Generalized, Linear and Mixed Models

Wiley Series in Probability and Statistics offers 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, non-normally 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 non-normal 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; and, a discussion of the impact of computing issues on mixed models.

Table of Contents:
Preface. Introduction. One--Way Classifications. Single--Predictor Regression. Linear Models (LMs). Generalized Linear Models (GLMs). Linear Mixed Models (LMMs). Longitudinal Data. GLMMs. Prediction. Computing. Nonlinear Models. Appendix M: Some Matrix Results. Appendix S: Some Statistical Results. References. Index.