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Estimating The Covariance Components Of An Unbalanced Multivariate Latent Random Model Via The Em Algorithm
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Book Synopsis Estimating the Covariance Components of an Unbalanced Multivariate Latent Random Model Via the EM Algorithm by : Leonard Joseph Bianchi
Download or read book Estimating the Covariance Components of an Unbalanced Multivariate Latent Random Model Via the EM Algorithm written by Leonard Joseph Bianchi and published by . This book was released on 1987 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Contemporary Multivariate Analysis and Design of Experiments by : Kaitai Fang
Download or read book Contemporary Multivariate Analysis and Design of Experiments written by Kaitai Fang and published by World Scientific. This book was released on 2005 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: Index. Subject index -- Author index
Book Synopsis Random Effect and Latent Variable Model Selection by : David Dunson
Download or read book Random Effect and Latent Variable Model Selection written by David Dunson and published by Springer Science & Business Media. This book was released on 2010-03-18 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Random Effect and Latent Variable Model Selection In recent years, there has been a dramatic increase in the collection of multivariate and correlated data in a wide variety of ?elds. For example, it is now standard pr- tice to routinely collect many response variables on each individual in a study. The different variables may correspond to repeated measurements over time, to a battery of surrogates for one or more latent traits, or to multiple types of outcomes having an unknown dependence structure. Hierarchical models that incorporate subje- speci?c parameters are one of the most widely-used tools for analyzing multivariate and correlated data. Such subject-speci?c parameters are commonly referred to as random effects, latent variables or frailties. There are two modeling frameworks that have been particularly widely used as hierarchical generalizations of linear regression models. The ?rst is the linear mixed effects model (Laird and Ware , 1982) and the second is the structural equation model (Bollen , 1989). Linear mixed effects (LME) models extend linear regr- sion to incorporate two components, with the ?rst corresponding to ?xed effects describing the impact of predictors on the mean and the second to random effects characterizing the impact on the covariance. LMEs have also been increasingly used for function estimation. In implementing LME analyses, model selection problems are unavoidable. For example, there may be interest in comparing models with and without a predictor in the ?xed and/or random effects component.
Book Synopsis Modeling Contextual Effects in Longitudinal Studies by : Todd D. Little
Download or read book Modeling Contextual Effects in Longitudinal Studies written by Todd D. Little and published by Routledge. This book was released on 2007-03-21 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling the impact and influence of contextual factors on human development is something that many talk about but few actually do. The goal of this book is to provide researchers with an accessible guide to understanding the many different ways that contextual factors can be including in longitudinal studies of human development.
Book Synopsis Dissertation Abstracts International by :
Download or read book Dissertation Abstracts International written by and published by . This book was released on 1988 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Annual Meeting Program by : American Educational Research Association
Download or read book Annual Meeting Program written by American Educational Research Association and published by . This book was released on 1990 with total page 1042 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Handbook of Research Methods in Social and Personality Psychology by : Harry T. Reis
Download or read book Handbook of Research Methods in Social and Personality Psychology written by Harry T. Reis and published by Cambridge University Press. This book was released on 2000-03-13 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume, first published in 2000, provides an overview of research methods in contemporary social psychology.
Book Synopsis American Doctoral Dissertations by :
Download or read book American Doctoral Dissertations written by and published by . This book was released on 1987 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Least-squares Variance Component Estimation by : AliReza Amiri-Simkooei
Download or read book Least-squares Variance Component Estimation written by AliReza Amiri-Simkooei and published by . This book was released on 2007 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Index to American Doctoral Dissertations by :
Download or read book Index to American Doctoral Dissertations written by and published by . This book was released on 1989 with total page 1252 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Current Index to Statistics, Applications, Methods and Theory by :
Download or read book Current Index to Statistics, Applications, Methods and Theory written by and published by . This book was released on 1996 with total page 810 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Current Index to Statistics (CIS) is a bibliographic index of publications in statistics, probability, and related fields.
