Multivariate Generalized Linear Mixed Models Using R by Robert Crouchley (Englis

Description: Multivariate Generalized Linear Mixed Models Using R by Robert Crouchley, Damon Mark Berridge To provide researchers with the ability to analyze large and complex data sets using robust models, this book presents a unified framework for a broad class of models that can be applied using a dedicated R package (Sabre). It includes chapters that cover the analysis of multilevel models using univariate generalized linear mixed models (GLMMs). FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. A Unified Framework for a Broad Class of Models The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model estimation, and endogenous variables, along with SabreR commands and examples.Improve Your Longitudinal Study In medical and social science research, MGLMMs help disentangle state dependence from incidental parameters. Focusing on these sophisticated data analysis techniques, this book explains the statistical theory and modeling involved in longitudinal studies. Many examples throughout the text illustrate the analysis of real-world data sets. Exercises, solutions, and other material are available on a supporting website. Author Biography Damon M. Berridge is a senior lecturer in the Department of Mathematics and Statistics at Lancaster University. Dr. Berridge has nearly 20 years of experience as a statistical consultant. His research focuses on the modeling of binary and ordinal recurrent events through random effects models, with application in medical and social statistics. Robert Crouchley is a professor of applied statistics and director of the Centre for e-Science at Lancaster University. His research interests involve the development of statistical methods and software for causal inference in nonexperimental data. These methods include models for errors in variables, missing data, heterogeneity, state dependence, nonstationarity, event history data, and selection effects. Table of Contents Introduction. Generalized Linear Models for Continuous/Interval Scale Data. Generalized Linear Models for Other Types of Data. Family of Generalized Linear Models. Mixed Models for Continuous/Interval Scale Data. Mixed Models for Binary Data. Mixed Models for Ordinal Data. Mixed Models for Count Data. Family of Two-Level Generalized Linear Models. Three-Level Generalized Linear Models. Models for Multivariate Data. Models for Duration and Event History Data. Stayers, Non-Susceptibles, and Endpoints. Handling Initial Conditions/State Dependence in Binary Data. Incidental Parameters: An Empirical Comparison of Fixed Effects and Random Effects Models. Appendices. Bibliography. Review I think this is a very well organised and written book and therefore I highly recommend it not only to professionals and students but also to applied researchers from many research areas such as education, psychology and economics working on complex and large data sets.—Sebnem Er, Journal of Applied Statistics, 2012 Review Quote I think this is a very well organised and written book and therefore I highly recommend it not only to professionals and students but also to applied researchers from many research areas such as education, psychology and economics working on complex and large data sets. -Sebnem Er, Journal of Applied Statistics, 2012 Details ISBN1439813264 Author Damon Mark Berridge Short Title MULTIVARIATE GENERALIZED LINEA Language English ISBN-10 1439813264 ISBN-13 9781439813263 Media Book Format Hardcover Year 2011 Imprint CRC Press Inc Place of Publication Bosa Roca Country of Publication United States Affiliation Lancaster University, UK Replaced by 9781498740654 AU Release Date 2011-04-25 NZ Release Date 2011-04-25 US Release Date 2011-04-25 Publication Date 2011-04-25 UK Release Date 2011-04-25 Pages 304 Publisher Taylor & Francis Inc Alternative 9780367221409 DEWEY 519.53 Illustrations 9 Tables, black and white; 18 Illustrations, black and white Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. 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