Multivariate Generalized Linear Mixed Models Using R

Damon Mark Berridge (u. a.)

ISBN 10: 1439813264 / ISBN 13: 9781439813263

Published by CRC Press, 2011

About this Item

Multivariate Generalized Linear Mixed Models Using R | Damon Mark Berridge (u. a.) | Buch | Englisch | 2011 | CRC Press | EAN 9781439813263 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 128523034

Bibliographic Details

• Title: Multivariate Generalized Linear Mixed Models Using R
• Publisher: CRC Press
• Publication Date: 2011
• Language: English
• ISBN 10: 1439813264
• ISBN 13: 9781439813263
• Binding: Buch
• Condition: Neu
• Dimensions: 240W x 161H x 21D millimeters 240 millimeters width by 161 millimeters height by 21 millimeters depth
• Weight: 625 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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 StudyIn 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.

About the Author

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.