of V2 nor the interactions between the two areas are well understood. One key aspect is that the statistics of the inputs received by V2 depend on the nonlinear response properties of V1. Here, we focused on divisive normalization, a canonical nonlinear computation that is observed in many neural areas and modalities. We simulated V1 responses with (and without) different forms of surround normalization derived from statistical models of natural scenes, including canonical normalization and a statistically optimal extension that accounted for image nonhomogeneities. The statistics of the V1 population responses differed markedly across models. We then addressed how V2 receptive fields pool the responses of V1 model units with different tuning. We assumed this is achieved by learning without supervision a linear representation that removes correlations, which could be accomplished with principal component analysis. This approach revealed V2-like feature selectivity when we used the optimal normalization and, to a lesser extent, the canonical one but not in the absence of both. We compared the resulting two-stage models on two perceptual tasks; while models encompassing V1 surround normalization performed better at object recognition, only statistically optimal normalization provided systematic advantages in a task more closely matched to midlevel vision, namely figure/ground judgment. Our results suggest that experiments probing midlevel areas might benefit from using stimuli designed to engage the computations that characterize V1 optimality. PMID:23857950

Cognitive Complaints After Breast Cancer Treatments: Examining the Relationship With Neuropsychological Test Performance

PubMed Central

2013-01-01

Background Cognitive complaints are reported frequently after breast cancer treatments. Their association with neuropsychological (NP) test performance is not well-established. Methods Early-stage, posttreatment breast cancer patients were enrolled in a prospective, longitudinal, cohort study prior to starting endocrine therapy. Evaluation included an NP test battery and self-report questionnaires assessing symptoms, including cognitive complaints. Multivariable regression models assessed associations among cognitive complaints, mood, treatment exposures, and NP test performance. Results One hundred eighty-nine breast cancer patients, aged 21–65 years, completed the evaluation; 23.3% endorsed higher memory complaints and 19.0% reported higher executive function complaints (>1 SD above the mean for healthy control sample). Regression modeling demonstrated a statistically significant association of higher memory complaints with combined chemotherapy and radiation treatments (P = .01), poorer NP verbal memory performance (P = .02), and higher depressive symptoms (P < .001), controlling for age and IQ. For executive functioning complaints, multivariable modeling controlling for age, IQ, and other confounds demonstrated statistically significant associations with better NP visual memory performance (P = .03) and higher depressive symptoms (P < .001), whereas combined chemotherapy and radiation treatment (P = .05) approached statistical significance. Conclusions About one in five post–adjuvant treatment breast cancer patients had elevated memory and/or executive function complaints that were statistically significantly associated with domain-specific NP test performances and depressive symptoms; combined chemotherapy and radiation treatment was also statistically significantly associated with memory complaints. These results and other emerging studies suggest that subjective cognitive complaints in part reflect objective NP performance, although their etiology and biology appear to be multifactorial, motivating further transdisciplinary research. PMID:23606729

Measured, modeled, and causal conceptions of fitness

PubMed Central

Abrams, Marshall

2012-01-01

This paper proposes partial answers to the following questions: in what senses can fitness differences plausibly be considered causes of evolution?What relationships are there between fitness concepts used in empirical research, modeling, and abstract theoretical proposals? How does the relevance of different fitness concepts depend on research questions and methodological constraints? The paper develops a novel taxonomy of fitness concepts, beginning with type fitness (a property of a genotype or phenotype), token fitness (a property of a particular individual), and purely mathematical fitness. Type fitness includes statistical type fitness, which can be measured from population data, and parametric type fitness, which is an underlying property estimated by statistical type fitnesses. Token fitness includes measurable token fitness, which can be measured on an individual, and tendential token fitness, which is assumed to be an underlying property of the individual in its environmental circumstances. Some of the paper's conclusions can be outlined as follows: claims that fitness differences do not cause evolution are reasonable when fitness is treated as statistical type fitness, measurable token fitness, or purely mathematical fitness. Some of the ways in which statistical methods are used in population genetics suggest that what natural selection involves are differences in parametric type fitnesses. Further, it's reasonable to think that differences in parametric type fitness can cause evolution. Tendential token fitnesses, however, are not themselves sufficient for natural selection. Though parametric type fitnesses are typically not directly measurable, they can be modeled with purely mathematical fitnesses and estimated by statistical type fitnesses, which in turn are defined in terms of measurable token fitnesses. The paper clarifies the ways in which fitnesses depend on pragmatic choices made by researchers. PMID:23112804

Statistical Approaches to Interpretation of Local, Regional, and National Highway-Runoff and Urban-Stormwater Data

USGS Publications Warehouse

Tasker, Gary D.; Granato, Gregory E.

2000-01-01

Decision makers need viable methods for the interpretation of local, regional, and national-highway runoff and urban-stormwater data including flows, concentrations and loads of chemical constituents and sediment, potential effects on receiving waters, and the potential effectiveness of various best management practices (BMPs). Valid (useful for intended purposes), current, and technically defensible stormwater-runoff models are needed to interpret data collected in field studies, to support existing highway and urban-runoffplanning processes, to meet National Pollutant Discharge Elimination System (NPDES) requirements, and to provide methods for computation of Total Maximum Daily Loads (TMDLs) systematically and economically. Historically, conceptual, simulation, empirical, and statistical models of varying levels of detail, complexity, and uncertainty have been used to meet various data-quality objectives in the decision-making processes necessary for the planning, design, construction, and maintenance of highways and for other land-use applications. Water-quality simulation models attempt a detailed representation of the physical processes and mechanisms at a given site. Empirical and statistical regional water-quality assessment models provide a more general picture of water quality or changes in water quality over a region. All these modeling techniques share one common aspect-their predictive ability is poor without suitable site-specific data for calibration. To properly apply the correct model, one must understand the classification of variables, the unique characteristics of water-resources data, and the concept of population structure and analysis. Classifying variables being used to analyze data may determine which statistical methods are appropriate for data analysis. An understanding of the characteristics of water-resources data is necessary to evaluate the applicability of different statistical methods, to interpret the results of these techniques, and to use tools and techniques that account for the unique nature of water-resources data sets. Populations of data on stormwater-runoff quantity and quality are often best modeled as logarithmic transformations. Therefore, these factors need to be considered to form valid, current, and technically defensible stormwater-runoff models. Regression analysis is an accepted method for interpretation of water-resources data and for prediction of current or future conditions at sites that fit the input data model. Regression analysis is designed to provide an estimate of the average response of a system as it relates to variation in one or more known variables. To produce valid models, however, regression analysis should include visual analysis of scatterplots, an examination of the regression equation, evaluation of the method design assumptions, and regression diagnostics. A number of statistical techniques are described in the text and in the appendixes to provide information necessary to interpret data by use of appropriate methods. Uncertainty is an important part of any decisionmaking process. In order to deal with uncertainty problems, the analyst needs to know the severity of the statistical uncertainty of the methods used to predict water quality. Statistical models need to be based on information that is meaningful, representative, complete, precise, accurate, and comparable to be deemed valid, up to date, and technically supportable. To assess uncertainty in the analytical tools, the modeling methods, and the underlying data set, all of these components need be documented and communicated in an accessible format within project publications.

A Geostatistical Scaling Approach for the Generation of Non Gaussian Random Variables and Increments

NASA Astrophysics Data System (ADS)

Guadagnini, Alberto; Neuman, Shlomo P.; Riva, Monica; Panzeri, Marco

2016-04-01

We address manifestations of non-Gaussian statistical scaling displayed by many variables, Y, and their (spatial or temporal) increments. Evidence of such behavior includes symmetry of increment distributions at all separation distances (or lags) with sharp peaks and heavy tails which tend to decay asymptotically as lag increases. Variables reported to exhibit such distributions include quantities of direct relevance to hydrogeological sciences, e.g. porosity, log permeability, electrical resistivity, soil and sediment texture, sediment transport rate, rainfall, measured and simulated turbulent fluid velocity, and other. No model known to us captures all of the documented statistical scaling behaviors in a unique and consistent manner. We recently proposed a generalized sub-Gaussian model (GSG) which reconciles within a unique theoretical framework the probability distributions of a target variable and its increments. We presented an algorithm to generate unconditional random realizations of statistically isotropic or anisotropic GSG functions and illustrated it in two dimensions. In this context, we demonstrated the feasibility of estimating all key parameters of a GSG model underlying a single realization of Y by analyzing jointly spatial moments of Y data and corresponding increments. Here, we extend our GSG model to account for noisy measurements of Y at a discrete set of points in space (or time), present an algorithm to generate conditional realizations of corresponding isotropic or anisotropic random field, and explore them on one- and two-dimensional synthetic test cases.

Data-driven non-Markovian closure models

NASA Astrophysics Data System (ADS)

Kondrashov, Dmitri; Chekroun, Mickaël D.; Ghil, Michael

2015-03-01

This paper has two interrelated foci: (i) obtaining stable and efficient data-driven closure models by using a multivariate time series of partial observations from a large-dimensional system; and (ii) comparing these closure models with the optimal closures predicted by the Mori-Zwanzig (MZ) formalism of statistical physics. Multilayer stochastic models (MSMs) are introduced as both a generalization and a time-continuous limit of existing multilevel, regression-based approaches to closure in a data-driven setting; these approaches include empirical model reduction (EMR), as well as more recent multi-layer modeling. It is shown that the multilayer structure of MSMs can provide a natural Markov approximation to the generalized Langevin equation (GLE) of the MZ formalism. A simple correlation-based stopping criterion for an EMR-MSM model is derived to assess how well it approximates the GLE solution. Sufficient conditions are derived on the structure of the nonlinear cross-interactions between the constitutive layers of a given MSM to guarantee the existence of a global random attractor. This existence ensures that no blow-up can occur for a broad class of MSM applications, a class that includes non-polynomial predictors and nonlinearities that do not necessarily preserve quadratic energy invariants. The EMR-MSM methodology is first applied to a conceptual, nonlinear, stochastic climate model of coupled slow and fast variables, in which only slow variables are observed. It is shown that the resulting closure model with energy-conserving nonlinearities efficiently captures the main statistical features of the slow variables, even when there is no formal scale separation and the fast variables are quite energetic. Second, an MSM is shown to successfully reproduce the statistics of a partially observed, generalized Lotka-Volterra model of population dynamics in its chaotic regime. The challenges here include the rarity of strange attractors in the model's parameter space and the existence of multiple attractor basins with fractal boundaries. The positivity constraint on the solutions' components replaces here the quadratic-energy-preserving constraint of fluid-flow problems and it successfully prevents blow-up.

System analysis for the Huntsville Operational Support Center distributed computer system

NASA Technical Reports Server (NTRS)

Ingels, E. M.

1983-01-01

A simulation model was developed and programmed in three languages BASIC, PASCAL, and SLAM. Two of the programs are included in this report, the BASIC and the PASCAL language programs. SLAM is not supported by NASA/MSFC facilities and hence was not included. The statistical comparison of simulations of the same HOSC system configurations are in good agreement and are in agreement with the operational statistics of HOSC that were obtained. Three variations of the most recent HOSC configuration was run and some conclusions drawn as to the system performance under these variations.

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.

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.

An Interactive Tool For Semi-automated Statistical Prediction Using Earth Observations and Models

NASA Astrophysics Data System (ADS)

Zaitchik, B. F.; Berhane, F.; Tadesse, T.

2015-12-01

We developed a semi-automated statistical prediction tool applicable to concurrent analysis or seasonal prediction of any time series variable in any geographic location. The tool was developed using Shiny, JavaScript, HTML and CSS. A user can extract a predictand by drawing a polygon over a region of interest on the provided user interface (global map). The user can select the Climatic Research Unit (CRU) precipitation or Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) as predictand. They can also upload their own predictand time series. Predictors can be extracted from sea surface temperature, sea level pressure, winds at different pressure levels, air temperature at various pressure levels, and geopotential height at different pressure levels. By default, reanalysis fields are applied as predictors, but the user can also upload their own predictors, including a wide range of compatible satellite-derived datasets. The package generates correlations of the variables selected with the predictand. The user also has the option to generate composites of the variables based on the predictand. Next, the user can extract predictors by drawing polygons over the regions that show strong correlations (composites). Then, the user can select some or all of the statistical prediction models provided. Provided models include Linear Regression models (GLM, SGLM), Tree-based models (bagging, random forest, boosting), Artificial Neural Network, and other non-linear models such as Generalized Additive Model (GAM) and Multivariate Adaptive Regression Splines (MARS). Finally, the user can download the analysis steps they used, such as the region they selected, the time period they specified, the predictand and predictors they chose and preprocessing options they used, and the model results in PDF or HTML format. Key words: Semi-automated prediction, Shiny, R, GLM, ANN, RF, GAM, MARS

Choosing an Appropriate Modelling Framework for Analysing Multispecies Co-culture Cell Biology Experiments.

PubMed

Markham, Deborah C; Simpson, Matthew J; Baker, Ruth E

2015-04-01

In vitro cell biology assays play a crucial role in informing our understanding of the migratory, proliferative and invasive properties of many cell types in different biological contexts. While mono-culture assays involve the study of a population of cells composed of a single cell type, co-culture assays study a population of cells composed of multiple cell types (or subpopulations of cells). Such co-culture assays can provide more realistic insights into many biological processes including tissue repair, tissue regeneration and malignant spreading. Typically, system parameters, such as motility and proliferation rates, are estimated by calibrating a mathematical or computational model to the observed experimental data. However, parameter estimates can be highly sensitive to the choice of model and modelling framework. This observation motivates us to consider the fundamental question of how we can best choose a model to facilitate accurate parameter estimation for a particular assay. In this work we describe three mathematical models of mono-culture and co-culture assays that include different levels of spatial detail. We study various spatial summary statistics to explore if they can be used to distinguish between the suitability of each model over a range of parameter space. Our results for mono-culture experiments are promising, in that we suggest two spatial statistics that can be used to direct model choice. However, co-culture experiments are far more challenging: we show that these same spatial statistics which provide useful insight into mono-culture systems are insufficient for co-culture systems. Therefore, we conclude that great care ought to be exercised when estimating the parameters of co-culture assays.

Large ensemble modeling of the last deglacial retreat of the West Antarctic Ice Sheet: comparison of simple and advanced statistical techniques

NASA Astrophysics Data System (ADS)

Pollard, David; Chang, Won; Haran, Murali; Applegate, Patrick; DeConto, Robert

2016-05-01

A 3-D hybrid ice-sheet model is applied to the last deglacial retreat of the West Antarctic Ice Sheet over the last ˜ 20 000 yr. A large ensemble of 625 model runs is used to calibrate the model to modern and geologic data, including reconstructed grounding lines, relative sea-level records, elevation-age data and uplift rates, with an aggregate score computed for each run that measures overall model-data misfit. Two types of statistical methods are used to analyze the large-ensemble results: simple averaging weighted by the aggregate score, and more advanced Bayesian techniques involving Gaussian process-based emulation and calibration, and Markov chain Monte Carlo. The analyses provide sea-level-rise envelopes with well-defined parametric uncertainty bounds, but the simple averaging method only provides robust results with full-factorial parameter sampling in the large ensemble. Results for best-fit parameter ranges and envelopes of equivalent sea-level rise with the simple averaging method agree well with the more advanced techniques. Best-fit parameter ranges confirm earlier values expected from prior model tuning, including large basal sliding coefficients on modern ocean beds.

Probabilistic-driven oriented Speckle reducing anisotropic diffusion with application to cardiac ultrasonic images.

PubMed

Vegas-Sanchez-Ferrero, G; Aja-Fernandez, S; Martin-Fernandez, M; Frangi, A F; Palencia, C

2010-01-01

A novel anisotropic diffusion filter is proposed in this work with application to cardiac ultrasonic images. It includes probabilistic models which describe the probability density function (PDF) of tissues and adapts the diffusion tensor to the image iteratively. For this purpose, a preliminary study is performed in order to select the probability models that best fit the stastitical behavior of each tissue class in cardiac ultrasonic images. Then, the parameters of the diffusion tensor are defined taking into account the statistical properties of the image at each voxel. When the structure tensor of the probability of belonging to each tissue is included in the diffusion tensor definition, a better boundaries estimates can be obtained instead of calculating directly the boundaries from the image. This is the main contribution of this work. Additionally, the proposed method follows the statistical properties of the image in each iteration. This is considered as a second contribution since state-of-the-art methods suppose that noise or statistical properties of the image do not change during the filter process.

Machine learning to analyze images of shocked materials for precise and accurate measurements

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

Dresselhaus-Cooper, Leora; Howard, Marylesa; Hock, Margaret C.

A supervised machine learning algorithm, called locally adaptive discriminant analysis (LADA), has been developed to locate boundaries between identifiable image features that have varying intensities. LADA is an adaptation of image segmentation, which includes techniques that find the positions of image features (classes) using statistical intensity distributions for each class in the image. In order to place a pixel in the proper class, LADA considers the intensity at that pixel and the distribution of intensities in local (nearby) pixels. This paper presents the use of LADA to provide, with statistical uncertainties, the positions and shapes of features within ultrafast imagesmore » of shock waves. We demonstrate the ability to locate image features including crystals, density changes associated with shock waves, and material jetting caused by shock waves. This algorithm can analyze images that exhibit a wide range of physical phenomena because it does not rely on comparison to a model. LADA enables analysis of images from shock physics with statistical rigor independent of underlying models or simulations.« less

Regularized learning of linear ordered-statistic constant false alarm rate filters (Conference Presentation)

NASA Astrophysics Data System (ADS)

Havens, Timothy C.; Cummings, Ian; Botts, Jonathan; Summers, Jason E.

2017-05-01

The linear ordered statistic (LOS) is a parameterized ordered statistic (OS) that is a weighted average of a rank-ordered sample. LOS operators are useful generalizations of aggregation as they can represent any linear aggregation, from minimum to maximum, including conventional aggregations, such as mean and median. In the fuzzy logic field, these aggregations are called ordered weighted averages (OWAs). Here, we present a method for learning LOS operators from training data, viz., data for which you know the output of the desired LOS. We then extend the learning process with regularization, such that a lower complexity or sparse LOS can be learned. Hence, we discuss what 'lower complexity' means in this context and how to represent that in the optimization procedure. Finally, we apply our learning methods to the well-known constant-false-alarm-rate (CFAR) detection problem, specifically for the case of background levels modeled by long-tailed distributions, such as the K-distribution. These backgrounds arise in several pertinent imaging problems, including the modeling of clutter in synthetic aperture radar and sonar (SAR and SAS) and in wireless communications.

Data-driven fuel consumption estimation: A multivariate adaptive regression spline approach

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

Chen, Yuche; Zhu, Lei; Gonder, Jeffrey

Providing guidance and information to drivers to help them make fuel-efficient route choices remains an important and effective strategy in the near term to reduce fuel consumption from the transportation sector. One key component in implementing this strategy is a fuel-consumption estimation model. In this paper, we developed a mesoscopic fuel consumption estimation model that can be implemented into an eco-routing system. Our proposed model presents a framework that utilizes large-scale, real-world driving data, clusters road links by free-flow speed and fits one statistical model for each of cluster. This model includes predicting variables that were rarely or never consideredmore » before, such as free-flow speed and number of lanes. We applied the model to a real-world driving data set based on a global positioning system travel survey in the Philadelphia-Camden-Trenton metropolitan area. Results from the statistical analyses indicate that the independent variables we chose influence the fuel consumption rates of vehicles. But the magnitude and direction of the influences are dependent on the type of road links, specifically free-flow speeds of links. Here, a statistical diagnostic is conducted to ensure the validity of the models and results. Although the real-world driving data we used to develop statistical relationships are specific to one region, the framework we developed can be easily adjusted and used to explore the fuel consumption relationship in other regions.« less

Data-driven fuel consumption estimation: A multivariate adaptive regression spline approach

DOE PAGES

Chen, Yuche; Zhu, Lei; Gonder, Jeffrey; ...

2017-08-12

Providing guidance and information to drivers to help them make fuel-efficient route choices remains an important and effective strategy in the near term to reduce fuel consumption from the transportation sector. One key component in implementing this strategy is a fuel-consumption estimation model. In this paper, we developed a mesoscopic fuel consumption estimation model that can be implemented into an eco-routing system. Our proposed model presents a framework that utilizes large-scale, real-world driving data, clusters road links by free-flow speed and fits one statistical model for each of cluster. This model includes predicting variables that were rarely or never consideredmore » before, such as free-flow speed and number of lanes. We applied the model to a real-world driving data set based on a global positioning system travel survey in the Philadelphia-Camden-Trenton metropolitan area. Results from the statistical analyses indicate that the independent variables we chose influence the fuel consumption rates of vehicles. But the magnitude and direction of the influences are dependent on the type of road links, specifically free-flow speeds of links. Here, a statistical diagnostic is conducted to ensure the validity of the models and results. Although the real-world driving data we used to develop statistical relationships are specific to one region, the framework we developed can be easily adjusted and used to explore the fuel consumption relationship in other regions.« less

New statistical potential for quality assessment of protein models and a survey of energy functions

PubMed Central

2010-01-01

Background Scoring functions, such as molecular mechanic forcefields and statistical potentials are fundamentally important tools in protein structure modeling and quality assessment. Results The performances of a number of publicly available scoring functions are compared with a statistical rigor, with an emphasis on knowledge-based potentials. We explored the effect on accuracy of alternative choices for representing interaction center types and other features of scoring functions, such as using information on solvent accessibility, on torsion angles, accounting for secondary structure preferences and side chain orientation. Partially based on the observations made, we present a novel residue based statistical potential, which employs a shuffled reference state definition and takes into account the mutual orientation of residue side chains. Atom- and residue-level statistical potentials and Linux executables to calculate the energy of a given protein proposed in this work can be downloaded from http://www.fiserlab.org/potentials. Conclusions Among the most influential terms we observed a critical role of a proper reference state definition and the benefits of including information about the microenvironment of interaction centers. Molecular mechanical potentials were also tested and found to be over-sensitive to small local imperfections in a structure, requiring unfeasible long energy relaxation before energy scores started to correlate with model quality. PMID:20226048

Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic.

