Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range of applications, including combining information over trials (meta-analysis), analysis of frailty models for survival data, genetic epidemiology, and analysis of spatial and temporal models with correlated errors.

Written by pioneering authorities in the field, this reference provides an introduction to various theories and examines likelihood inference and GLMs. The authors show how to extend the class of GLMs while retaining as much simplicity as possible. By maximizing and deriving other quantities from h-likelihood, they also demonstrate how to use a single algorithm for all members of the class, resulting in a faster algorithm as compared to existing alternatives.

Complementing theory with examples, many of which can be run by using the code supplied on the accompanying CD, this book is beneficial to statisticians and researchers involved in the above applications as well as quality-improvement experiments and missing-data analysis.

... This book provides a comprehensive summary of [the authors' past work]. However, it is much more than that, and even statisticians who do not agree with their approach to inference will find much here of interest. ... some instructors might find this to be a useful text for a course on generalized linear models. ... there are many ideas that will be useful for students to mull over ...
-A. Agresti (University of Florida), Short Book Reviews