This is the second edition of a monograph on generalized linear models with random effects that extends the classic work of McCullagh and Nelder. It has been thoroughly updated, with around 80 pages added, including new material on the extended likelihood approach that strengthens the theoretical basis of the methodology, new developments in variable selection and multiple testing, and new examples and applications. It includes an R package for all the methods and examples that supplement the book.
"Generalized Linear Models with Random Effects is a comprehensive book on likelihood methods in generalized linear models (GLMs) including linear models with normally distributed errors. … The book is suitable for those with graduate training in mathematical statistics. The level of mathematical detail is similar to that of McCullagh and Nelder (1989), with the focus shifted towards likelihood methods. All chapters contain examples with a fair amount of detail. The book is very broad and offers a comprehensive overview of likelihood methods."
—Christiana Drake, in ISCB News, December 2018
Praise for the first edition:
"… 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
"The book is well written and replete with examples and discussions. With over 500 references, the authors have amassed an enormous amount of information in a single source."
– James W. Hardin, University of South Carolina, in Journal of the American Statistical Association, June 2009, Vol. 104, No. 486
"The book’s material is valuable . . . There are numerous examples and applications, illustrated on the accompanying Genstat CD."
– Hassan S. Bakouch, Tanta University, in Journal of Applied Statistics, September 2007, Vol. 34, No. 7
Preface to the first edition
Classical likelihood theory
Generlized linear models
Extended likelihood inferences
Normal linear mixed models
HGLMs with structured dispersion
Correlated randoms effects for HGLMs
Variable Selection and Sparsity Models
Multivariate and Missing Data Analysis