PubMed

Leyrat, Clémence; Caille, Agnès; Foucher, Yohann; Giraudeau, Bruno

2016-01-22

Despite randomization, baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs). Covariate imbalance may jeopardize the validity of statistical inferences if they occur on prognostic factors. Thus, the diagnosis of a such imbalance is essential to adjust statistical analysis if required. We developed a tool based on the c-statistic of the propensity score (PS) model to detect global baseline covariate imbalance in CRTs and assess the risk of confounding bias. We performed a simulation study to assess the performance of the proposed tool and applied this method to analyze the data from 2 published CRTs. The proposed method had good performance for large sample sizes (n =500 per arm) and when the number of unbalanced covariates was not too small as compared with the total number of baseline covariates (≥40% of unbalanced covariates). We also provide a strategy for pre selection of the covariates needed to be included in the PS model to enhance imbalance detection. The proposed tool could be useful in deciding whether covariate adjustment is required before performing statistical analyses of CRTs.

Utilization of an Enhanced Canonical Correlation Analysis (ECCA) to Predict Daily Precipitation and Temperature in a Semi-Arid Environment

NASA Astrophysics Data System (ADS)

Lopez, S. R.; Hogue, T. S.

2011-12-01

Global climate models (GCMs) are primarily used to generate historical and future large-scale circulation patterns at a coarse resolution (typical order of 50,000 km2) and fail to capture climate variability at the ground level due to localized surface influences (i.e topography, marine, layer, land cover, etc). Their inability to accurately resolve these processes has led to the development of numerous 'downscaling' techniques. The goal of this study is to enhance statistical downscaling of daily precipitation and temperature for regions with heterogeneous land cover and topography. Our analysis was divided into two periods, historical (1961-2000) and contemporary (1980-2000), and tested using sixteen predictand combinations from four GCMs (GFDL CM2.0, GFDL CM2.1, CNRM-CM3 and MRI-CGCM2 3.2a. The Southern California area was separated into five county regions: Santa Barbara, Ventura, Los Angeles, Orange and San Diego. Principle component analysis (PCA) was performed on ground-based observations in order to (1) reduce the number of redundant gauges and minimize dimensionality and (2) cluster gauges that behave statistically similarly for post-analysis. Post-PCA analysis included extensive testing of predictor-predictand relationships using an enhanced canonical correlation analysis (ECCA). The ECCA includes obtaining the optimal predictand sets for all models within each spatial domain (county) as governed by daily and monthly overall statistics. Results show all models maintain mean annual and monthly behavior within each county and daily statistics are improved. The level of improvement highly depends on the vegetation extent within each county and the land-to-ocean ratio within the GCM spatial grid. The utilization of the entire historical period also leads to better statistical representation of observed daily precipitation. The validated ECCA technique is being applied to future climate scenarios distributed by the IPCC in order to provide forcing data for regional hydrologic models and assess future water resources in the Southern California region.

Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1

PubMed Central

Schuch, Klaus; Logothetis, Nikos K.; Maass, Wolfgang

2011-01-01

A major goal of computational neuroscience is the creation of computer models for cortical areas whose response to sensory stimuli resembles that of cortical areas in vivo in important aspects. It is seldom considered whether the simulated spiking activity is realistic (in a statistical sense) in response to natural stimuli. Because certain statistical properties of spike responses were suggested to facilitate computations in the cortex, acquiring a realistic firing regimen in cortical network models might be a prerequisite for analyzing their computational functions. We present a characterization and comparison of the statistical response properties of the primary visual cortex (V1) in vivo and in silico in response to natural stimuli. We recorded from multiple electrodes in area V1 of 4 macaque monkeys and developed a large state-of-the-art network model for a 5 × 5-mm patch of V1 composed of 35,000 neurons and 3.9 million synapses that integrates previously published anatomical and physiological details. By quantitative comparison of the model response to the “statistical fingerprint” of responses in vivo, we find that our model for a patch of V1 responds to the same movie in a way which matches the statistical structure of the recorded data surprisingly well. The deviation between the firing regimen of the model and the in vivo data are on the same level as deviations among monkeys and sessions. This suggests that, despite strong simplifications and abstractions of cortical network models, they are nevertheless capable of generating realistic spiking activity. To reach a realistic firing state, it was not only necessary to include both N-methyl-d-aspartate and GABAB synaptic conductances in our model, but also to markedly increase the strength of excitatory synapses onto inhibitory neurons (>2-fold) in comparison to literature values, hinting at the importance to carefully adjust the effect of inhibition for achieving realistic dynamics in current network models. PMID:21106898

Markov modulated Poisson process models incorporating covariates for rainfall intensity.

PubMed

Thayakaran, R; Ramesh, N I

2013-01-01

Time series of rainfall bucket tip times at the Beaufort Park station, Bracknell, in the UK are modelled by a class of Markov modulated Poisson processes (MMPP) which may be thought of as a generalization of the Poisson process. Our main focus in this paper is to investigate the effects of including covariate information into the MMPP model framework on statistical properties. In particular, we look at three types of time-varying covariates namely temperature, sea level pressure, and relative humidity that are thought to be affecting the rainfall arrival process. Maximum likelihood estimation is used to obtain the parameter estimates, and likelihood ratio tests are employed in model comparison. Simulated data from the fitted model are used to make statistical inferences about the accumulated rainfall in the discrete time interval. Variability of the daily Poisson arrival rates is studied.

CORSSA: Community Online Resource for Statistical Seismicity Analysis

NASA Astrophysics Data System (ADS)

Zechar, J. D.; Hardebeck, J. L.; Michael, A. J.; Naylor, M.; Steacy, S.; Wiemer, S.; Zhuang, J.

2011-12-01

Statistical seismology is critical to the understanding of seismicity, the evaluation of proposed earthquake prediction and forecasting methods, and the assessment of seismic hazard. Unfortunately, despite its importance to seismology-especially to those aspects with great impact on public policy-statistical seismology is mostly ignored in the education of seismologists, and there is no central repository for the existing open-source software tools. To remedy these deficiencies, and with the broader goal to enhance the quality of statistical seismology research, we have begun building the Community Online Resource for Statistical Seismicity Analysis (CORSSA, www.corssa.org). We anticipate that the users of CORSSA will range from beginning graduate students to experienced researchers. More than 20 scientists from around the world met for a week in Zurich in May 2010 to kick-start the creation of CORSSA: the format and initial table of contents were defined; a governing structure was organized; and workshop participants began drafting articles. CORSSA materials are organized with respect to six themes, each will contain between four and eight articles. CORSSA now includes seven articles with an additional six in draft form along with forums for discussion, a glossary, and news about upcoming meetings, special issues, and recent papers. Each article is peer-reviewed and presents a balanced discussion, including illustrative examples and code snippets. Topics in the initial set of articles include: introductions to both CORSSA and statistical seismology, basic statistical tests and their role in seismology; understanding seismicity catalogs and their problems; basic techniques for modeling seismicity; and methods for testing earthquake predictability hypotheses. We have also begun curating a collection of statistical seismology software packages.

Weak lensing mass map and peak statistics in Canada-France-Hawaii Telescope Stripe 82 survey

NASA Astrophysics Data System (ADS)

Shan, Huan Yuan; Kneib, Jean-Paul; Comparat, Johan; Jullo, Eric; Charbonnier, Aldée; Erben, Thomas; Makler, Martin; Moraes, Bruno; Van Waerbeke, Ludovic; Courbin, Frédéric; Meylan, Georges; Tao, Charling; Taylor, James E.

2014-08-01

We present a weak lensing mass map covering ˜124 deg2 of the Canada-France-Hawaii Telescope Stripe 82 Survey (CS82). We study the statistics of rare peaks in the map, including peak abundance, the peak-peak correlation functions and the tangential-shear profiles around peaks. We find that the abundance of peaks detected in CS82 is consistent with predictions from a Λ cold dark matter cosmological model, once noise effects are properly included. The correlation functions of peaks with different signal-to-noise ratio (SNR) are well described by power laws, and there is a clear cross-correlation between the Sloan Digital Sky Survey III/Constant Mass galaxies and high SNR peaks. The tangential-shear profiles around peaks increase with peak SNR. We fit analytical models to the tangential-shear profiles, including a projected singular isothermal sphere (SIS) model and a projected Navarro, Frenk & White (NFW) model, plus a two-halo term. For the high SNR peaks, the SIS model is rejected at ˜3σ. The NFW model plus a two-halo term gives more acceptable fits to the data. Some peaks match the positions of optically detected clusters, while others are relatively dark. Comparing dark and matched peaks, we find a difference in lensing signal of a factor of 2, suggesting that about half of the dark peaks are false detections.

An ensemble Kalman filter for statistical estimation of physics constrained nonlinear regression models

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

Harlim, John, E-mail: jharlim@psu.edu; Mahdi, Adam, E-mail: amahdi@ncsu.edu; Majda, Andrew J., E-mail: jonjon@cims.nyu.edu

2014-01-15

A central issue in contemporary science is the development of nonlinear data driven statistical–dynamical models for time series of noisy partial observations from nature or a complex model. It has been established recently that ad-hoc quadratic multi-level regression models can have finite-time blow-up of statistical solutions and/or pathological behavior of their invariant measure. Recently, a new class of physics constrained nonlinear regression models were developed to ameliorate this pathological behavior. Here a new finite ensemble Kalman filtering algorithm is developed for estimating the state, the linear and nonlinear model coefficients, the model and the observation noise covariances from available partialmore » noisy observations of the state. Several stringent tests and applications of the method are developed here. In the most complex application, the perfect model has 57 degrees of freedom involving a zonal (east–west) jet, two topographic Rossby waves, and 54 nonlinearly interacting Rossby waves; the perfect model has significant non-Gaussian statistics in the zonal jet with blocked and unblocked regimes and a non-Gaussian skewed distribution due to interaction with the other 56 modes. We only observe the zonal jet contaminated by noise and apply the ensemble filter algorithm for estimation. Numerically, we find that a three dimensional nonlinear stochastic model with one level of memory mimics the statistical effect of the other 56 modes on the zonal jet in an accurate fashion, including the skew non-Gaussian distribution and autocorrelation decay. On the other hand, a similar stochastic model with zero memory levels fails to capture the crucial non-Gaussian behavior of the zonal jet from the perfect 57-mode model.« less

Accounting for individualized competing mortality risks in estimating postmenopausal breast cancer risk

PubMed Central

Schonberg, Mara A.; Li, Vicky W.; Eliassen, A. Heather; Davis, Roger B.; LaCroix, Andrea Z.; McCarthy, Ellen P.; Rosner, Bernard A.; Chlebowski, Rowan T.; Hankinson, Susan E.; Marcantonio, Edward R.; Ngo, Long H.

2016-01-01

Purpose Accurate risk assessment is necessary for decision-making around breast cancer prevention. We aimed to develop a breast cancer prediction model for postmenopausal women that would take into account their individualized competing risk of non-breast cancer death. Methods We included 73,066 women who completed the 2004 Nurses’ Health Study (NHS) questionnaire (all ≥57 years) and followed participants until May 2014. We considered 17 breast cancer risk factors (health behaviors, demographics, family history, reproductive factors), 7 risk factors for non-breast cancer death (comorbidities, functional dependency), and mammography use. We used competing risk regression to identify factors independently associated with breast cancer. We validated the final model by examining calibration (expected-to-observed ratio of breast cancer incidence, E/O) and discrimination (c-statistic) using 74,887 subjects from the Women’s Health Initiative Extension Study (WHI-ES; all were ≥55 years and followed for 5 years). Results Within 5 years, 1.8% of NHS participants were diagnosed with breast cancer (vs. 2.0% in WHI-ES, p=0.02) and 6.6% experienced non-breast cancer death (vs. 5.2% in WHI-ES, p<0.001). Using a model selection procedure which incorporated the Akaike Information Criterion, c-statistic, statistical significance, and clinical judgement, our final model included 9 breast cancer risk factors, 5 comorbidities, functional dependency, and mammography use. The model’s c-statistic was 0.61 (95% CI [0.60–0.63]) in NHS and 0.57 (0.55–0.58) in WHI-ES. On average our model under predicted breast cancer in WHI-ES (E/O 0.92 [0.88–0.97]). Conclusions We developed a novel prediction model that factors in postmenopausal women’s individualized competing risks of non-breast cancer death when estimating breast cancer risk. PMID:27770283

Statistical Analysis of Complexity Generators for Cost Estimation

NASA Technical Reports Server (NTRS)

Rowell, Ginger Holmes

1999-01-01

Predicting the cost of cutting edge new technologies involved with spacecraft hardware can be quite complicated. A new feature of the NASA Air Force Cost Model (NAFCOM), called the Complexity Generator, is being developed to model the complexity factors that drive the cost of space hardware. This parametric approach is also designed to account for the differences in cost, based on factors that are unique to each system and subsystem. The cost driver categories included in this model are weight, inheritance from previous missions, technical complexity, and management factors. This paper explains the Complexity Generator framework, the statistical methods used to select the best model within this framework, and the procedures used to find the region of predictability and the prediction intervals for the cost of a mission.

Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models.

PubMed

Sul, Jae Hoon; Bilow, Michael; Yang, Wen-Yun; Kostem, Emrah; Furlotte, Nick; He, Dan; Eskin, Eleazar

2016-03-01

Although genome-wide association studies (GWASs) have discovered numerous novel genetic variants associated with many complex traits and diseases, those genetic variants typically explain only a small fraction of phenotypic variance. Factors that account for phenotypic variance include environmental factors and gene-by-environment interactions (GEIs). Recently, several studies have conducted genome-wide gene-by-environment association analyses and demonstrated important roles of GEIs in complex traits. One of the main challenges in these association studies is to control effects of population structure that may cause spurious associations. Many studies have analyzed how population structure influences statistics of genetic variants and developed several statistical approaches to correct for population structure. However, the impact of population structure on GEI statistics in GWASs has not been extensively studied and nor have there been methods designed to correct for population structure on GEI statistics. In this paper, we show both analytically and empirically that population structure may cause spurious GEIs and use both simulation and two GWAS datasets to support our finding. We propose a statistical approach based on mixed models to account for population structure on GEI statistics. We find that our approach effectively controls population structure on statistics for GEIs as well as for genetic variants.

Investigating the role of background and observation error correlations in improving a model forecast of forest carbon balance using four dimensional variational data assimilation.

NASA Astrophysics Data System (ADS)

Pinnington, Ewan; Casella, Eric; Dance, Sarah; Lawless, Amos; Morison, James; Nichols, Nancy; Wilkinson, Matthew; Quaife, Tristan

2016-04-01

Forest ecosystems play an important role in sequestering human emitted carbon-dioxide from the atmosphere and therefore greatly reduce the effect of anthropogenic induced climate change. For that reason understanding their response to climate change is of great importance. Efforts to implement variational data assimilation routines with functional ecology models and land surface models have been limited, with sequential and Markov chain Monte Carlo data assimilation methods being prevalent. When data assimilation has been used with models of carbon balance, background "prior" errors and observation errors have largely been treated as independent and uncorrelated. Correlations between background errors have long been known to be a key aspect of data assimilation in numerical weather prediction. More recently, it has been shown that accounting for correlated observation errors in the assimilation algorithm can considerably improve data assimilation results and forecasts. In this paper we implement a 4D-Var scheme with a simple model of forest carbon balance, for joint parameter and state estimation and assimilate daily observations of Net Ecosystem CO2 Exchange (NEE) taken at the Alice Holt forest CO2 flux site in Hampshire, UK. We then investigate the effect of specifying correlations between parameter and state variables in background error statistics and the effect of specifying correlations in time between observation error statistics. The idea of including these correlations in time is new and has not been previously explored in carbon balance model data assimilation. In data assimilation, background and observation error statistics are often described by the background error covariance matrix and the observation error covariance matrix. We outline novel methods for creating correlated versions of these matrices, using a set of previously postulated dynamical constraints to include correlations in the background error statistics and a Gaussian correlation function to include time correlations in the observation error statistics. The methods used in this paper will allow the inclusion of time correlations between many different observation types in the assimilation algorithm, meaning that previously neglected information can be accounted for. In our experiments we compared the results using our new correlated background and observation error covariance matrices and those using diagonal covariance matrices. We found that using the new correlated matrices reduced the root mean square error in the 14 year forecast of daily NEE by 44 % decreasing from 4.22 g C m-2 day-1 to 2.38 g C m-2 day-1.

Statistical Analysis of Big Data on Pharmacogenomics

PubMed Central

Fan, Jianqing; Liu, Han

2013-01-01

This paper discusses statistical methods for estimating complex correlation structure from large pharmacogenomic datasets. We selectively review several prominent statistical methods for estimating large covariance matrix for understanding correlation structure, inverse covariance matrix for network modeling, large-scale simultaneous tests for selecting significantly differently expressed genes and proteins and genetic markers for complex diseases, and high dimensional variable selection for identifying important molecules for understanding molecule mechanisms in pharmacogenomics. Their applications to gene network estimation and biomarker selection are used to illustrate the methodological power. Several new challenges of Big data analysis, including complex data distribution, missing data, measurement error, spurious correlation, endogeneity, and the need for robust statistical methods, are also discussed. PMID:23602905

Multidimensional effects in nonadiabatic statistical theories of spin- forbidden kinetics. A case study of 3O + CO → CO 2

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

Jasper, Ahren

2015-04-14

The appropriateness of treating crossing seams of electronic states of different spins as nonadiabatic transition states in statistical calculations of spin-forbidden reaction rates is considered. We show that the spin-forbidden reaction coordinate, the nuclear coordinate perpendicular to the crossing seam, is coupled to the remaining nuclear degrees of freedom. We found that this coupling gives rise to multidimensional effects that are not typically included in statistical treatments of spin-forbidden kinetics. Three qualitative categories of multidimensional effects may be identified: static multidimensional effects due to the geometry-dependence of the local shape of the crossing seam and of the spin–orbit coupling, dynamicalmore » multidimensional effects due to energy exchange with the reaction coordinate during the seam crossing, and nonlocal(history-dependent) multidimensional effects due to interference of the electronic variables at second, third, and later seam crossings. Nonlocal multidimensional effects are intimately related to electronic decoherence, where electronic dephasing acts to erase the history of the system. A semiclassical model based on short-time full-dimensional trajectories that includes all three multidimensional effects as well as a model for electronic decoherence is presented. The results of this multidimensional nonadiabatic statistical theory (MNST) for the 3O + CO → CO 2 reaction are compared with the results of statistical theories employing one-dimensional (Landau–Zener and weak coupling) models for the transition probability and with those calculated previously using multistate trajectories. The MNST method is shown to accurately reproduce the multistate decay-of-mixing trajectory results, so long as consistent thresholds are used. Furthermore, the MNST approach has several advantages over multistate trajectory approaches and is more suitable in chemical kinetics calculations at low temperatures and for complex systems. The error in statistical calculations that neglect multidimensional effects is shown to be as large as a factor of 2 for this system, with static multidimensional effects identified as the largest source of error.« less

Monitoring the Earth System Grid Federation through the ESGF Dashboard

NASA Astrophysics Data System (ADS)

Fiore, S.; Bell, G. M.; Drach, B.; Williams, D.; Aloisio, G.

2012-12-01

The Climate Model Intercomparison Project, phase 5 (CMIP5) is a global effort coordinated by the World Climate Research Programme (WCRP) involving tens of modeling groups spanning 19 countries. It is expected the CMIP5 distributed data archive will total upwards of 3.5 petabytes, stored across several ESGF Nodes on four continents (North America, Europe, Asia, and Australia). The Earth System Grid Federation (ESGF) provides the IT infrastructure to support the CMIP5. In this regard, the monitoring of the distributed ESGF infrastructure represents a crucial part carried out by the ESGF Dashboard. The ESGF Dashboard is a software component of the ESGF stack, responsible for collecting key information about the status of the federation in terms of: 1) Network topology (peer-groups composition), 2) Node type (host/services mapping), 3) Registered users (including their Identity Providers), 4) System metrics (e.g., round-trip time, service availability, CPU, memory, disk, processes, etc.), 5) Download metrics (both at the Node and federation level). The last class of information is very important since it provides a strong insight of the CMIP5 experiment: the data usage statistics. In this regard, CMCC and LLNL have developed a data analytics management system for the analysis of both node-level and federation-level data usage statistics. It provides data usage statistics aggregated by project, model, experiment, variable, realm, peer node, time, ensemble, datasetname (including version), etc. The back-end of the system is able to infer the data usage information of the entire federation, by carrying out: - at node level: a 18-step reconciliation process on the peer node databases (i.e. node manager and publisher DB) which provides a 15-dimension datawarehouse with local statistics and - at global level: an aggregation process which federates the data usage statistics into a 16-dimension datawarehouse with federation-level data usage statistics. The front-end of the Dashboard system exploits a web desktop approach, which joins the pervasivity of a web application with the flexibility of a desktop one.

Nonlinear GARCH model and 1 / f noise

NASA Astrophysics Data System (ADS)

Kononovicius, A.; Ruseckas, J.

2015-06-01

Auto-regressive conditionally heteroskedastic (ARCH) family models are still used, by practitioners in business and economic policy making, as a conditional volatility forecasting models. Furthermore ARCH models still are attracting an interest of the researchers. In this contribution we consider the well known GARCH(1,1) process and its nonlinear modifications, reminiscent of NGARCH model. We investigate the possibility to reproduce power law statistics, probability density function and power spectral density, using ARCH family models. For this purpose we derive stochastic differential equations from the GARCH processes in consideration. We find the obtained equations to be similar to a general class of stochastic differential equations known to reproduce power law statistics. We show that linear GARCH(1,1) process has power law distribution, but its power spectral density is Brownian noise-like. However, the nonlinear modifications exhibit both power law distribution and power spectral density of the 1 /fβ form, including 1 / f noise.

RAD-ADAPT: Software for modelling clonogenic assay data in radiation biology.

PubMed

Zhang, Yaping; Hu, Kaiqiang; Beumer, Jan H; Bakkenist, Christopher J; D'Argenio, David Z

2017-04-01

We present a comprehensive software program, RAD-ADAPT, for the quantitative analysis of clonogenic assays in radiation biology. Two commonly used models for clonogenic assay analysis, the linear-quadratic model and single-hit multi-target model, are included in the software. RAD-ADAPT uses maximum likelihood estimation method to obtain parameter estimates with the assumption that cell colony count data follow a Poisson distribution. The program has an intuitive interface, generates model prediction plots, tabulates model parameter estimates, and allows automatic statistical comparison of parameters between different groups. The RAD-ADAPT interface is written using the statistical software R and the underlying computations are accomplished by the ADAPT software system for pharmacokinetic/pharmacodynamic systems analysis. The use of RAD-ADAPT is demonstrated using an example that examines the impact of pharmacologic ATM and ATR kinase inhibition on human lung cancer cell line A549 after ionizing radiation. Copyright © 2017 Elsevier B.V. All rights reserved.

