Multivariate Generalized Linear Mixed Models Using R

Name: Multivariate Generalized Linear Mixed Models Using R
Brand: Taylor & Francis Ltd
Price: 88.75 AUD
Availability: BackOrder

By: Damon Mark Berridge, Robert Crouchley

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Paperback | 14 October 2024 | Edition Number 1

At a Glance

Paperback
306 Pages

Dimensions(cm)
23.4 x 15.6 x 1.63

Paperback

RRP $98.99

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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.

Industry Reviews

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

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