Multivariate Generalized Linear Mixed Models Using R - Couverture rigide

Berridge, Damon Mark; Crouchley, Robert

Couverture rigide

ISBN 10 : 1439813264 ISBN 13 : 9781439813263

Editeur : CRC Press, 2011

Synopsis

Multivariate Generalized Linear Mixed Models Using R 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). Full description

A propos du livre

Présentation de l'éditeur

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.

Biographie de l'auteur

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.

Détails de l'édition

• Éditeur: CRC Press
• Date d'édition: 2011
• ISBN 10: 1439813264
• ISBN 13: 9781439813263
• Reliure: Relié
• Langue: anglais
• Nombre de pages: 304