Sex-Specific Prediction Models for Sleep Apnea From the Hispanic Community Health Study/Study of Latinos.

PubMed

Shah, Neomi; Hanna, David B; Teng, Yanping; Sotres-Alvarez, Daniela; Hall, Martica; Loredo, Jose S; Zee, Phyllis; Kim, Mimi; Yaggi, H Klar; Redline, Susan; Kaplan, Robert C

2016-06-01

We developed and validated the first-ever sleep apnea (SA) risk calculator in a large population-based cohort of Hispanic/Latino subjects. Cross-sectional data on adults from the Hispanic Community Health Study/Study of Latinos (2008-2011) were analyzed. Subjective and objective sleep measurements were obtained. Clinically significant SA was defined as an apnea-hypopnea index ≥ 15 events per hour. Using logistic regression, four prediction models were created: three sex-specific models (female-only, male-only, and a sex × covariate interaction model to allow differential predictor effects), and one overall model with sex included as a main effect only. Models underwent 10-fold cross-validation and were assessed by using the C statistic. SA and its predictive variables; a total of 17 variables were considered. A total of 12,158 participants had complete sleep data available; 7,363 (61%) were women. The population-weighted prevalence of SA (apnea-hypopnea index ≥ 15 events per hour) was 6.1% in female subjects and 13.5% in male subjects. Male-only (C statistic, 0.808) and female-only (C statistic, 0.836) prediction models had the same predictor variables (ie, age, BMI, self-reported snoring). The sex-interaction model (C statistic, 0.836) contained sex, age, age × sex, BMI, BMI × sex, and self-reported snoring. The final overall model (C statistic, 0.832) contained age, BMI, snoring, and sex. We developed two websites for our SA risk calculator: one in English (https://www.montefiore.org/sleepapneariskcalc.html) and another in Spanish (http://www.montefiore.org/sleepapneariskcalc-es.html). We created an internally validated, highly discriminating, well-calibrated, and parsimonious prediction model for SA. Contrary to the study hypothesis, the variables did not have different predictive magnitudes in male and female subjects. Copyright © 2016 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Multivariate statistical approach to estimate mixing proportions for unknown end members

USGS Publications Warehouse

Valder, Joshua F.; Long, Andrew J.; Davis, Arden D.; Kenner, Scott J.

2012-01-01

A multivariate statistical method is presented, which includes principal components analysis (PCA) and an end-member mixing model to estimate unknown end-member hydrochemical compositions and the relative mixing proportions of those end members in mixed waters. PCA, together with the Hotelling T2 statistic and a conceptual model of groundwater flow and mixing, was used in selecting samples that best approximate end members, which then were used as initial values in optimization of the end-member mixing model. This method was tested on controlled datasets (i.e., true values of estimates were known a priori) and found effective in estimating these end members and mixing proportions. The controlled datasets included synthetically generated hydrochemical data, synthetically generated mixing proportions, and laboratory analyses of sample mixtures, which were used in an evaluation of the effectiveness of this method for potential use in actual hydrological settings. For three different scenarios tested, correlation coefficients (R2) for linear regression between the estimated and known values ranged from 0.968 to 0.993 for mixing proportions and from 0.839 to 0.998 for end-member compositions. The method also was applied to field data from a study of end-member mixing in groundwater as a field example and partial method validation.

Systematic review of statistically-derived models of immunological response in HIV-infected adults on antiretroviral therapy in Sub-Saharan Africa.

PubMed

Sempa, Joseph B; Ujeneza, Eva L; Nieuwoudt, Martin

2017-01-01

In Sub-Saharan African (SSA) resource limited settings, Cluster of Differentiation 4 (CD4) counts continue to be used for clinical decision making in antiretroviral therapy (ART). Here, HIV-infected people often remain with CD4 counts <350 cells/μL even after 5 years of viral load suppression. Ongoing immunological monitoring is necessary. Due to varying statistical modeling methods comparing immune response to ART across different cohorts is difficult. We systematically review such models and detail the similarities, differences and problems. 'Preferred Reporting Items for Systematic Review and Meta-Analyses' guidelines were used. Only studies of immune-response after ART initiation from SSA in adults were included. Data was extracted from each study and tabulated. Outcomes were categorized into 3 groups: 'slope', 'survival', and 'asymptote' models. Wordclouds were drawn wherein the frequency of variables occurring in the reviewed models is indicated by their size and color. 69 covariates were identified in the final models of 35 studies. Effect sizes of covariates were not directly quantitatively comparable in view of the combination of differing variables and scale transformation methods across models. Wordclouds enabled the identification of qualitative and semi-quantitative covariate sets for each outcome category. Comparison across categories identified sex, baseline age, baseline log viral load, baseline CD4, ART initiation regimen and ART duration as a minimal consensus set. Most models were different with respect to covariates included, variable transformations and scales, model assumptions, modelling strategies and reporting methods, even for the same outcomes. To enable comparison across cohorts, statistical models would benefit from the application of more uniform modelling techniques. Historic efforts have produced results that are anecdotal to individual cohorts only. This study was able to define 'prior' knowledge in the Bayesian sense. Such information has value for prospective modelling efforts.

Theoretical approaches to the steady-state statistical physics of interacting dissipative units

NASA Astrophysics Data System (ADS)

Bertin, Eric

2017-02-01

The aim of this review is to provide a concise overview of some of the generic approaches that have been developed to deal with the statistical description of large systems of interacting dissipative ‘units’. The latter notion includes, e.g. inelastic grains, active or self-propelled particles, bubbles in a foam, low-dimensional dynamical systems like driven oscillators, or even spatially extended modes like Fourier modes of the velocity field in a fluid. We first review methods based on the statistical properties of a single unit, starting with elementary mean-field approximations, either static or dynamic, that describe a unit embedded in a ‘self-consistent’ environment. We then discuss how this basic mean-field approach can be extended to account for spatial dependences, in the form of space-dependent mean-field Fokker-Planck equations, for example. We also briefly review the use of kinetic theory in the framework of the Boltzmann equation, which is an appropriate description for dilute systems. We then turn to descriptions in terms of the full N-body distribution, starting from exact solutions of one-dimensional models, using a matrix-product ansatz method when correlations are present. Since exactly solvable models are scarce, we also present some approximation methods which can be used to determine the N-body distribution in a large system of dissipative units. These methods include the Edwards approach for dense granular matter and the approximate treatment of multiparticle Langevin equations with colored noise, which models systems of self-propelled particles. Throughout this review, emphasis is put on methodological aspects of the statistical modeling and on formal similarities between different physical problems, rather than on the specific behavior of a given system.

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

Apte, A; Veeraraghavan, H; Oh, J

Purpose: To present an open source and free platform to facilitate radiomics research — The “Radiomics toolbox” in CERR. Method: There is scarcity of open source tools that support end-to-end modeling of image features to predict patient outcomes. The “Radiomics toolbox” strives to fill the need for such a software platform. The platform supports (1) import of various kinds of image modalities like CT, PET, MR, SPECT, US. (2) Contouring tools to delineate structures of interest. (3) Extraction and storage of image based features like 1st order statistics, gray-scale co-occurrence and zonesize matrix based texture features and shape features andmore » (4) Statistical Analysis. Statistical analysis of the extracted features is supported with basic functionality that includes univariate correlations, Kaplan-Meir curves and advanced functionality that includes feature reduction and multivariate modeling. The graphical user interface and the data management are performed with Matlab for the ease of development and readability of code and features for wide audience. Open-source software developed with other programming languages is integrated to enhance various components of this toolbox. For example: Java-based DCM4CHE for import of DICOM, R for statistical analysis. Results: The Radiomics toolbox will be distributed as an open source, GNU copyrighted software. The toolbox was prototyped for modeling Oropharyngeal PET dataset at MSKCC. The analysis will be presented in a separate paper. Conclusion: The Radiomics Toolbox provides an extensible platform for extracting and modeling image features. To emphasize new uses of CERR for radiomics and image-based research, we have changed the name from the “Computational Environment for Radiotherapy Research” to the “Computational Environment for Radiological Research”.« less

Pitfalls in statistical landslide susceptibility modelling

NASA Astrophysics Data System (ADS)

Schröder, Boris; Vorpahl, Peter; Märker, Michael; Elsenbeer, Helmut

2010-05-01

The use of statistical methods is a well-established approach to predict landslide occurrence probabilities and to assess landslide susceptibility. This is achieved by applying statistical methods relating historical landslide inventories to topographic indices as predictor variables. In our contribution, we compare several new and powerful methods developed in machine learning and well-established in landscape ecology and macroecology for predicting the distribution of shallow landslides in tropical mountain rainforests in southern Ecuador (among others: boosted regression trees, multivariate adaptive regression splines, maximum entropy). Although these methods are powerful, we think it is necessary to follow a basic set of guidelines to avoid some pitfalls regarding data sampling, predictor selection, and model quality assessment, especially if a comparison of different models is contemplated. We therefore suggest to apply a novel toolbox to evaluate approaches to the statistical modelling of landslide susceptibility. Additionally, we propose some methods to open the "black box" as an inherent part of machine learning methods in order to achieve further explanatory insights into preparatory factors that control landslides. Sampling of training data should be guided by hypotheses regarding processes that lead to slope failure taking into account their respective spatial scales. This approach leads to the selection of a set of candidate predictor variables considered on adequate spatial scales. This set should be checked for multicollinearity in order to facilitate model response curve interpretation. Model quality assesses how well a model is able to reproduce independent observations of its response variable. This includes criteria to evaluate different aspects of model performance, i.e. model discrimination, model calibration, and model refinement. In order to assess a possible violation of the assumption of independency in the training samples or a possible lack of explanatory information in the chosen set of predictor variables, the model residuals need to be checked for spatial auto¬correlation. Therefore, we calculate spline correlograms. In addition to this, we investigate partial dependency plots and bivariate interactions plots considering possible interactions between predictors to improve model interpretation. Aiming at presenting this toolbox for model quality assessment, we investigate the influence of strategies in the construction of training datasets for statistical models on model quality.

Mild cognitive impairment and fMRI studies of brain functional connectivity: the state of the art

PubMed Central

Farràs-Permanyer, Laia; Guàrdia-Olmos, Joan; Peró-Cebollero, Maribel

2015-01-01

In the last 15 years, many articles have studied brain connectivity in Mild Cognitive Impairment patients with fMRI techniques, seemingly using different connectivity statistical models in each investigation to identify complex connectivity structures so as to recognize typical behavior in this type of patient. This diversity in statistical approaches may cause problems in results comparison. This paper seeks to describe how researchers approached the study of brain connectivity in MCI patients using fMRI techniques from 2002 to 2014. The focus is on the statistical analysis proposed by each research group in reference to the limitations and possibilities of those techniques to identify some recommendations to improve the study of functional connectivity. The included articles came from a search of Web of Science and PsycINFO using the following keywords: f MRI, MCI, and functional connectivity. Eighty-one papers were found, but two of them were discarded because of the lack of statistical analysis. Accordingly, 79 articles were included in this review. We summarized some parts of the articles, including the goal of every investigation, the cognitive paradigm and methods used, brain regions involved, use of ROI analysis and statistical analysis, emphasizing on the connectivity estimation model used in each investigation. The present analysis allowed us to confirm the remarkable variability of the statistical analysis methods found. Additionally, the study of brain connectivity in this type of population is not providing, at the moment, any significant information or results related to clinical aspects relevant for prediction and treatment. We propose to follow guidelines for publishing fMRI data that would be a good solution to the problem of study replication. The latter aspect could be important for future publications because a higher homogeneity would benefit the comparison between publications and the generalization of results. PMID:26300802

Filter Tuning Using the Chi-Squared Statistic

NASA Technical Reports Server (NTRS)

Lilly-Salkowski, Tyler B.

2017-01-01

This paper examines the use of the Chi-square statistic as a means of evaluating filter performance. The goal of the process is to characterize the filter performance in the metric of covariance realism. The Chi-squared statistic is the value calculated to determine the realism of a covariance based on the prediction accuracy and the covariance values at a given point in time. Once calculated, it is the distribution of this statistic that provides insight on the accuracy of the covariance. The process of tuning an Extended Kalman Filter (EKF) for Aqua and Aura support is described, including examination of the measurement errors of available observation types, and methods of dealing with potentially volatile atmospheric drag modeling. Predictive accuracy and the distribution of the Chi-squared statistic, calculated from EKF solutions, are assessed.

Chern-Simons Term: Theory and Applications.

NASA Astrophysics Data System (ADS)

Gupta, Kumar Sankar

1992-01-01

We investigate the quantization and applications of Chern-Simons theories to several systems of interest. Elementary canonical methods are employed for the quantization of abelian and nonabelian Chern-Simons actions using ideas from gauge theories and quantum gravity. When the spatial slice is a disc, it yields quantum states at the edge of the disc carrying a representation of the Kac-Moody algebra. We next include sources in this model and their quantum states are shown to be those of a conformal family. Vertex operators for both abelian and nonabelian sources are constructed. The regularized abelian Wilson line is proved to be a vertex operator. The spin-statistics theorem is established for Chern-Simons dynamics using purely geometrical techniques. Chern-Simons action is associated with exotic spin and statistics in 2 + 1 dimensions. We study several systems in which the Chern-Simons action affects the spin and statistics. The first class of systems we study consist of G/H models. The solitons of these models are shown to obey anyonic statistics in the presence of a Chern-Simons term. The second system deals with the effect of the Chern -Simons term in a model for high temperature superconductivity. The coefficient of the Chern-Simons term is shown to be quantized, one of its possible values giving fermionic statistics to the solitons of this model. Finally, we study a system of spinning particles interacting with 2 + 1 gravity, the latter being described by an ISO(2,1) Chern-Simons term. An effective action for the particles is obtained by integrating out the gauge fields. Next we construct operators which exchange the particles. They are shown to satisfy the braid relations. There are ambiguities in the quantization of this system which can be exploited to give anyonic statistics to the particles. We also point out that at the level of the first quantized theory, the usual spin-statistics relation need not apply to these particles.

Comment on “Two statistics for evaluating parameter identifiability and error reduction” by John Doherty and Randall J. Hunt

USGS Publications Warehouse

Hill, Mary C.

2010-01-01

Doherty and Hunt (2009) present important ideas for first-order-second moment sensitivity analysis, but five issues are discussed in this comment. First, considering the composite-scaled sensitivity (CSS) jointly with parameter correlation coefficients (PCC) in a CSS/PCC analysis addresses the difficulties with CSS mentioned in the introduction. Second, their new parameter identifiability statistic actually is likely to do a poor job of parameter identifiability in common situations. The statistic instead performs the very useful role of showing how model parameters are included in the estimated singular value decomposition (SVD) parameters. Its close relation to CSS is shown. Third, the idea from p. 125 that a suitable truncation point for SVD parameters can be identified using the prediction variance is challenged using results from Moore and Doherty (2005). Fourth, the relative error reduction statistic of Doherty and Hunt is shown to belong to an emerging set of statistics here named perturbed calculated variance statistics. Finally, the perturbed calculated variance statistics OPR and PPR mentioned on p. 121 are shown to explicitly include the parameter null-space component of uncertainty. Indeed, OPR and PPR results that account for null-space uncertainty have appeared in the literature since 2000.

Statistical Patterns of Ionospheric Convection Derived From Mid-Latitude, High-Latitude, and Polar SuperDARN HF Radar Observations

NASA Astrophysics Data System (ADS)

Thomas, E. G.; Shepherd, S. G.

2017-12-01

Global patterns of ionospheric convection have been widely studied in terms of the interplanetary magnetic field (IMF) magnitude and orientation in both the Northern and Southern Hemispheres using observations from the Super Dual Auroral Radar Network (SuperDARN). The dynamic range of driving conditions under which existing SuperDARN statistical models are valid is currently limited to periods when the high-latitude convection pattern remains above about 60° geomagnetic latitude. Cousins and Shepherd [2010] found this to correspond to intervals when the solar wind electric field Esw < 4.1 mV/m and IMF Bz is negative. Conversely, under northward IMF conditions (Bz > 0) the high-latitude radars often experience difficulties in measuring convection above about 85° geomagnetic latitude. In this presentation, we introduce a new statistical model of ionospheric convection which is valid for much more dominant IMF Bz conditions than was previously possible by including velocity measurements from the newly constructed tiers of radars in the Northern Hemisphere at midlatitudes and in the polar cap. This new model (TS17) is compared to previous statistical models derived from high-latitude SuperDARN observations (RG96, PSR10, CS10) and its impact on instantaneous Map Potential solutions is examined.

Enhanced understanding of the relationship between erection and satisfaction in ED treatment: application of a longitudinal mediation model.

PubMed

Bushmakin, A G; Cappelleri, J C; Symonds, T; Stecher, V J

2014-01-01

To apportion the direct effect and the indirect effect (through erections) that sildenafil (vs placebo) has on individual satisfaction and couple satisfaction over time, longitudinal mediation modeling was applied to outcomes on the Sexual Experience Questionnaire. The model included data from weeks 4 and 10 (double-blind phase) and week 16 (open-label phase) of a controlled study. Data from 167 patients with erectile dysfunction (ED) were available for analysis. Estimation of statistical significance was based on bootstrap simulations, which allowed inferences at and between time points. Percentages (and corresponding 95% confidence intervals) for direct and indirect effects of treatment were calculated using the model. For the individual satisfaction and couple satisfaction domains, direct treatment effects were negligible (not statistically significant) whereas indirect treatment effects via the erection domain represented >90% of the treatment effects (statistically significant). Week 4 vs week 10 percentages of direct and indirect effects were not statistically different, indicating that the mediation effects are longitudinally invariant. As there was no placebo arm in the open-label phase, mediation effects at week 16 were not estimable. In conclusion, erection has a crucial role as a mediator in restoring individual satisfaction and couple satisfaction in men with ED treated with sildenafil.

Inevitable end-of-21st-century trends toward earlier surface runoff timing in California's Sierra Nevada Mountains

NASA Astrophysics Data System (ADS)

Schwartz, M. A.; Hall, A. D.; Sun, F.; Walton, D.; Berg, N.

2015-12-01

Hybrid dynamical-statistical downscaling is used to produce surface runoff timing projections for California's Sierra Nevada, a high-elevation mountain range with significant seasonal snow cover. First, future climate change projections (RCP8.5 forcing scenario, 2081-2100 period) from five CMIP5 global climate models (GCMs) are dynamically downscaled. These projections reveal that future warming leads to a shift toward earlier snowmelt and surface runoff timing throughout the Sierra Nevada region. Relationships between warming and surface runoff timing from the dynamical simulations are used to build a simple statistical model that mimics the dynamical model's projected surface runoff timing changes given GCM input or other statistically-downscaled input. This statistical model can be used to produce surface runoff timing projections for other GCMs, periods, and forcing scenarios to quantify ensemble-mean changes, uncertainty due to intermodel variability and consequences stemming from choice of forcing scenario. For all CMIP5 GCMs and forcing scenarios, significant trends toward earlier surface runoff timing occur at elevations below 2500m. Thus, we conclude that trends toward earlier surface runoff timing by the end-of-the-21st century are inevitable. The changes to surface runoff timing diagnosed in this study have implications for many dimensions of climate change, including impacts on surface hydrology, water resources, and ecosystems.

Dose response explorer: an integrated open-source tool for exploring and modelling radiotherapy dose volume outcome relationships

NASA Astrophysics Data System (ADS)

El Naqa, I.; Suneja, G.; Lindsay, P. E.; Hope, A. J.; Alaly, J. R.; Vicic, M.; Bradley, J. D.; Apte, A.; Deasy, J. O.

2006-11-01

Radiotherapy treatment outcome models are a complicated function of treatment, clinical and biological factors. Our objective is to provide clinicians and scientists with an accurate, flexible and user-friendly software tool to explore radiotherapy outcomes data and build statistical tumour control or normal tissue complications models. The software tool, called the dose response explorer system (DREES), is based on Matlab, and uses a named-field structure array data type. DREES/Matlab in combination with another open-source tool (CERR) provides an environment for analysing treatment outcomes. DREES provides many radiotherapy outcome modelling features, including (1) fitting of analytical normal tissue complication probability (NTCP) and tumour control probability (TCP) models, (2) combined modelling of multiple dose-volume variables (e.g., mean dose, max dose, etc) and clinical factors (age, gender, stage, etc) using multi-term regression modelling, (3) manual or automated selection of logistic or actuarial model variables using bootstrap statistical resampling, (4) estimation of uncertainty in model parameters, (5) performance assessment of univariate and multivariate analyses using Spearman's rank correlation and chi-square statistics, boxplots, nomograms, Kaplan-Meier survival plots, and receiver operating characteristics curves, and (6) graphical capabilities to visualize NTCP or TCP prediction versus selected variable models using various plots. DREES provides clinical researchers with a tool customized for radiotherapy outcome modelling. DREES is freely distributed. We expect to continue developing DREES based on user feedback.

Conjoint Analysis: A Study of the Effects of Using Person Variables.

ERIC Educational Resources Information Center

Fraas, John W.; Newman, Isadore

Three statistical techniques--conjoint analysis, a multiple linear regression model, and a multiple linear regression model with a surrogate person variable--were used to estimate the relative importance of five university attributes for students in the process of selecting a college. The five attributes include: availability and variety of…

Suppressor Variables: The Difference between "Is" versus "Acting As"

ERIC Educational Resources Information Center

Ludlow, Larry; Klein, Kelsey

2014-01-01

Correlated predictors in regression models are a fact of life in applied social science research. The extent to which they are correlated will influence the estimates and statistics associated with the other variables they are modeled along with. These effects, for example, may include enhanced regression coefficients for the other variables--a…

A Comparison of Conventional Linear Regression Methods and Neural Networks for Forecasting Educational Spending.

ERIC Educational Resources Information Center

Baker, Bruce D.; Richards, Craig E.

1999-01-01

Applies neural network methods for forecasting 1991-95 per-pupil expenditures in U.S. public elementary and secondary schools. Forecasting models included the National Center for Education Statistics' multivariate regression model and three neural architectures. Regarding prediction accuracy, neural network results were comparable or superior to…

The Five-Factor Model of Personality Traits and Organizational Citizenship Behaviors: A Meta-Analysis

ERIC Educational Resources Information Center

Chiaburu, Dan S.; Oh, In-Sue; Berry, Christopher M.; Li, Ning; Gardner, Richard G.

