Generalized, Linear and Mixed Models

Wiley Series in Probability and Statistics
A modern perspective on mixed models
The availability of powerful computing methods in recent decades has thrust linear and nonlinear mixed models into the mainstream of statistical application. This volume offers a modern perspective on generalized, linear, and mixed models, presenting a unified and accessible treatment of the newest statistical methods for analyzing correlated, nonnormally distributed data.
As a follow-up to Searle's classic, Linear Models, and Variance Components by Searle, Casella, and McCulloch, this new work progresses from the basic one-way classification to generalized linear mixed models. A variety of statistical methods are explained and illustrated, with an emphasis on maximum likelihood and restricted maximum likelihood. An invaluable resource for applied statisticians and industrial practitioners, as well as students interested in the latest results, Generalized, Linear, and Mixed Models features:
* A review of the basics of linear models and linear mixed models
* Descriptions of models for nonnormal data, including generalized linear and nonlinear models
* Analysis and illustration of techniques for a variety of real data sets
* Information on the accommodation of longitudinal data using these models
* Coverage of the prediction of realized values of random effects
* A discussion of the impact of computing issues on mixed models

CHARLES E. MCCULLOCH, PhD, is Professor of Biostatistics at the University of California, San Francisco. He is the author of numerous scientific publications on biometrics and biological statistics and a coauthor (with Shayle Searle and George Casella) of Variance Components (Wiley).

SHAYLE R. SEARLE, PhD, is Professor Emeritus of Biometry at Cornell University. He is the author of Linear Models, Linear Models for Unbalanced Data, and Matrix Algebra Useful for Statistics, all from Wiley.