Download or read book Mathematical Reviews written by and published by . This book was released on 2005 with total page 1852 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Mixed Effects Models for Complex Data by : Lang Wu
Download or read book Mixed Effects Models for Complex Data written by Lang Wu and published by CRC Press. This book was released on 2009-11-11 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.
Book Synopsis Modeling Ordered Choices by : William H. Greene
Download or read book Modeling Ordered Choices written by William H. Greene and published by Cambridge University Press. This book was released on 2010-04-08 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is increasingly common for analysts to seek out the opinions of individuals and organizations using attitudinal scales such as degree of satisfaction or importance attached to an issue. Examples include levels of obesity, seriousness of a health condition, attitudes towards service levels, opinions on products, voting intentions, and the degree of clarity of contracts. Ordered choice models provide a relevant methodology for capturing the sources of influence that explain the choice made amongst a set of ordered alternatives. The methods have evolved to a level of sophistication that can allow for heterogeneity in the threshold parameters, in the explanatory variables (through random parameters), and in the decomposition of the residual variance. This book brings together contributions in ordered choice modeling from a number of disciplines, synthesizing developments over the last fifty years, and suggests useful extensions to account for the wide range of sources of influence on choice.
Book Synopsis Methods and Applications of Longitudinal Data Analysis by : Xian Liu
Download or read book Methods and Applications of Longitudinal Data Analysis written by Xian Liu and published by Elsevier. This book was released on 2015-09-01 with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and functions including a variety of regression models, and their practical applications across many areas of research. Statistical procedures featured within the text include: - descriptive methods for delineating trends over time - linear mixed regression models with both fixed and random effects - covariance pattern models on correlated errors - generalized estimating equations - nonlinear regression models for categorical repeated measurements - techniques for analyzing longitudinal data with non-ignorable missing observations Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data. Methods and Applications of Longitudinal Data Analysis equips both graduate students and professionals to confidently apply longitudinal data analysis to their particular discipline. It also provides a valuable reference source for applied statisticians, demographers and other quantitative methodologists. - From novice to professional: this book starts with the introduction of basic models and ends with the description of some of the most advanced models in longitudinal data analysis - Enables students to select the correct statistical methods to apply to their longitudinal data and avoid the pitfalls associated with incorrect selection - Identifies the limitations of classical repeated measures models and describes newly developed techniques, along with real-world examples.
Book Synopsis Joint Models for Longitudinal and Time-to-Event Data by : Dimitris Rizopoulos
Download or read book Joint Models for Longitudinal and Time-to-Event Data written by Dimitris Rizopoulos and published by CRC Press. This book was released on 2012-06-22 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data. The content is primarily explanatory, focusing on applications of joint modeling, but sufficient mathematical details are provided to facilitate understanding of the key features of these models. All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author. All the R code used in the book is available at: http://jmr.r-forge.r-project.org/
Book Synopsis Statistical Parametric Mapping: The Analysis of Functional Brain Images by : William D. Penny
Download or read book Statistical Parametric Mapping: The Analysis of Functional Brain Images written by William D. Penny and published by Elsevier. This book was released on 2011-04-28 with total page 689 pages. Available in PDF, EPUB and Kindle. Book excerpt: In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically, Statistical Parametric Mapping provides a widely accepted conceptual framework which allows treatment of all these different modalities. This rests on an understanding of the brain's functional anatomy and the way that measured signals are caused experimentally. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. Critically, the material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. The book also serves as a companion to the software packages that have been developed for brain imaging data analysis. - An essential reference and companion for users of the SPM software - Provides a complete description of the concepts and procedures entailed by the analysis of brain images - Offers full didactic treatment of the basic mathematics behind the analysis of brain imaging data - Stands as a compendium of all the advances in neuroimaging data analysis over the past decade - Adopts an easy to understand and incremental approach that takes the reader from basic statistics to state of the art approaches such as Variational Bayes - Structured treatment of data analysis issues that links different modalities and models - Includes a series of appendices and tutorial-style chapters that makes even the most sophisticated approaches accessible