2011-01-01

Using meta-analytic tests based on 87 statistically independent samples, we investigated the relationships between the five-factor model (FFM) of personality traits and organizational citizenship behaviors in both the aggregate and specific forms, including individual-directed, organization-directed, and change-oriented citizenship. We found that…

Teacher's Corner: Structural Equation Modeling with the Sem Package in R

ERIC Educational Resources Information Center

Fox, John

2006-01-01

R is free, open-source, cooperatively developed software that implements the S statistical programming language and computing environment. The current capabilities of R are extensive, and it is in wide use, especially among statisticians. The sem package provides basic structural equation modeling facilities in R, including the ability to fit…

Testing the Self-Efficacy-Performance Linkage of Social-Cognitive Theory.

ERIC Educational Resources Information Center

Harrison, Allison W.; Rainer, R. Kelly, Jr.; Hochwarter, Wayne A.; Thompson, Kenneth R.

1997-01-01

Briefly reviews Albert Bandura's Self-Efficacy Performance Model (ability to perform a task is influenced by an individual's belief in their capability). Tests this model with a sample of 776 university employees and computer-related knowledge and skills. Results supported Bandura's thesis. Includes statistical tables and a discussion of related…

Does money matter in inflation forecasting?

NASA Astrophysics Data System (ADS)

Binner, J. M.; Tino, P.; Tepper, J.; Anderson, R.; Jones, B.; Kendall, G.

2010-11-01

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two nonlinear techniques, namely, recurrent neural networks and kernel recursive least squares regression-techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naïve random walk model. The best models were nonlinear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. Beyond its economic findings, our study is in the tradition of physicists’ long-standing interest in the interconnections among statistical mechanics, neural networks, and related nonparametric statistical methods, and suggests potential avenues of extension for such studies.

Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics

PubMed Central

Coen-Cagli, Ruben; Dayan, Peter; Schwartz, Odelia

2012-01-01

Spatial context in images induces perceptual phenomena associated with salience and modulates the responses of neurons in primary visual cortex (V1). However, the computational and ecological principles underlying contextual effects are incompletely understood. We introduce a model of natural images that includes grouping and segmentation of neighboring features based on their joint statistics, and we interpret the firing rates of V1 neurons as performing optimal recognition in this model. We show that this leads to a substantial generalization of divisive normalization, a computation that is ubiquitous in many neural areas and systems. A main novelty in our model is that the influence of the context on a target stimulus is determined by their degree of statistical dependence. We optimized the parameters of the model on natural image patches, and then simulated neural and perceptual responses on stimuli used in classical experiments. The model reproduces some rich and complex response patterns observed in V1, such as the contrast dependence, orientation tuning and spatial asymmetry of surround suppression, while also allowing for surround facilitation under conditions of weak stimulation. It also mimics the perceptual salience produced by simple displays, and leads to readily testable predictions. Our results provide a principled account of orientation-based contextual modulation in early vision and its sensitivity to the homogeneity and spatial arrangement of inputs, and lends statistical support to the theory that V1 computes visual salience. PMID:22396635

Computer-aided auditing of prescription drug claims.

PubMed

Iyengar, Vijay S; Hermiz, Keith B; Natarajan, Ramesh

2014-09-01

We describe a methodology for identifying and ranking candidate audit targets from a database of prescription drug claims. The relevant audit targets may include various entities such as prescribers, patients and pharmacies, who exhibit certain statistical behavior indicative of potential fraud and abuse over the prescription claims during a specified period of interest. Our overall approach is consistent with related work in statistical methods for detection of fraud and abuse, but has a relative emphasis on three specific aspects: first, based on the assessment of domain experts, certain focus areas are selected and data elements pertinent to the audit analysis in each focus area are identified; second, specialized statistical models are developed to characterize the normalized baseline behavior in each focus area; and third, statistical hypothesis testing is used to identify entities that diverge significantly from their expected behavior according to the relevant baseline model. The application of this overall methodology to a prescription claims database from a large health plan is considered in detail.

Reconciling statistical and systems science approaches to public health.

PubMed

Ip, Edward H; Rahmandad, Hazhir; Shoham, David A; Hammond, Ross; Huang, Terry T-K; Wang, Youfa; Mabry, Patricia L

2013-10-01

Although systems science has emerged as a set of innovative approaches to study complex phenomena, many topically focused researchers including clinicians and scientists working in public health are somewhat befuddled by this methodology that at times appears to be radically different from analytic methods, such as statistical modeling, to which the researchers are accustomed. There also appears to be conflicts between complex systems approaches and traditional statistical methodologies, both in terms of their underlying strategies and the languages they use. We argue that the conflicts are resolvable, and the sooner the better for the field. In this article, we show how statistical and systems science approaches can be reconciled, and how together they can advance solutions to complex problems. We do this by comparing the methods within a theoretical framework based on the work of population biologist Richard Levins. We present different types of models as representing different tradeoffs among the four desiderata of generality, realism, fit, and precision.

Reconciling Statistical and Systems Science Approaches to Public Health

PubMed Central

Ip, Edward H.; Rahmandad, Hazhir; Shoham, David A.; Hammond, Ross; Huang, Terry T.-K.; Wang, Youfa; Mabry, Patricia L.

2016-01-01

Although systems science has emerged as a set of innovative approaches to study complex phenomena, many topically focused researchers including clinicians and scientists working in public health are somewhat befuddled by this methodology that at times appears to be radically different from analytic methods, such as statistical modeling, to which the researchers are accustomed. There also appears to be conflicts between complex systems approaches and traditional statistical methodologies, both in terms of their underlying strategies and the languages they use. We argue that the conflicts are resolvable, and the sooner the better for the field. In this article, we show how statistical and systems science approaches can be reconciled, and how together they can advance solutions to complex problems. We do this by comparing the methods within a theoretical framework based on the work of population biologist Richard Levins. We present different types of models as representing different tradeoffs among the four desiderata of generality, realism, fit, and precision. PMID:24084395

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.

Fundamental questions of earthquake statistics, source behavior, and the estimation of earthquake probabilities from possible foreshocks

USGS Publications Warehouse

Michael, Andrew J.

2012-01-01

Estimates of the probability that an ML 4.8 earthquake, which occurred near the southern end of the San Andreas fault on 24 March 2009, would be followed by an M 7 mainshock over the following three days vary from 0.0009 using a Gutenberg–Richter model of aftershock statistics (Reasenberg and Jones, 1989) to 0.04 using a statistical model of foreshock behavior and long‐term estimates of large earthquake probabilities, including characteristic earthquakes (Agnew and Jones, 1991). I demonstrate that the disparity between the existing approaches depends on whether or not they conform to Gutenberg–Richter behavior. While Gutenberg–Richter behavior is well established over large regions, it could be violated on individual faults if they have characteristic earthquakes or over small areas if the spatial distribution of large‐event nucleations is disproportional to the rate of smaller events. I develop a new form of the aftershock model that includes characteristic behavior and combines the features of both models. This new model and the older foreshock model yield the same results when given the same inputs, but the new model has the advantage of producing probabilities for events of all magnitudes, rather than just for events larger than the initial one. Compared with the aftershock model, the new model has the advantage of taking into account long‐term earthquake probability models. Using consistent parameters, the probability of an M 7 mainshock on the southernmost San Andreas fault is 0.0001 for three days from long‐term models and the clustering probabilities following the ML 4.8 event are 0.00035 for a Gutenberg–Richter distribution and 0.013 for a characteristic‐earthquake magnitude–frequency distribution. Our decisions about the existence of characteristic earthquakes and how large earthquakes nucleate have a first‐order effect on the probabilities obtained from short‐term clustering models for these large events.

An innovative statistical approach for analysing non-continuous variables in environmental monitoring: assessing temporal trends of TBT pollution.

PubMed

Santos, José António; Galante-Oliveira, Susana; Barroso, Carlos

2011-03-01

The current work presents an innovative statistical approach to model ordinal variables in environmental monitoring studies. An ordinal variable has values that can only be compared as "less", "equal" or "greater" and it is not possible to have information about the size of the difference between two particular values. The example of ordinal variable under this study is the vas deferens sequence (VDS) used in imposex (superimposition of male sexual characters onto prosobranch females) field assessment programmes for monitoring tributyltin (TBT) pollution. The statistical methodology presented here is the ordered logit regression model. It assumes that the VDS is an ordinal variable whose values match up a process of imposex development that can be considered continuous in both biological and statistical senses and can be described by a latent non-observable continuous variable. This model was applied to the case study of Nucella lapillus imposex monitoring surveys conducted in the Portuguese coast between 2003 and 2008 to evaluate the temporal evolution of TBT pollution in this country. In order to produce more reliable conclusions, the proposed model includes covariates that may influence the imposex response besides TBT (e.g. the shell size). The model also provides an analysis of the environmental risk associated to TBT pollution by estimating the probability of the occurrence of females with VDS ≥ 2 in each year, according to OSPAR criteria. We consider that the proposed application of this statistical methodology has a great potential in environmental monitoring whenever there is the need to model variables that can only be assessed through an ordinal scale of values.

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

A new statistical method for characterizing the atmospheres of extrasolar planets

NASA Astrophysics Data System (ADS)

Henderson, Cassandra S.; Skemer, Andrew J.; Morley, Caroline V.; Fortney, Jonathan J.

2017-10-01

By detecting light from extrasolar planets, we can measure their compositions and bulk physical properties. The technologies used to make these measurements are still in their infancy, and a lack of self-consistency suggests that previous observations have underestimated their systemic errors. We demonstrate a statistical method, newly applied to exoplanet characterization, which uses a Bayesian formalism to account for underestimated errorbars. We use this method to compare photometry of a substellar companion, GJ 758b, with custom atmospheric models. Our method produces a probability distribution of atmospheric model parameters including temperature, gravity, cloud model (fsed) and chemical abundance for GJ 758b. This distribution is less sensitive to highly variant data and appropriately reflects a greater uncertainty on parameter fits.

Bayesian methods in reliability

NASA Astrophysics Data System (ADS)

Sander, P.; Badoux, R.

1991-11-01

The present proceedings from a course on Bayesian methods in reliability encompasses Bayesian statistical methods and their computational implementation, models for analyzing censored data from nonrepairable systems, the traits of repairable systems and growth models, the use of expert judgment, and a review of the problem of forecasting software reliability. Specific issues addressed include the use of Bayesian methods to estimate the leak rate of a gas pipeline, approximate analyses under great prior uncertainty, reliability estimation techniques, and a nonhomogeneous Poisson process. Also addressed are the calibration sets and seed variables of expert judgment systems for risk assessment, experimental illustrations of the use of expert judgment for reliability testing, and analyses of the predictive quality of software-reliability growth models such as the Weibull order statistics.

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.

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

Statistical ensembles for money and debt

NASA Astrophysics Data System (ADS)

Viaggiu, Stefano; Lionetto, Andrea; Bargigli, Leonardo; Longo, Michele

2012-10-01

We build a statistical ensemble representation of two economic models describing respectively, in simplified terms, a payment system and a credit market. To this purpose we adopt the Boltzmann-Gibbs distribution where the role of the Hamiltonian is taken by the total money supply (i.e. including money created from debt) of a set of interacting economic agents. As a result, we can read the main thermodynamic quantities in terms of monetary ones. In particular, we define for the credit market model a work term which is related to the impact of monetary policy on credit creation. Furthermore, with our formalism we recover and extend some results concerning the temperature of an economic system, previously presented in the literature by considering only the monetary base as a conserved quantity. Finally, we study the statistical ensemble for the Pareto distribution.

Statistical Mechanics of Node-perturbation Learning with Noisy Baseline

NASA Astrophysics Data System (ADS)

Hara, Kazuyuki; Katahira, Kentaro; Okada, Masato

2017-02-01

Node-perturbation learning is a type of statistical gradient descent algorithm that can be applied to problems where the objective function is not explicitly formulated, including reinforcement learning. It estimates the gradient of an objective function by using the change in the object function in response to the perturbation. The value of the objective function for an unperturbed output is called a baseline. Cho et al. proposed node-perturbation learning with a noisy baseline. In this paper, we report on building the statistical mechanics of Cho's model and on deriving coupled differential equations of order parameters that depict learning dynamics. We also show how to derive the generalization error by solving the differential equations of order parameters. On the basis of the results, we show that Cho's results are also apply in general cases and show some general performances of Cho's model.

Statistical modelling of subdiffusive dynamics in the cytoplasm of living cells: A FARIMA approach

NASA Astrophysics Data System (ADS)

Burnecki, K.; Muszkieta, M.; Sikora, G.; Weron, A.

2012-04-01

Golding and Cox (Phys. Rev. Lett., 96 (2006) 098102) tracked the motion of individual fluorescently labelled mRNA molecules inside live E. coli cells. They found that in the set of 23 trajectories from 3 different experiments, the automatically recognized motion is subdiffusive and published an intriguing microscopy video. Here, we extract the corresponding time series from this video by image segmentation method and present its detailed statistical analysis. We find that this trajectory was not included in the data set already studied and has different statistical properties. It is best fitted by a fractional autoregressive integrated moving average (FARIMA) process with the normal-inverse Gaussian (NIG) noise and the negative memory. In contrast to earlier studies, this shows that the fractional Brownian motion is not the best model for the dynamics documented in this video.

Detection of reflecting surfaces by a statistical model

NASA Astrophysics Data System (ADS)

He, Qiang; Chu, Chee-Hung H.

2009-02-01

Remote sensing is widely used assess the destruction from natural disasters and to plan relief and recovery operations. How to automatically extract useful features and segment interesting objects from digital images, including remote sensing imagery, becomes a critical task for image understanding. Unfortunately, current research on automated feature extraction is ignorant of contextual information. As a result, the fidelity of populating attributes corresponding to interesting features and objects cannot be satisfied. In this paper, we present an exploration on meaningful object extraction integrating reflecting surfaces. Detection of specular reflecting surfaces can be useful in target identification and then can be applied to environmental monitoring, disaster prediction and analysis, military, and counter-terrorism. Our method is based on a statistical model to capture the statistical properties of specular reflecting surfaces. And then the reflecting surfaces are detected through cluster analysis.

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.

Detailed climate-change projections for urban land-use change and green-house gas increases for Belgium with COSMO-CLM coupled to TERRA_URB

NASA Astrophysics Data System (ADS)

Wouters, Hendrik; Vanden Broucke, Sam; van Lipzig, Nicole; Demuzere, Matthias

2016-04-01

Recent research clearly show that climate modelling at high resolution - which resolve the deep convection, the detailed orography and land-use including urbanization - leads to better modelling performance with respect to temperatures, the boundary-layer, clouds and precipitation. The increasing computational power enables the climate research community to address climate-change projections with higher accuracy and much more detail. In the framework of the CORDEX.be project aiming for coherent high-resolution micro-ensemble projections for Belgium employing different GCMs and RCMs, the KU Leuven contributes by means of the downscaling of EC-EARTH global climate model projections (provided by the Royal Meteorological Institute of the Netherlands) to the Belgian domain. The downscaling is obtained with regional climate simulations at 12.5km resolution over Europe (CORDEX-EU domain) and at 2.8km resolution over Belgium (CORDEX.be domain) using COSMO-CLM coupled to urban land-surface parametrization TERRA_URB. This is done for the present-day (1975-2005) and future (2040 → 2070 and 2070 → 2100). In these high-resolution runs, both GHG changes (in accordance to RCP8.5) and urban land-use changes (in accordance to a business-as-usual urban expansion scenario) are taken into account. Based on these simulations, it is shown how climate-change statistics are modified when going from coarse resolution modelling to high-resolution modelling. The climate-change statistics of particular interest are the changes in number of extreme precipitation events and extreme heat waves in cities. Hereby, it is futher investigated for the robustness of the signal change between the course and high-resolution and whether a (statistical) translation is possible. The different simulations also allow to address the relative impact and synergy between the urban expansion and increased GHG on the climate-change statistics. Hereby, it is investigated for which climate-change statistics the urban heat island and urban expansion is relevant, and to what extent the urban expansion can be included in the coarse-to-high resolution translation.

Prognostic factors for risk stratification of adult cancer patients with chemotherapy-induced febrile neutropenia: a systematic review and meta-analysis.

PubMed

Lee, Yee Mei; Lang, Dora; Lockwood, Craig

Increasing numbers of studies identify new prognostic factors for categorising chemotherapy-induced febrile neutropenia adult cancer patients into high- or low-risk groups for adverse outcomes. These groupings are used to tailor therapy according to level of risk. However many emerging factors with prognostic significance remain controversial, being based on single studies only. A systematic review was conducted to determine the strength of association of all identified factors associated with the outcomes of chemotherapy-induced febrile neutropenia patients. The participants included were adults of 15 years old and above, with a cancer diagnosis and who underwent cancer treatment.The review focused on clinical factors and their association with the outcomes of cancer patients with chemotherapy-induced febrile neutropenia at presentation of fever.All quantitative studies published in English which investigated clinical factors for risk stratification of adult cancer patients with chemotherapy-induced febrile neutropenia were considered.The primary outcome of interest was to identify the clinical factors for risk stratification of adult cancer patients with chemotherapy-induced febrile neutropenia. Electronic databases searched from their respective inception date up to December 2011 include MEDLINE, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, Science-Direct, Scopus and Mednar. The quality of the included studies was subjected to assessment by two independent reviewers. The standardised critical appraisal tool from the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI) was used to assess the following criteria: representativeness of study population; clearly defined prognostic factors and outcomes; whether potential confounders were addressed and appropriate statistical analysis was undertaken for the study design. Data extraction was performed using a modified version of the standardised extraction tool from the JBI-MAStARI. Prognostic factors and the accompanying odds ratio reported for the significance of these factors that were identified by multivariate regression, were extracted from each included study. Studies results were pooled in statistical meta-analysis using Review Manager 5.1. Where statistical pooling was not possible, the findings were presented in narrative form. Seven studies (four prospective cohort and three retrospective cohort) investigating 22 factors in total were included. Fixed effects meta-analysis showed: hypotension [OR=1.66, 95%CI, 1.14-2.41, p=0.008] and thrombocytopenia [OR=3.92, 95%CI, 2.19-7.01, p<0.00001)] were associated with high-risk of adverse outcomes for febrile neutropenia. Other factors that were statistically significant from single studies included: age of patients, clinical presentation at fever onset, presence or absence of co-morbidities, infections, duration and severity of neutropenia state. Five prognostic factors failed to demonstrate an association between the variables and the outcomes measured and they include: presence of pneumonia, total febrile days, median days to fever, recovery from neutropenia and presence of moderate clinical symptoms in association with Gram-negative bacteraemia. Despite the overall limitations identified in the included studies, this review has provided a synthesis of the best available evidence for the prognostic factors used in risk stratification of febrile neutropenia patients. However, the dynamic aspects of prognostic model development, validation and utilisation have not been addressed adequately thus far. Given the findings of this review, it is timely to address these issues and improve the utilisation of prognostic models in the management of febrile neutropenia patients. The identified factors are similar to the factors in current prognostic models. However, additional factors that were reported to be statistically significant in this review (thrombocytopenia, presence of central venous catheter, and duration and severity of neutropenia) have not previously been included in prognostic models. This review has found these factors may improve the performance of current models by adding or replacing some of the factors. The role of risk stratification of chemotherapy-induced febrile neutropenia patients continues to evolve as the practice of risk-based therapy has been demonstrated to be beneficial to patients, clinicians and health care organisations. Further research to identify new factors /markers is needed to develop a new model which is reliable and accurate for these patients, regardless of cancer types. A robust and well-validated prognostic model is the key to enhance patient safety in the risk-based management of cancer patients with chemotherapy-induced febrile neutropenia.

Prediction of new onset of end stage renal disease in Chinese patients with type 2 diabetes mellitus - a population-based retrospective cohort study.

PubMed

Wan, Eric Yuk Fai; Fong, Daniel Yee Tak; Fung, Colman Siu Cheung; Yu, Esther Yee Tak; Chin, Weng Yee; Chan, Anca Ka Chun; Lam, Cindy Lo Kuen

2017-08-01

Since diabetes mellitus (DM) is the leading cause of end stage renal disease (ESRD), this study aimed to develop a 5-year ESRD risk prediction model among Chinese patients with Type 2 DM (T2DM) in primary care. A retrospective cohort study was conducted on 149,333 Chinese adult T2DM primary care patients without ESRD in 2010. Using the derivation cohort over a median of 5 years follow-up, the gender-specific models including the interaction effect between predictors and age were derived using Cox regression with a forward stepwise approach. Harrell's C-statistic and calibration plot were applied to the validation cohort to assess discrimination and calibration of the models. Prediction models showed better discrimination with Harrell's C-statistics of 0.866 (males) and 0.862 (females) and calibration power from the plots than other established models. The predictors included age, usages of anti-hypertensive drugs, anti-glucose drugs, and Hemogloblin A1c, blood pressure, urine albumin/creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR). Specific predictors for male were smoking and presence of sight threatening diabetic retinopathy while additional predictors for female included longer duration of diabetes and quadratic effect of body mass index. Interaction factors with age showed a greater weighting of insulin and urine ACR in younger males, and eGFR in younger females. Our newly developed gender-specific models provide a more accurate 5-year ESRD risk predictions for Chinese diabetic primary care patients than other existing models. The models included several modifiable risk factors that clinicians can use to counsel patients, and to target at in the delivery of care to patients.

(Draft) Community air pollution and mortality: Analysis of 1980 data from US metropolitan areas

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

Lipfert, F.W.

1992-11-01

1980 data from up to 149 metropolitan areas were used to define cross-sectional associations between community air pollution and ``excess`` human mortality. The regression model proposed by Ozkaynak and Thurston (1987), which accounted for age, race, education, poverty, and population density, was evaluated and several new models were developed. The new models also accounted for migration, drinking water hardness, and smoking, and included a more detailed description of race. Cause-of-death categories analyzed include all causes, all ``non-external`` causes, major cardiovascular diseases, and chronic obstructive pulmonary diseases (COPD). Both annual mortality rates and their logarithms were analyzed. Air quality data weremore » obtained from the EPA AIRS database (TSP, SO{sub 4}{sup =}, Mn, and ozone) and from the inhalable particulate network (PM{sub 15}, PM{sub 2.5} and SO{sub 4}{sup =}, for 63{sup 4} locations). The data on particulates were averaged across all monitoring stations available for each SMSA and the TSP data were restricted to the year 1980. The associations between mortality and air pollution were found to be dependent on the socioeconomic factors included in the models, the specific locations included in the data set, and the type of statistical model used. Statistically significant associations were found as follows: between TSP and mortality due to non-external causes with log-linear models, but not with a linear model betweenestimated 10-year average (1980--90) ozone levels and 1980 non-external and cardiovascular deaths; and between TSP and COPD mortality for both linear and log-linear models. When the sulfate contribution to TSP was subtracted, the relationship with COPD mortality was strengthened.« less

(Draft) Community air pollution and mortality: Analysis of 1980 data from US metropolitan areas

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

Lipfert, F.W.

1992-11-01

1980 data from up to 149 metropolitan areas were used to define cross-sectional associations between community air pollution and excess'' human mortality. The regression model proposed by Ozkaynak and Thurston (1987), which accounted for age, race, education, poverty, and population density, was evaluated and several new models were developed. The new models also accounted for migration, drinking water hardness, and smoking, and included a more detailed description of race. Cause-of-death categories analyzed include all causes, all non-external'' causes, major cardiovascular diseases, and chronic obstructive pulmonary diseases (COPD). Both annual mortality rates and their logarithms were analyzed. Air quality data weremore » obtained from the EPA AIRS database (TSP, SO[sub 4][sup =], Mn, and ozone) and from the inhalable particulate network (PM[sub 15], PM[sub 2.5] and SO[sub 4][sup =], for 63[sup 4] locations). The data on particulates were averaged across all monitoring stations available for each SMSA and the TSP data were restricted to the year 1980. The associations between mortality and air pollution were found to be dependent on the socioeconomic factors included in the models, the specific locations included in the data set, and the type of statistical model used. Statistically significant associations were found as follows: between TSP and mortality due to non-external causes with log-linear models, but not with a linear model betweenestimated 10-year average (1980--90) ozone levels and 1980 non-external and cardiovascular deaths; and between TSP and COPD mortality for both linear and log-linear models. When the sulfate contribution to TSP was subtracted, the relationship with COPD mortality was strengthened.« less

School Interventions To Prevent Youth Suicide. Technical Assistance Sampler.

ERIC Educational Resources Information Center

California Univ., Los Angeles. Center for Mental Health in Schools.

This guide lists information and resources concerning school interventions to prevent youth suicide. Part 1 includes articles related to youth suicide, depression, and violence, along with a listing of basic facts, statistics, and myths about suicide. A general model of youth suicide is included followed by information on prevention. Part 2…

Additive hazards regression and partial likelihood estimation for ecological monitoring data across space.

PubMed

Lin, Feng-Chang; Zhu, Jun

2012-01-01

We develop continuous-time models for the analysis of environmental or ecological monitoring data such that subjects are observed at multiple monitoring time points across space. Of particular interest are additive hazards regression models where the baseline hazard function can take on flexible forms. We consider time-varying covariates and take into account spatial dependence via autoregression in space and time. We develop statistical inference for the regression coefficients via partial likelihood. Asymptotic properties, including consistency and asymptotic normality, are established for parameter estimates under suitable regularity conditions. Feasible algorithms utilizing existing statistical software packages are developed for computation. We also consider a simpler additive hazards model with homogeneous baseline hazard and develop hypothesis testing for homogeneity. A simulation study demonstrates that the statistical inference using partial likelihood has sound finite-sample properties and offers a viable alternative to maximum likelihood estimation. For illustration, we analyze data from an ecological study that monitors bark beetle colonization of red pines in a plantation of Wisconsin.

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

The effect of QBO on foE

NASA Astrophysics Data System (ADS)

Atıcı, Ramazan; Sağır, Selçuk

2017-07-01

In the present work, the relationship with QBO of difference (ΔfoE = foEmea - foEIRI) between critical frequency (foE) values of ionospheric E-region, measured at Darwin and Casos Island stations and calculated by IRI-2012 ionospheric model, is statistically investigated. A multiple regression model is used as statistical tool. The ;Dummy; variables (;DummyWest; and ;DummyEast; represent westerly QBO values and easterly QBO values, respectively) are added to model in order to see the effect of westerly and easterly QBO. In the result of calculations, it is observed that the changes in ΔfoE about 50-52% can be explained by QBO at both stations. The relationship between QBO and ΔfoE is negative at both stations. The change of 1 ms-1 in whole set of QBO leads to a decrease of 0.008 MHz at Casos Island station and 0.017 MHz at Darwin station in ΔfoE. Directions of QBO have an effect on ΔfoE at the Darwin station, but they've not any effect on ΔfoE at Casos Island station. It is thought that the difference values in the foE are due to not to be included in the IRI-model of all parameters affecting the critical frequency value. Thus, QBO which is not included to IRI-model can have an effect on foE and more accurate results can be obtained by IRI model if the QBO is included in this model calculations.

Computational Analysis for Rocket-Based Combined-Cycle Systems During Rocket-Only Operation

NASA Technical Reports Server (NTRS)

Steffen, C. J., Jr.; Smith, T. D.; Yungster, S.; Keller, D. J.

2000-01-01

A series of Reynolds-averaged Navier-Stokes calculations were employed to study the performance of rocket-based combined-cycle systems operating in an all-rocket mode. This parametric series of calculations were executed within a statistical framework, commonly known as design of experiments. The parametric design space included four geometric and two flowfield variables set at three levels each, for a total of 729 possible combinations. A D-optimal design strategy was selected. It required that only 36 separate computational fluid dynamics (CFD) solutions be performed to develop a full response surface model, which quantified the linear, bilinear, and curvilinear effects of the six experimental variables. The axisymmetric, Reynolds-averaged Navier-Stokes simulations were executed with the NPARC v3.0 code. The response used in the statistical analysis was created from Isp efficiency data integrated from the 36 CFD simulations. The influence of turbulence modeling was analyzed by using both one- and two-equation models. Careful attention was also given to quantify the influence of mesh dependence, iterative convergence, and artificial viscosity upon the resulting statistical model. Thirteen statistically significant effects were observed to have an influence on rocket-based combined-cycle nozzle performance. It was apparent that the free-expansion process, directly downstream of the rocket nozzle, can influence the Isp efficiency. Numerical schlieren images and particle traces have been used to further understand the physical phenomena behind several of the statistically significant results.

Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models.

PubMed

Fan, Ruzong; Wang, Yifan; Boehnke, Michael; Chen, Wei; Li, Yun; Ren, Haobo; Lobach, Iryna; Xiong, Momiao

2015-08-01

Meta-analysis of genetic data must account for differences among studies including study designs, markers genotyped, and covariates. The effects of genetic variants may differ from population to population, i.e., heterogeneity. Thus, meta-analysis of combining data of multiple studies is difficult. Novel statistical methods for meta-analysis are needed. In this article, functional linear models are developed for meta-analyses that connect genetic data to quantitative traits, adjusting for covariates. The models can be used to analyze rare variants, common variants, or a combination of the two. Both likelihood-ratio test (LRT) and F-distributed statistics are introduced to test association between quantitative traits and multiple variants in one genetic region. Extensive simulations are performed to evaluate empirical type I error rates and power performance of the proposed tests. The proposed LRT and F-distributed statistics control the type I error very well and have higher power than the existing methods of the meta-analysis sequence kernel association test (MetaSKAT). We analyze four blood lipid levels in data from a meta-analysis of eight European studies. The proposed methods detect more significant associations than MetaSKAT and the P-values of the proposed LRT and F-distributed statistics are usually much smaller than those of MetaSKAT. The functional linear models and related test statistics can be useful in whole-genome and whole-exome association studies. Copyright © 2015 by the Genetics Society of America.

A Climate Statistics Tool and Data Repository

NASA Astrophysics Data System (ADS)

Wang, J.; Kotamarthi, V. R.; Kuiper, J. A.; Orr, A.

2017-12-01

Researchers at Argonne National Laboratory and collaborating organizations have generated regional scale, dynamically downscaled climate model output using Weather Research and Forecasting (WRF) version 3.3.1 at a 12km horizontal spatial resolution over much of North America. The WRF model is driven by boundary conditions obtained from three independent global scale climate models and two different future greenhouse gas emission scenarios, named representative concentration pathways (RCPs). The repository of results has a temporal resolution of three hours for all the simulations, includes more than 50 variables, is stored in Network Common Data Form (NetCDF) files, and the data volume is nearly 600Tb. A condensed 800Gb set of NetCDF files were made for selected variables most useful for climate-related planning, including daily precipitation, relative humidity, solar radiation, maximum temperature, minimum temperature, and wind. The WRF model simulations are conducted for three 10-year time periods (1995-2004, 2045-2054, and 2085-2094), and two future scenarios RCP4.5 and RCP8.5). An open-source tool was coded using Python 2.7.8 and ESRI ArcGIS 10.3.1 programming libraries to parse the NetCDF files, compute summary statistics, and output results as GIS layers. Eight sets of summary statistics were generated as examples for the contiguous U.S. states and much of Alaska, including number of days over 90°F, number of days with a heat index over 90°F, heat waves, monthly and annual precipitation, drought, extreme precipitation, multi-model averages, and model bias. This paper will provide an overview of the project to generate the main and condensed data repositories, describe the Python tool and how to use it, present the GIS results of the computed examples, and discuss some of the ways they can be used for planning. The condensed climate data, Python tool, computed GIS results, and documentation of the work are shared on the Internet.

Performance of the Generalized S-X[Superscript 2] Item Fit Index for Polytomous IRT Models

ERIC Educational Resources Information Center

Kang, Taehoon; Chen, Troy T.

2008-01-01

Orlando and Thissen's S-X[superscript 2] item fit index has performed better than traditional item fit statistics such as Yen' s Q[subscript 1] and McKinley and Mill' s G[superscript 2] for dichotomous item response theory (IRT) models. This study extends the utility of S-X[superscript 2] to polytomous IRT models, including the generalized partial…

Goddard trajectory determination subsystem: Mathematical specifications

NASA Technical Reports Server (NTRS)

Wagner, W. E. (Editor); Velez, C. E. (Editor)

1972-01-01

The mathematical specifications of the Goddard trajectory determination subsystem of the flight dynamics system are presented. These specifications include the mathematical description of the coordinate systems, dynamic and measurement model, numerical integration techniques, and statistical estimation concepts.

Drivers willingness to pay progressive rate for street parking.

DOT National Transportation Integrated Search

2015-01-01

This study finds willingness to pay and price elasticity for on-street parking demand using stated : preference data obtained from 238 respondents. Descriptive, statistical and economic analyses including : regression, generalized linear model, and f...

An improved approach for flight readiness certification: Probabilistic models for flaw propagation and turbine blade failure. Volume 1: Methodology and applications

NASA Technical Reports Server (NTRS)

Moore, N. R.; Ebbeler, D. H.; Newlin, L. E.; Sutharshana, S.; Creager, M.

1992-01-01

An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with analytical modeling of failure phenomena to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in analytical modeling, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which analytical models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. State-of-the-art analytical models currently employed for designs failure prediction, or performance analysis are used in this methodology. The rationale for the statistical approach taken in the PFA methodology is discussed, the PFA methodology is described, and examples of its application to structural failure modes are presented. The engineering models and computer software used in fatigue crack growth and fatigue crack initiation applications are thoroughly documented.

An improved approach for flight readiness certification: Probabilistic models for flaw propagation and turbine blade failure. Volume 2: Software documentation

NASA Technical Reports Server (NTRS)

Moore, N. R.; Ebbeler, D. H.; Newlin, L. E.; Sutharshana, S.; Creager, M.

1992-01-01

An improved methodology for quantitatively evaluating failure risk of spaceflights systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with analytical modeling of failure phenomena to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in analytical modeling, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which analytical models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. State-of-the-art analytical models currently employed for design, failure prediction, or performance analysis are used in this methodology. The rationale for the statistical approach taken in the PFA methodology is discussed, the PFA methodology is described, and examples of its application to structural failure modes are presented. The engineering models and computer software used in fatigue crack growth and fatigue crack initiation applications are thoroughly documented.

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

The power prior: theory and applications.

PubMed

Ibrahim, Joseph G; Chen, Ming-Hui; Gwon, Yeongjin; Chen, Fang

2015-12-10

The power prior has been widely used in many applications covering a large number of disciplines. The power prior is intended to be an informative prior constructed from historical data. It has been used in clinical trials, genetics, health care, psychology, environmental health, engineering, economics, and business. It has also been applied for a wide variety of models and settings, both in the experimental design and analysis contexts. In this review article, we give an A-to-Z exposition of the power prior and its applications to date. We review its theoretical properties, variations in its formulation, statistical contexts for which it has been used, applications, and its advantages over other informative priors. We review models for which it has been used, including generalized linear models, survival models, and random effects models. Statistical areas where the power prior has been used include model selection, experimental design, hierarchical modeling, and conjugate priors. Frequentist properties of power priors in posterior inference are established, and a simulation study is conducted to further examine the empirical performance of the posterior estimates with power priors. Real data analyses are given illustrating the power prior as well as the use of the power prior in the Bayesian design of clinical trials. Copyright © 2015 John Wiley & Sons, Ltd.

An R2 statistic for fixed effects in the linear mixed model.

PubMed

Edwards, Lloyd J; Muller, Keith E; Wolfinger, Russell D; Qaqish, Bahjat F; Schabenberger, Oliver

2008-12-20

Statisticians most often use the linear mixed model to analyze Gaussian longitudinal data. The value and familiarity of the R(2) statistic in the linear univariate model naturally creates great interest in extending it to the linear mixed model. We define and describe how to compute a model R(2) statistic for the linear mixed model by using only a single model. The proposed R(2) statistic measures multivariate association between the repeated outcomes and the fixed effects in the linear mixed model. The R(2) statistic arises as a 1-1 function of an appropriate F statistic for testing all fixed effects (except typically the intercept) in a full model. The statistic compares the full model with a null model with all fixed effects deleted (except typically the intercept) while retaining exactly the same covariance structure. Furthermore, the R(2) statistic leads immediately to a natural definition of a partial R(2) statistic. A mixed model in which ethnicity gives a very small p-value as a longitudinal predictor of blood pressure (BP) compellingly illustrates the value of the statistic. In sharp contrast to the extreme p-value, a very small R(2) , a measure of statistical and scientific importance, indicates that ethnicity has an almost negligible association with the repeated BP outcomes for the study.

Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

PubMed

Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann

2003-01-01

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

MWASTools: an R/bioconductor package for metabolome-wide association studies.

PubMed

Rodriguez-Martinez, Andrea; Posma, Joram M; Ayala, Rafael; Neves, Ana L; Anwar, Maryam; Petretto, Enrico; Emanueli, Costanza; Gauguier, Dominique; Nicholson, Jeremy K; Dumas, Marc-Emmanuel

2018-03-01

MWASTools is an R package designed to provide an integrated pipeline to analyse metabonomic data in large-scale epidemiological studies. Key functionalities of our package include: quality control analysis; metabolome-wide association analysis using various models (partial correlations, generalized linear models); visualization of statistical outcomes; metabolite assignment using statistical total correlation spectroscopy (STOCSY); and biological interpretation of metabolome-wide association studies results. The MWASTools R package is implemented in R (version  > =3.4) and is available from Bioconductor: https://bioconductor.org/packages/MWASTools/. m.dumas@imperial.ac.uk. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Statistical hadronization with exclusive channels in e +e - annihilation

DOE PAGES

Ferroni, L.; Becattini, F.

2012-01-01

We present a systematic analysis of exclusive hadronic channels in e +e - collisions at centre-of-mass energies between 2.1 and 2.6 GeV within the statistical hadronization model. Because of the low multiplicities involved, calculations have been carried out in the full microcanonical ensemble, including conservation of energy-momentum, angular momentum, parity, isospin, and all relevant charges. We show that the data is in an overall good agreement with the model for an energy density of about 0.5 GeV/fm 3 and an extra strangeness suppression parameter γ S 0:7, essentially the same values found with fits to inclusive multiplicities at higher energy.

Uncertainty in Model Predictions of Vibrio Vulnificus Response to Climate Variability and Change: A Chesapeake Bay Case Study

NASA Technical Reports Server (NTRS)

Urquhart, Erin A.; Zaitchik, Benjamin F.; Waugh, Darryn W.; Guikema, Seth D.; Del Castillo, Carlos E.

2014-01-01

The effect that climate change and variability will have on waterborne bacteria is a topic of increasing concern for coastal ecosystems, including the Chesapeake Bay. Surface water temperature trends in the Bay indicate a warming pattern of roughly 0.3-0.4 C per decade over the past 30 years. It is unclear what impact future warming will have on pathogens currently found in the Bay, including Vibrio spp. Using historical environmental data, combined with three different statistical models of Vibrio vulnificus probability, we explore the relationship between environmental change and predicted Vibrio vulnificus presence in the upper Chesapeake Bay. We find that the predicted response of V. vulnificus probability to high temperatures in the Bay differs systematically between models of differing structure. As existing publicly available datasets are inadequate to determine which model structure is most appropriate, the impact of climatic change on the probability of V. vulnificus presence in the Chesapeake Bay remains uncertain. This result points to the challenge of characterizing climate sensitivity of ecological systems in which data are sparse and only statistical models of ecological sensitivity exist.

Model-Based Linkage Analysis of a Quantitative Trait.

PubMed

Song, Yeunjoo E; Song, Sunah; Schnell, Audrey H

2017-01-01

Linkage Analysis is a family-based method of analysis to examine whether any typed genetic markers cosegregate with a given trait, in this case a quantitative trait. If linkage exists, this is taken as evidence in support of a genetic basis for the trait. Historically, linkage analysis was performed using a binary disease trait, but has been extended to include quantitative disease measures. Quantitative traits are desirable as they provide more information than binary traits. Linkage analysis can be performed using single-marker methods (one marker at a time) or multipoint (using multiple markers simultaneously). In model-based linkage analysis the genetic model for the trait of interest is specified. There are many software options for performing linkage analysis. Here, we use the program package Statistical Analysis for Genetic Epidemiology (S.A.G.E.). S.A.G.E. was chosen because it also includes programs to perform data cleaning procedures and to generate and test genetic models for a quantitative trait, in addition to performing linkage analysis. We demonstrate in detail the process of running the program LODLINK to perform single-marker analysis, and MLOD to perform multipoint analysis using output from SEGREG, where SEGREG was used to determine the best fitting statistical model for the trait.

Diagnosis checking of statistical analysis in RCTs indexed in PubMed.

PubMed

Lee, Paul H; Tse, Andy C Y

2017-11-01

Statistical analysis is essential for reporting of the results of randomized controlled trials (RCTs), as well as evaluating their effectiveness. However, the validity of a statistical analysis also depends on whether the assumptions of that analysis are valid. To review all RCTs published in journals indexed in PubMed during December 2014 to provide a complete picture of how RCTs handle assumptions of statistical analysis. We reviewed all RCTs published in December 2014 that appeared in journals indexed in PubMed using the Cochrane highly sensitive search strategy. The 2014 impact factors of the journals were used as proxies for their quality. The type of statistical analysis used and whether the assumptions of the analysis were tested were reviewed. In total, 451 papers were included. Of the 278 papers that reported a crude analysis for the primary outcomes, 31 (27·2%) reported whether the outcome was normally distributed. Of the 172 papers that reported an adjusted analysis for the primary outcomes, diagnosis checking was rarely conducted, with only 20%, 8·6% and 7% checked for generalized linear model, Cox proportional hazard model and multilevel model, respectively. Study characteristics (study type, drug trial, funding sources, journal type and endorsement of CONSORT guidelines) were not associated with the reporting of diagnosis checking. The diagnosis of statistical analyses in RCTs published in PubMed-indexed journals was usually absent. Journals should provide guidelines about the reporting of a diagnosis of assumptions. © 2017 Stichting European Society for Clinical Investigation Journal Foundation.

A Hybrid Multi-Scale Model of Crystal Plasticity for Handling Stress Concentrations

DOE PAGES

Sun, Shang; Ramazani, Ali; Sundararaghavan, Veera

2017-09-04

Microstructural effects become important at regions of stress concentrators such as notches, cracks and contact surfaces. A multiscale model is presented that efficiently captures microstructural details at such critical regions. The approach is based on a multiresolution mesh that includes an explicit microstructure representation at critical regions where stresses are localized. At regions farther away from the stress concentration, a reduced order model that statistically captures the effect of the microstructure is employed. The statistical model is based on a finite element representation of the orientation distribution function (ODF). As an illustrative example, we have applied the multiscaling method tomore » compute the stress intensity factor K I around the crack tip in a wedge-opening load specimen. The approach is verified with an analytical solution within linear elasticity approximation and is then extended to allow modeling of microstructural effects on crack tip plasticity.« less

A Hybrid Multi-Scale Model of Crystal Plasticity for Handling Stress Concentrations

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

Sun, Shang; Ramazani, Ali; Sundararaghavan, Veera

Microstructural effects become important at regions of stress concentrators such as notches, cracks and contact surfaces. A multiscale model is presented that efficiently captures microstructural details at such critical regions. The approach is based on a multiresolution mesh that includes an explicit microstructure representation at critical regions where stresses are localized. At regions farther away from the stress concentration, a reduced order model that statistically captures the effect of the microstructure is employed. The statistical model is based on a finite element representation of the orientation distribution function (ODF). As an illustrative example, we have applied the multiscaling method tomore » compute the stress intensity factor K I around the crack tip in a wedge-opening load specimen. The approach is verified with an analytical solution within linear elasticity approximation and is then extended to allow modeling of microstructural effects on crack tip plasticity.« less

The FORE-SCE model: a practical approach for projecting land cover change using scenario-based modeling

USGS Publications Warehouse

Sohl, Terry L.; Sayler, Kristi L.; Drummond, Mark A.; Loveland, Thomas R.

2007-01-01

A wide variety of ecological applications require spatially explicit, historic, current, and projected land use and land cover data. The U.S. Land Cover Trends project is analyzing contemporary (1973–2000) land-cover change in the conterminous United States. The newly developed FORE-SCE model used Land Cover Trends data and theoretical, statistical, and deterministic modeling techniques to project future land cover change through 2020 for multiple plausible scenarios. Projected proportions of future land use were initially developed, and then sited on the lands with the highest potential for supporting that land use and land cover using a statistically based stochastic allocation procedure. Three scenarios of 2020 land cover were mapped for the western Great Plains in the US. The model provided realistic, high-resolution, scenario-based land-cover products suitable for multiple applications, including studies of climate and weather variability, carbon dynamics, and regional hydrology.

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

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.

Identification of statistically independent climatic pattern in GRACE and hydrological model data over West-Africa

NASA Astrophysics Data System (ADS)

Kusche, J.; Forootan, E.; Eicker, A.; Hoffmann-Dobrev, H.

2012-04-01

West-African countries have been exposed to changes in rainfall patterns over the last decades, including a significant negative trend. This causes adverse effects on water resources, for instance reduced freshwater availability, and changes in the frequency, duration and magnitude of droughts and floods. Extracting the main patterns of water storage change in West Africa from remote sensing and linking them to climate variability, is therefore an essential step to understand the hydrological aspects of the region. In this study, the higher order statistical method of Independent Component Analysis (ICA) is employed to extract statistically independent water storage patterns from monthly Gravity Recovery And Climate Experiment (GRACE), from the WaterGAP Global Hydrology Model (WGHM) and from Tropical Rainfall Measuring Mission (TRMM) products over West Africa, for the period 2002-2012. Then, to reveal the influences of climatic teleconnections on the individual patterns, these results were correlated to the El Nino-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) indices. To study the predictability of water storage changes, advanced statistical methods were applied on the main independent Sea Surface Temperature (SST) patterns over the Atlantic and Indian Oceans for the period 2002-2012 and the ICA results. Our results show a water storage decrease over the coastal regions of West Africa (including Sierra Leone, Liberia, Togo and Nigeria), associated with rainfall decrease. The comparison between GRACE estimations and WGHM results indicates some inconsistencies that underline the importance of forcing data for hydrological modeling of West Africa. Keywords: West Africa; GRACE-derived water storage; ICA; ENSO; IOD

Football fever: goal distributions and non-Gaussian statistics

NASA Astrophysics Data System (ADS)

Bittner, E.; Nußbaumer, A.; Janke, W.; Weigel, M.

2009-02-01

Analyzing football score data with statistical techniques, we investigate how the not purely random, but highly co-operative nature of the game is reflected in averaged properties such as the probability distributions of scored goals for the home and away teams. As it turns out, especially the tails of the distributions are not well described by the Poissonian or binomial model resulting from the assumption of uncorrelated random events. Instead, a good effective description of the data is provided by less basic distributions such as the negative binomial one or the probability densities of extreme value statistics. To understand this behavior from a microscopical point of view, however, no waiting time problem or extremal process need be invoked. Instead, modifying the Bernoulli random process underlying the Poissonian model to include a simple component of self-affirmation seems to describe the data surprisingly well and allows to understand the observed deviation from Gaussian statistics. The phenomenological distributions used before can be understood as special cases within this framework. We analyzed historical football score data from many leagues in Europe as well as from international tournaments, including data from all past tournaments of the “FIFA World Cup” series, and found the proposed models to be applicable rather universally. In particular, here we analyze the results of the German women’s premier football league and consider the two separate German men’s premier leagues in the East and West during the cold war times as well as the unified league after 1990 to see how scoring in football and the component of self-affirmation depend on cultural and political circumstances.

Can upstaging of ductal carcinoma in situ be predicted at biopsy by histologic and mammographic features?

NASA Astrophysics Data System (ADS)

Shi, Bibo; Grimm, Lars J.; Mazurowski, Maciej A.; Marks, Jeffrey R.; King, Lorraine M.; Maley, Carlo C.; Hwang, E. Shelley; Lo, Joseph Y.

2017-03-01

Reducing the overdiagnosis and overtreatment associated with ductal carcinoma in situ (DCIS) requires accurate prediction of the invasive potential at cancer screening. In this work, we investigated the utility of pre-operative histologic and mammographic features to predict upstaging of DCIS. The goal was to provide intentionally conservative baseline performance using readily available data from radiologists and pathologists and only linear models. We conducted a retrospective analysis on 99 patients with DCIS. Of those 25 were upstaged to invasive cancer at the time of definitive surgery. Pre-operative factors including both the histologic features extracted from stereotactic core needle biopsy (SCNB) reports and the mammographic features annotated by an expert breast radiologist were investigated with statistical analysis. Furthermore, we built classification models based on those features in an attempt to predict the presence of an occult invasive component in DCIS, with generalization performance assessed by receiver operating characteristic (ROC) curve analysis. Histologic features including nuclear grade and DCIS subtype did not show statistically significant differences between cases with pure DCIS and with DCIS plus invasive disease. However, three mammographic features, i.e., the major axis length of DCIS lesion, the BI-RADS level of suspicion, and radiologist's assessment did achieve the statistical significance. Using those three statistically significant features as input, a linear discriminant model was able to distinguish patients with DCIS plus invasive disease from those with pure DCIS, with AUC-ROC equal to 0.62. Overall, mammograms used for breast screening contain useful information that can be perceived by radiologists and help predict occult invasive components in DCIS.

Truth, models, model sets, AIC, and multimodel inference: a Bayesian perspective

USGS Publications Warehouse

Barker, Richard J.; Link, William A.

2015-01-01

Statistical inference begins with viewing data as realizations of stochastic processes. Mathematical models provide partial descriptions of these processes; inference is the process of using the data to obtain a more complete description of the stochastic processes. Wildlife and ecological scientists have become increasingly concerned with the conditional nature of model-based inference: what if the model is wrong? Over the last 2 decades, Akaike's Information Criterion (AIC) has been widely and increasingly used in wildlife statistics for 2 related purposes, first for model choice and second to quantify model uncertainty. We argue that for the second of these purposes, the Bayesian paradigm provides the natural framework for describing uncertainty associated with model choice and provides the most easily communicated basis for model weighting. Moreover, Bayesian arguments provide the sole justification for interpreting model weights (including AIC weights) as coherent (mathematically self consistent) model probabilities. This interpretation requires treating the model as an exact description of the data-generating mechanism. We discuss the implications of this assumption, and conclude that more emphasis is needed on model checking to provide confidence in the quality of inference.

Analysis and meta-analysis of single-case designs with a standardized mean difference statistic: a primer and applications.

PubMed

Shadish, William R; Hedges, Larry V; Pustejovsky, James E

2014-04-01

This article presents a d-statistic for single-case designs that is in the same metric as the d-statistic used in between-subjects designs such as randomized experiments and offers some reasons why such a statistic would be useful in SCD research. The d has a formal statistical development, is accompanied by appropriate power analyses, and can be estimated using user-friendly SPSS macros. We discuss both advantages and disadvantages of d compared to other approaches such as previous d-statistics, overlap statistics, and multilevel modeling. It requires at least three cases for computation and assumes normally distributed outcomes and stationarity, assumptions that are discussed in some detail. We also show how to test these assumptions. The core of the article then demonstrates in depth how to compute d for one study, including estimation of the autocorrelation and the ratio of between case variance to total variance (between case plus within case variance), how to compute power using a macro, and how to use the d to conduct a meta-analysis of studies using single-case designs in the free program R, including syntax in an appendix. This syntax includes how to read data, compute fixed and random effect average effect sizes, prepare a forest plot and a cumulative meta-analysis, estimate various influence statistics to identify studies contributing to heterogeneity and effect size, and do various kinds of publication bias analyses. This d may prove useful for both the analysis and meta-analysis of data from SCDs. Copyright © 2013 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Predictors of workplace violence among female sex workers in Tijuana, Mexico.

PubMed

Katsulis, Yasmina; Durfee, Alesha; Lopez, Vera; Robillard, Alyssa

2015-05-01

For sex workers, differences in rates of exposure to workplace violence are likely influenced by a variety of risk factors, including where one works and under what circumstances. Economic stressors, such as housing insecurity, may also increase the likelihood of exposure. Bivariate analyses demonstrate statistically significant associations between workplace violence and selected predictor variables, including age, drug use, exchanging sex for goods, soliciting clients outdoors, and experiencing housing insecurity. Multivariate regression analysis shows that after controlling for each of these variables in one model, only soliciting clients outdoors and housing insecurity emerge as statistically significant predictors for workplace violence. © The Author(s) 2014.

Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory

PubMed Central

Gopnik, Alison; Wellman, Henry M.

2012-01-01

We propose a new version of the “theory theory” grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and non-technical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists. PMID:22582739

The Thomas–Fermi quark model: Non-relativistic aspects

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

Liu, Quan, E-mail: quan_liu@baylor.edu; Wilcox, Walter, E-mail: walter_wilcox@baylor.edu

The first numerical investigation of non-relativistic aspects of the Thomas–Fermi (TF) statistical multi-quark model is given. We begin with a review of the traditional TF model without an explicit spin interaction and find that the spin splittings are too small in this approach. An explicit spin interaction is then introduced which entails the definition of a generalized spin “flavor”. We investigate baryonic states in this approach which can be described with two inequivalent wave functions; such states can however apply to multiple degenerate flavors. We find that the model requires a spatial separation of quark flavors, even if completely degenerate.more » Although the TF model is designed to investigate the possibility of many-quark states, we find surprisingly that it may be used to fit the low energy spectrum of almost all ground state octet and decuplet baryons. The charge radii of such states are determined and compared with lattice calculations and other models. The low energy fit obtained allows us to extrapolate to the six-quark doubly strange H-dibaryon state, flavor symmetric strange states of higher quark content and possible six quark nucleon–nucleon resonances. The emphasis here is on the systematics revealed in this approach. We view our model as a versatile and convenient tool for quickly assessing the characteristics of new, possibly bound, particle states of higher quark number content. -- Highlights: • First application of the statistical Thomas–Fermi quark model to baryonic systems. • Novel aspects: spin as generalized flavor; spatial separation of quark flavor phases. • The model is statistical, but the low energy baryonic spectrum is successfully fit. • Numerical applications include the H-dibaryon, strange states and nucleon resonances. • The statistical point of view does not encourage the idea of bound many-quark baryons.« less

P values are only an index to evidence: 20th- vs. 21st-century statistical science.

PubMed

Burnham, K P; Anderson, D R

2014-03-01

Early statistical methods focused on pre-data probability statements (i.e., data as random variables) such as P values; these are not really inferences nor are P values evidential. Statistical science clung to these principles throughout much of the 20th century as a wide variety of methods were developed for special cases. Looking back, it is clear that the underlying paradigm (i.e., testing and P values) was weak. As Kuhn (1970) suggests, new paradigms have taken the place of earlier ones: this is a goal of good science. New methods have been developed and older methods extended and these allow proper measures of strength of evidence and multimodel inference. It is time to move forward with sound theory and practice for the difficult practical problems that lie ahead. Given data the useful foundation shifts to post-data probability statements such as model probabilities (Akaike weights) or related quantities such as odds ratios and likelihood intervals. These new methods allow formal inference from multiple models in the a prior set. These quantities are properly evidential. The past century was aimed at finding the "best" model and making inferences from it. The goal in the 21st century is to base inference on all the models weighted by their model probabilities (model averaging). Estimates of precision can include model selection uncertainty leading to variances conditional on the model set. The 21st century will be about the quantification of information, proper measures of evidence, and multi-model inference. Nelder (1999:261) concludes, "The most important task before us in developing statistical science is to demolish the P-value culture, which has taken root to a frightening extent in many areas of both pure and applied science and technology".

Does Infection Site Matter? A Systematic Review of Infection Site Mortality in Sepsis.

PubMed

Motzkus, Christine A; Luckmann, Roger

2017-09-01

Sepsis treatment protocols emphasize source control with empiric antibiotics and fluid resuscitation. Previous reviews have examined the impact of infection site and specific pathogens on mortality from sepsis; however, no recent review has addressed the infection site. This review focuses on the impact of infection site on hospital mortality among patients with sepsis. The PubMed database was searched for articles from 2001 to 2014. Studies were eligible if they included (1) one or more statistical models with hospital mortality as the outcome and considered infection site for inclusion in the model and (2) adult patients with sepsis, severe sepsis, or septic shock. Data abstracted included stage of sepsis, infection site, and raw and adjusted effect estimates. Nineteen studies were included. Infection sites most studied included respiratory (n = 19), abdominal (n = 19), genitourinary (n = 18), and skin and soft tissue infections (n = 11). Several studies found a statistically significant lower mortality risk for genitourinary infections on hospital mortality when compared to respiratory infections. Based on studies included in this review, the impact of infection site in patients with sepsis on hospital mortality could not be reliably estimated. Misclassification among infections and disease states remains a serious possibility in studies on this topic.

Applying quantitative adiposity feature analysis models to predict benefit of bevacizumab-based chemotherapy in ovarian cancer patients

NASA Astrophysics Data System (ADS)

Wang, Yunzhi; Qiu, Yuchen; Thai, Theresa; More, Kathleen; Ding, Kai; Liu, Hong; Zheng, Bin

2016-03-01

How to rationally identify epithelial ovarian cancer (EOC) patients who will benefit from bevacizumab or other antiangiogenic therapies is a critical issue in EOC treatments. The motivation of this study is to quantitatively measure adiposity features from CT images and investigate the feasibility of predicting potential benefit of EOC patients with or without receiving bevacizumab-based chemotherapy treatment using multivariate statistical models built based on quantitative adiposity image features. A dataset involving CT images from 59 advanced EOC patients were included. Among them, 32 patients received maintenance bevacizumab after primary chemotherapy and the remaining 27 patients did not. We developed a computer-aided detection (CAD) scheme to automatically segment subcutaneous fat areas (VFA) and visceral fat areas (SFA) and then extracted 7 adiposity-related quantitative features. Three multivariate data analysis models (linear regression, logistic regression and Cox proportional hazards regression) were performed respectively to investigate the potential association between the model-generated prediction results and the patients' progression-free survival (PFS) and overall survival (OS). The results show that using all 3 statistical models, a statistically significant association was detected between the model-generated results and both of the two clinical outcomes in the group of patients receiving maintenance bevacizumab (p<0.01), while there were no significant association for both PFS and OS in the group of patients without receiving maintenance bevacizumab. Therefore, this study demonstrated the feasibility of using quantitative adiposity-related CT image features based statistical prediction models to generate a new clinical marker and predict the clinical outcome of EOC patients receiving maintenance bevacizumab-based chemotherapy.

Comparing and combining process-based crop models and statistical models with some implications for climate change

NASA Astrophysics Data System (ADS)

Roberts, Michael J.; Braun, Noah O.; Sinclair, Thomas R.; Lobell, David B.; Schlenker, Wolfram

2017-09-01

We compare predictions of a simple process-based crop model (Soltani and Sinclair 2012), a simple statistical model (Schlenker and Roberts 2009), and a combination of both models to actual maize yields on a large, representative sample of farmer-managed fields in the Corn Belt region of the United States. After statistical post-model calibration, the process model (Simple Simulation Model, or SSM) predicts actual outcomes slightly better than the statistical model, but the combined model performs significantly better than either model. The SSM, statistical model and combined model all show similar relationships with precipitation, while the SSM better accounts for temporal patterns of precipitation, vapor pressure deficit and solar radiation. The statistical and combined models show a more negative impact associated with extreme heat for which the process model does not account. Due to the extreme heat effect, predicted impacts under uniform climate change scenarios are considerably more severe for the statistical and combined models than for the process-based model.

LIDT-DD: A New Self-Consistent Debris Disc Model Including Radiation Pressure and Coupling Dynamical and Collisional Evolution

NASA Astrophysics Data System (ADS)

Kral, Q.; Thebault, P.; Charnoz, S.

2014-01-01

The first attempt at developing a fully self-consistent code coupling dynamics and collisions to study debris discs (Kral et al. 2013) is presented. So far, these two crucial mechanisms were studied separately, with N-body and statistical collisional codes respectively, because of stringent computational constraints. We present a new model named LIDT-DD which is able to follow over long timescales the coupled evolution of dynamics (including radiation forces) and collisions in a self-consistent way.

Oscillating in synchrony with a metronome: serial dependence, limit cycle dynamics, and modeling.

PubMed

Torre, Kjerstin; Balasubramaniam, Ramesh; Delignières, Didier

2010-07-01

We analyzed serial dependencies in periods and asynchronies collected during oscillations performed in synchrony with a metronome. Results showed that asynchronies contain 1/f fluctuations, and the series of periods contain antipersistent dependence. The analysis of the phase portrait revealed a specific asymmetry induced by synchronization. We propose a hybrid limit cycle model including a cycle-dependent stiffness parameter provided with fractal properties, and a parametric driving function based on velocity. This model accounts for most experimentally evidenced statistical features, including serial dependence and limit cycle dynamics. We discuss the results and modeling choices within the framework of event-based and emergent timing.

A new computer code for discrete fracture network modelling

NASA Astrophysics Data System (ADS)

Xu, Chaoshui; Dowd, Peter

2010-03-01

The authors describe a comprehensive software package for two- and three-dimensional stochastic rock fracture simulation using marked point processes. Fracture locations can be modelled by a Poisson, a non-homogeneous, a cluster or a Cox point process; fracture geometries and properties are modelled by their respective probability distributions. Virtual sampling tools such as plane, window and scanline sampling are included in the software together with a comprehensive set of statistical tools including histogram analysis, probability plots, rose diagrams and hemispherical projections. The paper describes in detail the theoretical basis of the implementation and provides a case study in rock fracture modelling to demonstrate the application of the software.

Prediction of rain effects on earth-space communication links operating in the 10 to 35 GHz frequency range

NASA Technical Reports Server (NTRS)

Stutzman, Warren L.

1989-01-01

This paper reviews the effects of precipitation on earth-space communication links operating the 10 to 35 GHz frequency range. Emphasis is on the quantitative prediction of rain attenuation and depolarization. Discussions center on the models developed at Virginia Tech. Comments on other models are included as well as literature references to key works. Also included is the system level modeling for dual polarized communication systems with techniques for calculating antenna and propagation medium effects. Simple models for the calculation of average annual attenuation and cross-polarization discrimination (XPD) are presented. Calculation of worst month statistics are also presented.

Slowdowns in diversification rates from real phylogenies may not be real.

PubMed

Cusimano, Natalie; Renner, Susanne S

2010-07-01

Studies of diversification patterns often find a slowing in lineage accumulation toward the present. This seemingly pervasive pattern of rate downturns has been taken as evidence for adaptive radiations, density-dependent regulation, and metacommunity species interactions. The significance of rate downturns is evaluated with statistical tests (the gamma statistic and Monte Carlo constant rates (MCCR) test; birth-death likelihood models and Akaike Information Criterion [AIC] scores) that rely on null distributions, which assume that the included species are a random sample of the entire clade. Sampling in real phylogenies, however, often is nonrandom because systematists try to include early-diverging species or representatives of previous intrataxon classifications. We studied the effects of biased sampling, structured sampling, and random sampling by experimentally pruning simulated trees (60 and 150 species) as well as a completely sampled empirical tree (58 species) and then applying the gamma statistic/MCCR test and birth-death likelihood models/AIC scores to assess rate changes. For trees with random species sampling, the true model (i.e., the one fitting the complete phylogenies) could be inferred in most cases. Oversampling deep nodes, however, strongly biases inferences toward downturns, with simulations of structured and biased sampling suggesting that this occurs when sampling percentages drop below 80%. The magnitude of the effect and the sensitivity of diversification rate models is such that a useful rule of thumb may be not to infer rate downturns from real trees unless they have >80% species sampling.

Evolution of Natural Attenuation Evaluation Protocols

EPA Science Inventory

Traditionally the evaluation of the efficacy of natural attenuation was based on changes in contaminant concentrations and mass reduction. Statistical tools and models such as Bioscreen provided evaluation protocols which now are being approached via other vehicles including m...

45 CFR 310.10 - What are the functional requirements for the Model Tribal IV-D System?

Code of Federal Regulations, 2011 CFR

2011-10-01

... plan, including: (1) Identifying information such as Social Security numbers, names, dates of birth... operations and to assess program performance through the audit of financial and statistical data maintained...

KINETICS OF LOW SOURCE REACTOR STARTUPS. PART I

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

Hurwitz, H. Jr.; MacMillan, D.B.; Smith, J.H.

1962-06-01

Statistical fluctuntions of neutron populations in reactors are analyzed by means of an approximate theoretical model. Development of the model is given in detail; also included are extensive numerical results derived from its application to systems with time-dependent reactivity, namely, a reactor during start-up. The special relationships of fluctuations to safety considerations are discussed. (auth)

Probabilistic Modeling and Evaluation of Surf Zone Injury Occurrence along the Delaware Coast

NASA Astrophysics Data System (ADS)

Doelp, M.; Puleo, J. A.

2017-12-01

Beebe Healthcare in Lewes, DE collected along the DE coast surf zone injury (SZI) data for seven summer seasons from 2010 through 2016. Data include, but are not limited to, time of injury, gender, age, and activity. Over 2000 injuries were recorded over the seven year period, including 116 spinal injuries and three fatalities. These injuries are predominantly wave related incidents including wading (41%), bodysurfing (26%), and body-boarding (20%). Despite the large number of injuries, beach associated hazards do not receive the same level of awareness that rip currents receive. Injury population statistics revealed those between the ages of 11 and 15 years old suffered the greatest proportion of injuries (18.8%). Male water users were twice as likely to sustain injury as their female counterparts. Also, non-locals were roughly six times more likely to sustain injury than locals. In 2016, five or more injuries occurred for 18.5% of the days sampled, and no injuries occurred for 31.4% of the sample days. The episodic nature of injury occurrence and population statistics indicate the importance of environmental conditions and human behavior on surf zone injuries. Higher order statistics are necessary to effectively assess SZI cause and likelihood of occurrence on a particular day. A Bayesian network using Netica software (Norsys) was constructed to model SZI and predict changes in injury likelihood on an hourly basis. The network incorporates environmental data collected by weather stations, NDBC buoy #44009, USACE buoy at Bethany Beach, and by researcher personnel on the beach. The Bayesian model includes prior (e.g., historic) information to infer relationships between provided parameters. Sensitivity analysis determined the most influential variables to injury likelihood are population, water temperature, nearshore wave height, beach slope, and the day of the week. Forecasting during the 2017 summer season will test model ability to predict injury likelihood.

Generalized background error covariance matrix model (GEN_BE v2.0)

NASA Astrophysics Data System (ADS)

Descombes, G.; Auligné, T.; Vandenberghe, F.; Barker, D. M.; Barré, J.

2015-03-01

The specification of state background error statistics is a key component of data assimilation since it affects the impact observations will have on the analysis. In the variational data assimilation approach, applied in geophysical sciences, the dimensions of the background error covariance matrix (B) are usually too large to be explicitly determined and B needs to be modeled. Recent efforts to include new variables in the analysis such as cloud parameters and chemical species have required the development of the code to GENerate the Background Errors (GEN_BE) version 2.0 for the Weather Research and Forecasting (WRF) community model. GEN_BE allows for a simpler, flexible, robust, and community-oriented framework that gathers methods used by some meteorological operational centers and researchers. We present the advantages of this new design for the data assimilation community by performing benchmarks of different modeling of B and showing some of the new features in data assimilation test cases. As data assimilation for clouds remains a challenge, we present a multivariate approach that includes hydrometeors in the control variables and new correlated errors. In addition, the GEN_BE v2.0 code is employed to diagnose error parameter statistics for chemical species, which shows that it is a tool flexible enough to implement new control variables. While the generation of the background errors statistics code was first developed for atmospheric research, the new version (GEN_BE v2.0) can be easily applied to other domains of science and chosen to diagnose and model B. Initially developed for variational data assimilation, the model of the B matrix may be useful for variational ensemble hybrid methods as well.

The Assessment of Climatological Impacts on Agricultural Production and Residential Energy Demand

NASA Astrophysics Data System (ADS)

Cooter, Ellen Jean

The assessment of climatological impacts on selected economic activities is presented as a multi-step, inter -disciplinary problem. The assessment process which is addressed explicitly in this report focuses on (1) user identification, (2) direct impact model selection, (3) methodological development, (4) product development and (5) product communication. Two user groups of major economic importance were selected for study; agriculture and gas utilities. The broad agricultural sector is further defined as U.S.A. corn production. The general category of utilities is narrowed to Oklahoma residential gas heating demand. The CERES physiological growth model was selected as the process model for corn production. The statistical analysis for corn production suggests that (1) although this is a statistically complex model, it can yield useful impact information, (2) as a result of output distributional biases, traditional statistical techniques are not adequate analytical tools, (3) the model yield distribution as a whole is probably non-Gausian, particularly in the tails and (4) there appears to be identifiable weekly patterns of forecasted yields throughout the growing season. Agricultural quantities developed include point yield impact estimates and distributional characteristics, geographic corn weather distributions, return period estimates, decision making criteria (confidence limits) and time series of indices. These products were communicated in economic terms through the use of a Bayesian decision example and an econometric model. The NBSLD energy load model was selected to represent residential gas heating consumption. A cursory statistical analysis suggests relationships among weather variables across the Oklahoma study sites. No linear trend in "technology -free" modeled energy demand or input weather variables which would correspond to that contained in observed state -level residential energy use was detected. It is suggested that this trend is largely the result of non-weather factors such as population and home usage patterns rather than regional climate change. Year-to-year changes in modeled residential heating demand on the order of 10('6) Btu's per household were determined and later related to state -level components of the Oklahoma economy. Products developed include the definition of regional forecast areas, likelihood estimates of extreme seasonal conditions and an energy/climate index. This information is communicated in economic terms through an input/output model which is used to estimate changes in Gross State Product and Household income attributable to weather variability.

Society of Thoracic Surgeons 2008 cardiac risk models predict in-hospital mortality of heart valve surgery in a Chinese population: a multicenter study.

PubMed

Wang, Lv; Lu, Fang-Lin; Wang, Chong; Tan, Meng-Wei; Xu, Zhi-yun

2014-12-01

The Society of Thoracic Surgeons 2008 cardiac surgery risk models have been developed for heart valve surgery with and without coronary artery bypass grafting. The aim of our study was to evaluate the performance of Society of Thoracic Surgeons 2008 cardiac risk models in Chinese patients undergoing single valve surgery and the predicted mortality rates of those undergoing multiple valve surgery derived from the Society of Thoracic Surgeons 2008 risk models. A total of 12,170 patients underwent heart valve surgery from January 2008 to December 2011. Combined congenital heart surgery and aortal surgery cases were excluded. A relatively small number of valve surgery combinations were excluded. The final research population included the following isolated heart valve surgery types: aortic valve replacement, mitral valve replacement, and mitral valve repair. The following combined valve surgery types were included: mitral valve replacement plus tricuspid valve repair, mitral valve replacement plus aortic valve replacement, and mitral valve replacement plus aortic valve replacement and tricuspid valve repair. Evaluation was performed by using the Hosmer-Lemeshow test and C-statistics. Data from 9846 patients were analyzed. The Society of Thoracic Surgeons 2008 cardiac risk models showed reasonable discrimination and poor calibration (C-statistic, 0.712; P = .00006 in Hosmer-Lemeshow test). Society of Thoracic Surgeons 2008 models had better discrimination (C-statistic, 0.734) and calibration (P = .5805) in patients undergoing isolated valve surgery than in patients undergoing multiple valve surgery (C-statistic, 0.694; P = .00002 in Hosmer-Lemeshow test). Estimates derived from the Society of Thoracic Surgeons 2008 models exceeded the mortality rates of multiple valve surgery (observed/expected ratios of 1.44 for multiple valve surgery and 1.17 for single valve surgery). The Society of Thoracic Surgeons 2008 cardiac surgery risk models performed well when predicting the mortality for Chinese patients undergoing valve surgery. The Society of Thoracic Surgeons 2008 models were suitable for single valve surgery in a Chinese population; estimates of mortality for multiple valve surgery derived from the Society of Thoracic Surgeons 2008 models were less accurate. Copyright © 2014 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

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.

The application of feature selection to the development of Gaussian process models for percutaneous absorption.

PubMed

Lam, Lun Tak; Sun, Yi; Davey, Neil; Adams, Rod; Prapopoulou, Maria; Brown, Marc B; Moss, Gary P

2010-06-01

The aim was to employ Gaussian processes to assess mathematically the nature of a skin permeability dataset and to employ these methods, particularly feature selection, to determine the key physicochemical descriptors which exert the most significant influence on percutaneous absorption, and to compare such models with established existing models. Gaussian processes, including automatic relevance detection (GPRARD) methods, were employed to develop models of percutaneous absorption that identified key physicochemical descriptors of percutaneous absorption. Using MatLab software, the statistical performance of these models was compared with single linear networks (SLN) and quantitative structure-permeability relationships (QSPRs). Feature selection methods were used to examine in more detail the physicochemical parameters used in this study. A range of statistical measures to determine model quality were used. The inherently nonlinear nature of the skin data set was confirmed. The Gaussian process regression (GPR) methods yielded predictive models that offered statistically significant improvements over SLN and QSPR models with regard to predictivity (where the rank order was: GPR > SLN > QSPR). Feature selection analysis determined that the best GPR models were those that contained log P, melting point and the number of hydrogen bond donor groups as significant descriptors. Further statistical analysis also found that great synergy existed between certain parameters. It suggested that a number of the descriptors employed were effectively interchangeable, thus questioning the use of models where discrete variables are output, usually in the form of an equation. The use of a nonlinear GPR method produced models with significantly improved predictivity, compared with SLN or QSPR models. Feature selection methods were able to provide important mechanistic information. However, it was also shown that significant synergy existed between certain parameters, and as such it was possible to interchange certain descriptors (i.e. molecular weight and melting point) without incurring a loss of model quality. Such synergy suggested that a model constructed from discrete terms in an equation may not be the most appropriate way of representing mechanistic understandings of skin absorption.

Assessing colour-dependent occupation statistics inferred from galaxy group catalogues

NASA Astrophysics Data System (ADS)

Campbell, Duncan; van den Bosch, Frank C.; Hearin, Andrew; Padmanabhan, Nikhil; Berlind, Andreas; Mo, H. J.; Tinker, Jeremy; Yang, Xiaohu

2015-09-01

We investigate the ability of current implementations of galaxy group finders to recover colour-dependent halo occupation statistics. To test the fidelity of group catalogue inferred statistics, we run three different group finders used in the literature over a mock that includes galaxy colours in a realistic manner. Overall, the resulting mock group catalogues are remarkably similar, and most colour-dependent statistics are recovered with reasonable accuracy. However, it is also clear that certain systematic errors arise as a consequence of correlated errors in group membership determination, central/satellite designation, and halo mass assignment. We introduce a new statistic, the halo transition probability (HTP), which captures the combined impact of all these errors. As a rule of thumb, errors tend to equalize the properties of distinct galaxy populations (i.e. red versus blue galaxies or centrals versus satellites), and to result in inferred occupation statistics that are more accurate for red galaxies than for blue galaxies. A statistic that is particularly poorly recovered from the group catalogues is the red fraction of central galaxies as a function of halo mass. Group finders do a good job in recovering galactic conformity, but also have a tendency to introduce weak conformity when none is present. We conclude that proper inference of colour-dependent statistics from group catalogues is best achieved using forward modelling (i.e. running group finders over mock data) or by implementing a correction scheme based on the HTP, as long as the latter is not too strongly model dependent.

Grain-Size Based Additivity Models for Scaling Multi-rate Uranyl Surface Complexation in Subsurface Sediments

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

Zhang, Xiaoying; Liu, Chongxuan; Hu, Bill X.

The additivity model assumed that field-scale reaction properties in a sediment including surface area, reactive site concentration, and reaction rate can be predicted from field-scale grain-size distribution by linearly adding reaction properties estimated in laboratory for individual grain-size fractions. This study evaluated the additivity model in scaling mass transfer-limited, multi-rate uranyl (U(VI)) surface complexation reactions in a contaminated sediment. Experimental data of rate-limited U(VI) desorption in a stirred flow-cell reactor were used to estimate the statistical properties of the rate constants for individual grain-size fractions, which were then used to predict rate-limited U(VI) desorption in the composite sediment. The resultmore » indicated that the additivity model with respect to the rate of U(VI) desorption provided a good prediction of U(VI) desorption in the composite sediment. However, the rate constants were not directly scalable using the additivity model. An approximate additivity model for directly scaling rate constants was subsequently proposed and evaluated. The result found that the approximate model provided a good prediction of the experimental results within statistical uncertainty. This study also found that a gravel-size fraction (2 to 8 mm), which is often ignored in modeling U(VI) sorption and desorption, is statistically significant to the U(VI) desorption in the sediment.« less

Pointwise probability reinforcements for robust statistical inference.

PubMed

Frénay, Benoît; Verleysen, Michel

2014-02-01

Statistical inference using machine learning techniques may be difficult with small datasets because of abnormally frequent data (AFDs). AFDs are observations that are much more frequent in the training sample that they should be, with respect to their theoretical probability, and include e.g. outliers. Estimates of parameters tend to be biased towards models which support such data. This paper proposes to introduce pointwise probability reinforcements (PPRs): the probability of each observation is reinforced by a PPR and a regularisation allows controlling the amount of reinforcement which compensates for AFDs. The proposed solution is very generic, since it can be used to robustify any statistical inference method which can be formulated as a likelihood maximisation. Experiments show that PPRs can be easily used to tackle regression, classification and projection: models are freed from the influence of outliers. Moreover, outliers can be filtered manually since an abnormality degree is obtained for each observation. Copyright © 2013 Elsevier Ltd. All rights reserved.

[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.

Computational methods to extract meaning from text and advance theories of human cognition.

PubMed

McNamara, Danielle S

2011-01-01

Over the past two decades, researchers have made great advances in the area of computational methods for extracting meaning from text. This research has to a large extent been spurred by the development of latent semantic analysis (LSA), a method for extracting and representing the meaning of words using statistical computations applied to large corpora of text. Since the advent of LSA, researchers have developed and tested alternative statistical methods designed to detect and analyze meaning in text corpora. This research exemplifies how statistical models of semantics play an important role in our understanding of cognition and contribute to the field of cognitive science. Importantly, these models afford large-scale representations of human knowledge and allow researchers to explore various questions regarding knowledge, discourse processing, text comprehension, and language. This topic includes the latest progress by the leading researchers in the endeavor to go beyond LSA. Copyright © 2010 Cognitive Science Society, Inc.

Rigorous force field optimization principles based on statistical distance minimization

DOE PAGES

Vlcek, Lukas; Chialvo, Ariel A.

2015-10-12

We use the concept of statistical distance to define a measure of distinguishability between a pair of statistical mechanical systems, i.e., a model and its target, and show that its minimization leads to general convergence of the model’s static measurable properties to those of the target. Here we exploit this feature to define a rigorous basis for the development of accurate and robust effective molecular force fields that are inherently compatible with coarse-grained experimental data. The new model optimization principles and their efficient implementation are illustrated through selected examples, whose outcome demonstrates the higher robustness and predictive accuracy of themore » approach compared to other currently used methods, such as force matching and relative entropy minimization. We also discuss relations between the newly developed principles and established thermodynamic concepts, which include the Gibbs-Bogoliubov inequality and the thermodynamic length.« less

Multivariable Parametric Cost Model for Ground Optical Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2005-01-01

A parametric cost model for ground-based telescopes is developed using multivariable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction-limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature are examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e., multi-telescope phased-array systems). Additionally, single variable models Based on aperture diameter are derived.

Estimating individual benefits of medical or behavioral treatments in severely ill patients.

PubMed

Diaz, Francisco J

2017-01-01

There is a need for statistical methods appropriate for the analysis of clinical trials from a personalized-medicine viewpoint as opposed to the common statistical practice that simply examines average treatment effects. This article proposes an approach to quantifying, reporting and analyzing individual benefits of medical or behavioral treatments to severely ill patients with chronic conditions, using data from clinical trials. The approach is a new development of a published framework for measuring the severity of a chronic disease and the benefits treatments provide to individuals, which utilizes regression models with random coefficients. Here, a patient is considered to be severely ill if the patient's basal severity is close to one. This allows the derivation of a very flexible family of probability distributions of individual benefits that depend on treatment duration and the covariates included in the regression model. Our approach may enrich the statistical analysis of clinical trials of severely ill patients because it allows investigating the probability distribution of individual benefits in the patient population and the variables that influence it, and we can also measure the benefits achieved in specific patients including new patients. We illustrate our approach using data from a clinical trial of the anti-depressant imipramine.

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

PubMed Central

2018-01-01

The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies. PMID:29765399

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress.

PubMed

Cheng, Ching-Hsue; Chan, Chia-Pang; Yang, Jun-He

2018-01-01

The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.

Statistical hadronization and microcanonical ensemble

DOE PAGES

Becattini, F.; Ferroni, L.

2004-01-01

We present a Monte Carlo calculation of the microcanonical ensemble of the of the ideal hadron-resonance gas including all known states up to a mass of 1. 8 GeV, taking into account quantum statistics. The computing method is a development of a previous one based on a Metropolis Monte Carlo algorithm, with a the grand-canonical limit of the multi-species multiplicity distribution as proposal matrix. The microcanonical average multiplicities of the various hadron species are found to converge to the canonical ones for moderately low values of the total energy. This algorithm opens the way for event generators based for themore » statistical hadronization model.« less

Evaluation of different models to estimate the global solar radiation on inclined surface

NASA Astrophysics Data System (ADS)

Demain, C.; Journée, M.; Bertrand, C.

2012-04-01

Global and diffuse solar radiation intensities are, in general, measured on horizontal surfaces, whereas stationary solar conversion systems (both flat plate solar collector and solar photovoltaic) are mounted on inclined surface to maximize the amount of solar radiation incident on the collector surface. Consequently, the solar radiation incident measured on a tilted surface has to be determined by converting solar radiation from horizontal surface to tilted surface of interest. This study evaluates the performance of 14 models transposing 10 minutes, hourly and daily diffuse solar irradiation from horizontal to inclined surface. Solar radiation data from 8 months (April to November 2011) which include diverse atmospheric conditions and solar altitudes, measured on the roof of the radiation tower of the Royal Meteorological Institute of Belgium in Uccle (Longitude 4.35°, Latitude 50.79°) were used for validation purposes. The individual model performance is assessed by an inter-comparison between the calculated and measured solar global radiation on the south-oriented surface tilted at 50.79° using statistical methods. The relative performance of the different models under different sky conditions has been studied. Comparison of the statistical errors between the different radiation models in function of the clearness index shows that some models perform better under one type of sky condition. Putting together different models acting under different sky conditions can lead to a diminution of the statistical error between global measured solar radiation and global estimated solar radiation. As models described in this paper have been developed for hourly data inputs, statistical error indexes are minimum for hourly data and increase for 10 minutes and one day frequency data.

Statistical Analysis of Model Data for Operational Space Launch Weather Support at Kennedy Space Center and Cape Canaveral Air Force Station

NASA Technical Reports Server (NTRS)

Bauman, William H., III

2010-01-01

The 12-km resolution North American Mesoscale (NAM) model (MesoNAM) is used by the 45th Weather Squadron (45 WS) Launch Weather Officers at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) to support space launch weather operations. The 45 WS tasked the Applied Meteorology Unit to conduct an objective statistics-based analysis of MesoNAM output compared to wind tower mesonet observations and then develop a an operational tool to display the results. The National Centers for Environmental Prediction began running the current version of the MesoNAM in mid-August 2006. The period of record for the dataset was 1 September 2006 - 31 January 2010. The AMU evaluated MesoNAM hourly forecasts from 0 to 84 hours based on model initialization times of 00, 06, 12 and 18 UTC. The MesoNAM forecast winds, temperature and dew point were compared to the observed values of these parameters from the sensors in the KSC/CCAFS wind tower network. The data sets were stratified by model initialization time, month and onshore/offshore flow for each wind tower. Statistics computed included bias (mean difference), standard deviation of the bias, root mean square error (RMSE) and a hypothesis test for bias = O. Twelve wind towers located in close proximity to key launch complexes were used for the statistical analysis with the sensors on the towers positioned at varying heights to include 6 ft, 30 ft, 54 ft, 60 ft, 90 ft, 162 ft, 204 ft and 230 ft depending on the launch vehicle and associated weather launch commit criteria being evaluated. These twelve wind towers support activities for the Space Shuttle (launch and landing), Delta IV, Atlas V and Falcon 9 launch vehicles. For all twelve towers, the results indicate a diurnal signal in the bias of temperature (T) and weaker but discernable diurnal signal in the bias of dewpoint temperature (T(sub d)) in the MesoNAM forecasts. Also, the standard deviation of the bias and RMSE of T, T(sub d), wind speed and wind direction indicated the model error increased with the forecast period all four parameters. The hypothesis testing uses statistics to determine the probability that a given hypothesis is true. The goal of using the hypothesis test was to determine if the model bias of any of the parameters assessed throughout the model forecast period was statistically zero. For th is dataset, if this test produced a value >= -1 .96 or <= 1.96 for a data point, then the bias at that point was effectively zero and the model forecast for that point was considered to have no error. A graphical user interface (GUI) was developed so the 45 WS would have an operational tool at their disposal that would be easy to navigate among the multiple stratifications of information to include tower locations, month, model initialization times, sensor heights and onshore/offshore flow. The AMU developed the GUI using HyperText Markup Language (HTML) so the tool could be used in most popular web browsers with computers running different operating systems such as Microsoft Windows and Linux.

Comparative Research Productivity Measures for Economic Departments.

ERIC Educational Resources Information Center

Huettner, David A.; Clark, William

1997-01-01

Develops a simple theoretical model to evaluate interdisciplinary differences in research productivity between economics departments and related subjects. Compares the research publishing statistics of economics, finance, psychology, geology, physics, oceanography, chemistry, and geophysics. Considers a number of factors including journal…

Goodness-Of-Fit Test for Nonparametric Regression Models: Smoothing Spline ANOVA Models as Example.

PubMed

Teran Hidalgo, Sebastian J; Wu, Michael C; Engel, Stephanie M; Kosorok, Michael R

2018-06-01

Nonparametric regression models do not require the specification of the functional form between the outcome and the covariates. Despite their popularity, the amount of diagnostic statistics, in comparison to their parametric counter-parts, is small. We propose a goodness-of-fit test for nonparametric regression models with linear smoother form. In particular, we apply this testing framework to smoothing spline ANOVA models. The test can consider two sources of lack-of-fit: whether covariates that are not currently in the model need to be included, and whether the current model fits the data well. The proposed method derives estimated residuals from the model. Then, statistical dependence is assessed between the estimated residuals and the covariates using the HSIC. If dependence exists, the model does not capture all the variability in the outcome associated with the covariates, otherwise the model fits the data well. The bootstrap is used to obtain p-values. Application of the method is demonstrated with a neonatal mental development data analysis. We demonstrate correct type I error as well as power performance through simulations.

Random forests for classification in ecology

USGS Publications Warehouse

Cutler, D.R.; Edwards, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J.

2007-01-01

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature. ?? 2007 by the Ecological Society of America.

Claims-based risk model for first severe COPD exacerbation.

PubMed

Stanford, Richard H; Nag, Arpita; Mapel, Douglas W; Lee, Todd A; Rosiello, Richard; Schatz, Michael; Vekeman, Francis; Gauthier-Loiselle, Marjolaine; Merrigan, J F Philip; Duh, Mei Sheng

2018-02-01

To develop and validate a predictive model for first severe chronic obstructive pulmonary disease (COPD) exacerbation using health insurance claims data and to validate the risk measure of controller medication to total COPD treatment (controller and rescue) ratio (CTR). A predictive model was developed and validated in 2 managed care databases: Truven Health MarketScan database and Reliant Medical Group database. This secondary analysis assessed risk factors, including CTR, during the baseline period (Year 1) to predict risk of severe exacerbation in the at-risk period (Year 2). Patients with COPD who were 40 years or older and who had at least 1 COPD medication dispensed during the year following COPD diagnosis were included. Subjects with severe exacerbations in the baseline year were excluded. Risk factors in the baseline period were included as potential predictors in multivariate analysis. Performance was evaluated using C-statistics. The analysis included 223,824 patients. The greatest risk factors for first severe exacerbation were advanced age, chronic oxygen therapy usage, COPD diagnosis type, dispensing of 4 or more canisters of rescue medication, and having 2 or more moderate exacerbations. A CTR of 0.3 or greater was associated with a 14% lower risk of severe exacerbation. The model performed well with C-statistics, ranging from 0.711 to 0.714. This claims-based risk model can predict the likelihood of first severe COPD exacerbation. The CTR could also potentially be used to target populations at greatest risk for severe exacerbations. This could be relevant for providers and payers in approaches to prevent severe exacerbations and reduce costs.

Markov Logic Networks in the Analysis of Genetic Data

PubMed Central

Sakhanenko, Nikita A.

2010-01-01

Abstract Complex, non-additive genetic interactions are common and can be critical in determining phenotypes. Genome-wide association studies (GWAS) and similar statistical studies of linkage data, however, assume additive models of gene interactions in looking for genotype-phenotype associations. These statistical methods view the compound effects of multiple genes on a phenotype as a sum of influences of each gene and often miss a substantial part of the heritable effect. Such methods do not use any biological knowledge about underlying mechanisms. Modeling approaches from the artificial intelligence (AI) field that incorporate deterministic knowledge into models to perform statistical analysis can be applied to include prior knowledge in genetic analysis. We chose to use the most general such approach, Markov Logic Networks (MLNs), for combining deterministic knowledge with statistical analysis. Using simple, logistic regression-type MLNs we can replicate the results of traditional statistical methods, but we also show that we are able to go beyond finding independent markers linked to a phenotype by using joint inference without an independence assumption. The method is applied to genetic data on yeast sporulation, a complex phenotype with gene interactions. In addition to detecting all of the previously identified loci associated with sporulation, our method identifies four loci with smaller effects. Since their effect on sporulation is small, these four loci were not detected with methods that do not account for dependence between markers due to gene interactions. We show how gene interactions can be detected using more complex models, which can be used as a general framework for incorporating systems biology with genetics. PMID:20958249

Comparison of LIDAR system performance for alternative single-mode receiver architectures: modeling and experimental validation

NASA Astrophysics Data System (ADS)

Toliver, Paul; Ozdur, Ibrahim; Agarwal, Anjali; Woodward, T. K.

2013-05-01

In this paper, we describe a detailed performance comparison of alternative single-pixel, single-mode LIDAR architectures including (i) linear-mode APD-based direct-detection, (ii) optically-preamplified PIN receiver, (iii) PINbased coherent-detection, and (iv) Geiger-mode single-photon-APD counting. Such a comparison is useful when considering next-generation LIDAR on a chip, which would allow one to leverage extensive waveguide-based structures and processing elements developed for telecom and apply them to small form-factor sensing applications. Models of four LIDAR transmit and receive systems are described in detail, which include not only the dominant sources of receiver noise commonly assumed in each of the four detection limits, but also additional noise terms present in realistic implementations. These receiver models are validated through the analysis of detection statistics collected from an experimental LIDAR testbed. The receiver is reconfigurable into four modes of operation, while transmit waveforms and channel characteristics are held constant. The use of a diffuse hard target highlights the importance of including speckle noise terms in the overall system analysis. All measurements are done at 1550 nm, which offers multiple system advantages including less stringent eye safety requirements and compatibility with available telecom components, optical amplification, and photonic integration. Ultimately, the experimentally-validated detection statistics can be used as part of an end-to-end system model for projecting rate, range, and resolution performance limits and tradeoffs of alternative integrated LIDAR architectures.

A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance.

PubMed

Meads, Catherine; Ahmed, Ikhlaaq; Riley, Richard D

2012-04-01

A risk prediction model is a statistical tool for estimating the probability that a currently healthy individual with specific risk factors will develop a condition in the future such as breast cancer. Reliably accurate prediction models can inform future disease burdens, health policies and individual decisions. Breast cancer prediction models containing modifiable risk factors, such as alcohol consumption, BMI or weight, condom use, exogenous hormone use and physical activity, are of particular interest to women who might be considering how to reduce their risk of breast cancer and clinicians developing health policies to reduce population incidence rates. We performed a systematic review to identify and evaluate the performance of prediction models for breast cancer that contain modifiable factors. A protocol was developed and a sensitive search in databases including MEDLINE and EMBASE was conducted in June 2010. Extensive use was made of reference lists. Included were any articles proposing or validating a breast cancer prediction model in a general female population, with no language restrictions. Duplicate data extraction and quality assessment were conducted. Results were summarised qualitatively, and where possible meta-analysis of model performance statistics was undertaken. The systematic review found 17 breast cancer models, each containing a different but often overlapping set of modifiable and other risk factors, combined with an estimated baseline risk that was also often different. Quality of reporting was generally poor, with characteristics of included participants and fitted model results often missing. Only four models received independent validation in external data, most notably the 'Gail 2' model with 12 validations. None of the models demonstrated consistently outstanding ability to accurately discriminate between those who did and those who did not develop breast cancer. For example, random-effects meta-analyses of the performance of the 'Gail 2' model showed the average C statistic was 0.63 (95% CI 0.59-0.67), and the expected/observed ratio of events varied considerably across studies (95% prediction interval for E/O ratio when the model was applied in practice was 0.75-1.19). There is a need for models with better predictive performance but, given the large amount of work already conducted, further improvement of existing models based on conventional risk factors is perhaps unlikely. Research to identify new risk factors with large additionally predictive ability is therefore needed, alongside clearer reporting and continual validation of new models as they develop.

A Markov Environment-dependent Hurricane Intensity Model and Its Comparison with Multiple Dynamic Models

NASA Astrophysics Data System (ADS)

Jing, R.; Lin, N.; Emanuel, K.; Vecchi, G. A.; Knutson, T. R.

2017-12-01

A Markov environment-dependent hurricane intensity model (MeHiM) is developed to simulate the climatology of hurricane intensity given the surrounding large-scale environment. The model considers three unobserved discrete states representing respectively storm's slow, moderate, and rapid intensification (and deintensification). Each state is associated with a probability distribution of intensity change. The storm's movement from one state to another, regarded as a Markov chain, is described by a transition probability matrix. The initial state is estimated with a Bayesian approach. All three model components (initial intensity, state transition, and intensity change) are dependent on environmental variables including potential intensity, vertical wind shear, midlevel relative humidity, and ocean mixing characteristics. This dependent Markov model of hurricane intensity shows a significant improvement over previous statistical models (e.g., linear, nonlinear, and finite mixture models) in estimating the distributions of 6-h and 24-h intensity change, lifetime maximum intensity, and landfall intensity, etc. Here we compare MeHiM with various dynamical models, including a global climate model [High-Resolution Forecast-Oriented Low Ocean Resolution model (HiFLOR)], a regional hurricane model (Geophysical Fluid Dynamics Laboratory (GFDL) hurricane model), and a simplified hurricane dynamic model [Coupled Hurricane Intensity Prediction System (CHIPS)] and its newly developed fast simulator. The MeHiM developed based on the reanalysis data is applied to estimate the intensity of simulated storms to compare with the dynamical-model predictions under the current climate. The dependences of hurricanes on the environment under current and future projected climates in the various models will also be compared statistically.

Long-term occlusal changes assessed by the American Board of Orthodontics' model grading system.

PubMed

Aszkler, Robert M; Preston, Charles B; Saltaji, Humam; Tabbaa, Sawsan

2014-02-01

The purpose of this study was to assess the long-term posttreatment changes in all criteria of the American Board of Orthodontics' (ABO) model grading system. We used plaster models from patients' final and posttreatment records. Thirty patients treated by 1 orthodontist using 1 bracket prescription were selected. An initial discrepancy index for each subject was performed to determine the complexity of each case. The final models were then graded using the ABO's model grading system immediately at posttreatment and postretention. Statistical analysis was performed on the 8 criteria of the model grading system, including paired t tests and Pearson correlations. An alpha of 0.05 was considered statistically significant. The average length of time between the posttreatment and postretention records was 12.7 ± 4.4 years. It was shown that alignment and rotations worsened by postretention (P = 0.014), and a weak statistically significant correlation at posttreatment and postretention was found (0.44; P = 0.016). Both marginal ridges and occlusal contacts scored less well at posttreatment. These criteria showed a significant decrease in scores between posttreatment and postretention (P <0.001), but the correlations were not statistically significant. The average total score showed a significant decrease between posttreatment and postretention (P <0.001), partly because of the large decrease in the previous 2 criteria. Higher scores for occlusal contacts and marginal ridges were found at the end of treatment; however, those scores and the overall scores for the 30 subjects improved in the postretention phase. Copyright © 2014. Published by Mosby, Inc.

Modeling Traffic on the Web Graph

NASA Astrophysics Data System (ADS)

Meiss, Mark R.; Gonçalves, Bruno; Ramasco, José J.; Flammini, Alessandro; Menczer, Filippo

Analysis of aggregate and individual Web requests shows that PageRank is a poor predictor of traffic. We use empirical data to characterize properties of Web traffic not reproduced by Markovian models, including both aggregate statistics such as page and link traffic, and individual statistics such as entropy and session size. As no current model reconciles all of these observations, we present an agent-based model that explains them through realistic browsing behaviors: (1) revisiting bookmarked pages; (2) backtracking; and (3) seeking out novel pages of topical interest. The resulting model can reproduce the behaviors we observe in empirical data, especially heterogeneous session lengths, reconciling the narrowly focused browsing patterns of individual users with the extreme variance in aggregate traffic measurements. We can thereby identify a few salient features that are necessary and sufficient to interpret Web traffic data. Beyond the descriptive and explanatory power of our model, these results may lead to improvements in Web applications such as search and crawling.

Development of an errorable car-following driver model

NASA Astrophysics Data System (ADS)

Yang, H.-H.; Peng, H.

2010-06-01

An errorable car-following driver model is presented in this paper. An errorable driver model is one that emulates human driver's functions and can generate both nominal (error-free), as well as devious (with error) behaviours. This model was developed for evaluation and design of active safety systems. The car-following data used for developing and validating the model were obtained from a large-scale naturalistic driving database. The stochastic car-following behaviour was first analysed and modelled as a random process. Three error-inducing behaviours were then introduced. First, human perceptual limitation was studied and implemented. Distraction due to non-driving tasks was then identified based on the statistical analysis of the driving data. Finally, time delay of human drivers was estimated through a recursive least-square identification process. By including these three error-inducing behaviours, rear-end collisions with the lead vehicle could occur. The simulated crash rate was found to be similar but somewhat higher than that reported in traffic statistics.

A diagnostic model for chronic hypersensitivity pneumonitis

PubMed Central

Johannson, Kerri A; Elicker, Brett M; Vittinghoff, Eric; Assayag, Deborah; de Boer, Kaïssa; Golden, Jeffrey A; Jones, Kirk D; King, Talmadge E; Koth, Laura L; Lee, Joyce S; Ley, Brett; Wolters, Paul J; Collard, Harold R

2017-01-01

The objective of this study was to develop a diagnostic model that allows for a highly specific diagnosis of chronic hypersensitivity pneumonitis using clinical and radiological variables alone. Chronic hypersensitivity pneumonitis and other interstitial lung disease cases were retrospectively identified from a longitudinal database. High-resolution CT scans were blindly scored for radiographic features (eg, ground-glass opacity, mosaic perfusion) as well as the radiologist’s diagnostic impression. Candidate models were developed then evaluated using clinical and radiographic variables and assessed by the cross-validated C-statistic. Forty-four chronic hypersensitivity pneumonitis and eighty other interstitial lung disease cases were identified. Two models were selected based on their statistical performance, clinical applicability and face validity. Key model variables included age, down feather and/or bird exposure, radiographic presence of ground-glass opacity and mosaic perfusion and moderate or high confidence in the radiographic impression of chronic hypersensitivity pneumonitis. Models were internally validated with good performance, and cut-off values were established that resulted in high specificity for a diagnosis of chronic hypersensitivity pneumonitis. PMID:27245779

Final Report - Enhanced LAW Glass Property - Composition Models - Phase 1 VSL-13R2940-1, Rev. 0, dated 9/27/2013

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

Kruger, Albert A.; Muller, I.; Gilbo, K.

2013-11-13

The objectives of this work are aimed at the development of enhanced LAW propertycomposition models that expand the composition region covered by the models. The models of interest include PCT, VHT, viscosity and electrical conductivity. This is planned as a multi-year effort that will be performed in phases with the objectives listed below for the current phase.  Incorporate property- composition data from the new glasses into the database.  Assess the database and identify composition spaces in the database that need augmentation.  Develop statistically-designed composition matrices to cover the composition regions identified in the above analysis.  Preparemore » crucible melts of glass compositions from the statistically-designed composition matrix and measure the properties of interest.  Incorporate the above property-composition data into the database.  Assess existing models against the complete dataset and, as necessary, start development of new models.« less

Statistically Based Approach to Broadband Liner Design and Assessment

NASA Technical Reports Server (NTRS)

Jones, Michael G. (Inventor); Nark, Douglas M. (Inventor)

2016-01-01

A broadband liner design optimization includes utilizing in-duct attenuation predictions with a statistical fan source model to obtain optimum impedance spectra over a number of flow conditions for one or more liner locations in a bypass duct. The predicted optimum impedance information is then used with acoustic liner modeling tools to design liners having impedance spectra that most closely match the predicted optimum values. Design selection is based on an acceptance criterion that provides the ability to apply increasing weighting to specific frequencies and/or operating conditions. One or more broadband design approaches are utilized to produce a broadband liner that targets a full range of frequencies and operating conditions.

Exact Local Correlations and Full Counting Statistics for Arbitrary States of the One-Dimensional Interacting Bose Gas

NASA Astrophysics Data System (ADS)

Bastianello, Alvise; Piroli, Lorenzo; Calabrese, Pasquale

2018-05-01

We derive exact analytic expressions for the n -body local correlations in the one-dimensional Bose gas with contact repulsive interactions (Lieb-Liniger model) in the thermodynamic limit. Our results are valid for arbitrary states of the model, including ground and thermal states, stationary states after a quantum quench, and nonequilibrium steady states arising in transport settings. Calculations for these states are explicitly presented and physical consequences are critically discussed. We also show that the n -body local correlations are directly related to the full counting statistics for the particle-number fluctuations in a short interval, for which we provide an explicit analytic result.

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.

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

ABSTRACT Motivation: Quantitative mass spectrometry-based proteomics requires protein-level estimates and confidence measures. Challenges include the presence of low-quality or incorrectly identified peptides and widespread, informative, missing data. Furthermore, models are required for rolling peptide-level information up to the protein level. Results: We present a statistical model for protein abundance in terms of peptide peak intensities, applicable to both label-based and label-free quantitation experiments. The model allows for both random and censoring missingness mechanisms and provides naturally for protein-level estimates and confidence measures. The model is also used to derive automated filtering and imputation routines. Three LC-MS datasets are used tomore » illustrate the methods. Availability: The software has been made available in the open-source proteomics platform DAnTE (Polpitiya et al. (2008)) (http://omics.pnl.gov/software/). Contact: adabney@stat.tamu.edu« less

Remote sensing-aided systems for snow qualification, evapotranspiration estimation, and their application in hydrologic models

NASA Technical Reports Server (NTRS)

Korram, S.

1977-01-01

The design of general remote sensing-aided methodologies was studied to provide the estimates of several important inputs to water yield forecast models. These input parameters are snow area extent, snow water content, and evapotranspiration. The study area is Feather River Watershed (780,000 hectares), Northern California. The general approach involved a stepwise sequence of identification of the required information, sample design, measurement/estimation, and evaluation of results. All the relevent and available information types needed in the estimation process are being defined. These include Landsat, meteorological satellite, and aircraft imagery, topographic and geologic data, ground truth data, and climatic data from ground stations. A cost-effective multistage sampling approach was employed in quantification of all the required parameters. The physical and statistical models for both snow quantification and evapotranspiration estimation was developed. These models use the information obtained by aerial and ground data through appropriate statistical sampling design.

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

An Overview of R in Health Decision Sciences.

PubMed

Jalal, Hawre; Pechlivanoglou, Petros; Krijkamp, Eline; Alarid-Escudero, Fernando; Enns, Eva; Hunink, M G Myriam

2017-10-01

As the complexity of health decision science applications increases, high-level programming languages are increasingly adopted for statistical analyses and numerical computations. These programming languages facilitate sophisticated modeling, model documentation, and analysis reproducibility. Among the high-level programming languages, the statistical programming framework R is gaining increased recognition. R is freely available, cross-platform compatible, and open source. A large community of users who have generated an extensive collection of well-documented packages and functions supports it. These functions facilitate applications of health decision science methodology as well as the visualization and communication of results. Although R's popularity is increasing among health decision scientists, methodological extensions of R in the field of decision analysis remain isolated. The purpose of this article is to provide an overview of existing R functionality that is applicable to the various stages of decision analysis, including model design, input parameter estimation, and analysis of model outputs.

Scaling in geology: landforms and earthquakes.

PubMed Central

Turcotte, D L

1995-01-01

Landforms and earthquakes appear to be extremely complex; yet, there is order in the complexity. Both satisfy fractal statistics in a variety of ways. A basic question is whether the fractal behavior is due to scale invariance or is the signature of a broadly applicable class of physical processes. Both landscape evolution and regional seismicity appear to be examples of self-organized critical phenomena. A variety of statistical models have been proposed to model landforms, including diffusion-limited aggregation, self-avoiding percolation, and cellular automata. Many authors have studied the behavior of multiple slider-block models, both in terms of the rupture of a fault to generate an earthquake and in terms of the interactions between faults associated with regional seismicity. The slider-block models exhibit a remarkably rich spectrum of behavior; two slider blocks can exhibit low-order chaotic behavior. Large numbers of slider blocks clearly exhibit self-organized critical behavior. Images Fig. 6 PMID:11607562

Statistical parameters of random heterogeneity estimated by analysing coda waves based on finite difference method

NASA Astrophysics Data System (ADS)

Emoto, K.; Saito, T.; Shiomi, K.

2017-12-01

Short-period (<1 s) seismograms are strongly affected by small-scale (<10 km) heterogeneities in the lithosphere. In general, short-period seismograms are analysed based on the statistical method by considering the interaction between seismic waves and randomly distributed small-scale heterogeneities. Statistical properties of the random heterogeneities have been estimated by analysing short-period seismograms. However, generally, the small-scale random heterogeneity is not taken into account for the modelling of long-period (>2 s) seismograms. We found that the energy of the coda of long-period seismograms shows a spatially flat distribution. This phenomenon is well known in short-period seismograms and results from the scattering by small-scale heterogeneities. We estimate the statistical parameters that characterize the small-scale random heterogeneity by modelling the spatiotemporal energy distribution of long-period seismograms. We analyse three moderate-size earthquakes that occurred in southwest Japan. We calculate the spatial distribution of the energy density recorded by a dense seismograph network in Japan at the period bands of 8-16 s, 4-8 s and 2-4 s and model them by using 3-D finite difference (FD) simulations. Compared to conventional methods based on statistical theories, we can calculate more realistic synthetics by using the FD simulation. It is not necessary to assume a uniform background velocity, body or surface waves and scattering properties considered in general scattering theories. By taking the ratio of the energy of the coda area to that of the entire area, we can separately estimate the scattering and the intrinsic absorption effects. Our result reveals the spectrum of the random inhomogeneity in a wide wavenumber range including the intensity around the corner wavenumber as P(m) = 8πε2a3/(1 + a2m2)2, where ε = 0.05 and a = 3.1 km, even though past studies analysing higher-frequency records could not detect the corner. Finally, we estimate the intrinsic attenuation by modelling the decay rate of the energy. The method proposed in this study is suitable for quantifying the statistical properties of long-wavelength subsurface random inhomogeneity, which leads the way to characterizing a wider wavenumber range of spectra, including the corner wavenumber.

An R package for analyzing and modeling ranking data

PubMed Central

2013-01-01

Background In medical informatics, psychology, market research and many other fields, researchers often need to analyze and model ranking data. However, there is no statistical software that provides tools for the comprehensive analysis of ranking data. Here, we present pmr, an R package for analyzing and modeling ranking data with a bundle of tools. The pmr package enables descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty’s and Koczkodaj’s inconsistencies), probability models (Luce model, distance-based model, and rank-ordered logit model), and the visualization of ranking data with multidimensional preference analysis. Results Examples of the use of package pmr are given using a real ranking dataset from medical informatics, in which 566 Hong Kong physicians ranked the top five incentives (1: competitive pressures; 2: increased savings; 3: government regulation; 4: improved efficiency; 5: improved quality care; 6: patient demand; 7: financial incentives) to the computerization of clinical practice. The mean rank showed that item 4 is the most preferred item and item 3 is the least preferred item, and significance difference was found between physicians’ preferences with respect to their monthly income. A multidimensional preference analysis identified two dimensions that explain 42% of the total variance. The first can be interpreted as the overall preference of the seven items (labeled as “internal/external”), and the second dimension can be interpreted as their overall variance of (labeled as “push/pull factors”). Various statistical models were fitted, and the best were found to be weighted distance-based models with Spearman’s footrule distance. Conclusions In this paper, we presented the R package pmr, the first package for analyzing and modeling ranking data. The package provides insight to users through descriptive statistics of ranking data. Users can also visualize ranking data by applying a thought multidimensional preference analysis. Various probability models for ranking data are also included, allowing users to choose that which is most suitable to their specific situations. PMID:23672645

An R package for analyzing and modeling ranking data.

PubMed

Lee, Paul H; Yu, Philip L H

2013-05-14

In medical informatics, psychology, market research and many other fields, researchers often need to analyze and model ranking data. However, there is no statistical software that provides tools for the comprehensive analysis of ranking data. Here, we present pmr, an R package for analyzing and modeling ranking data with a bundle of tools. The pmr package enables descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce model, distance-based model, and rank-ordered logit model), and the visualization of ranking data with multidimensional preference analysis. Examples of the use of package pmr are given using a real ranking dataset from medical informatics, in which 566 Hong Kong physicians ranked the top five incentives (1: competitive pressures; 2: increased savings; 3: government regulation; 4: improved efficiency; 5: improved quality care; 6: patient demand; 7: financial incentives) to the computerization of clinical practice. The mean rank showed that item 4 is the most preferred item and item 3 is the least preferred item, and significance difference was found between physicians' preferences with respect to their monthly income. A multidimensional preference analysis identified two dimensions that explain 42% of the total variance. The first can be interpreted as the overall preference of the seven items (labeled as "internal/external"), and the second dimension can be interpreted as their overall variance of (labeled as "push/pull factors"). Various statistical models were fitted, and the best were found to be weighted distance-based models with Spearman's footrule distance. In this paper, we presented the R package pmr, the first package for analyzing and modeling ranking data. The package provides insight to users through descriptive statistics of ranking data. Users can also visualize ranking data by applying a thought multidimensional preference analysis. Various probability models for ranking data are also included, allowing users to choose that which is most suitable to their specific situations.