An accessible and self-contained introduction to statistical
models-now in a modernized new edition

Generalized, Linear, and Mixed Models, Second Edition
provides an up-to-date treatment of the essential techniques for
developing and applying a wide variety of statistical models. The
book presents thorough and unified coverage of the theory behind
generalized, linear, and mixed models and highlights their
similarities and differences in various construction, application,
and computational aspects.

A clear introduction to the basic ideas of fixed effects models,
random effects models, and mixed models is maintained throughout,
and each chapter illustrates how these models are applicable in a
wide array of contexts. In addition, a discussion of general
methods for the analysis of such models is presented with an
emphasis on the method of maximum likelihood for the estimation of
parameters. The authors also provide comprehensive coverage of the
latest statistical models for correlated, non-normally distributed
data. Thoroughly updated to reflect the latest developments in the
field, the Second Edition features:

* A new chapter that covers omitted covariates, incorrect random
effects distribution, correlation of covariates and random effects,
and robust variance estimation

* A new chapter that treats shared random effects models, latent
class models, and properties of models

* A revised chapter on longitudinal data, which now includes a
discussion of generalized linear models, modern advances in
longitudinal data analysis, and the use between and within
covariate decompositions

* Expanded coverage of marginal versus conditional models

* Numerous new and updated examples

With its accessible style and wealth of illustrative exercises,
Generalized, Linear, and Mixed Models, Second Edition is an
ideal book for courses on generalized linear and mixed models at
the upper-undergraduate and beginning-graduate levels. It also
serves as a valuable reference for applied statisticians,
industrial practitioners, and researchers.

Charles E. McCulloch, PhD, is Professor and Head of the
Division of Biostatistics in the School of Medicine at the
University of California, San Francisco. A Fellow of the American
Statistical Association, Dr. McCulloch is the author of numerous
published articles in the areas of longitudinal data analysis,
generalized linear mixed models, and latent class models and their
applications.

Shayle R. Searle, PhD, is Professor Emeritus in the
Department of Biological Statistics and Computational Biology at
Cornell University. Dr. Searle is the author of Linear
Models, Linear Models for Unbalanced Data, Matrix
Algebra Useful for Statistics, and Variance Components,
all published by Wiley.

John M. Neuhaus, PhD, is Professor of Biostatistics in
the School of Medicine at the University of California, San
Francisco. A Fellow of the American Statistical Association and the
Royal Statistical Society, Dr. Neuhaus has authored or coauthored
numerous journal articles on statistical methods for analyzing
correlated response data and assessments on the effects of
statistical model misspecification.

• A new chapter that covers omitted covariates, incorrect random effects distribution, correlation of covariates and random effects, and robust variance estimation
• A new chapter that treats shared random effects models, latent class models, and properties of models
• A revised chapter on longitudinal data, which now includes a discussion of generalized linear models, modern advances in longitudinal data analysis, and the use between and within covariate decompositions
• Expanded coverage of marginal versus conditional models
• Numerous new and updated examples

Preface to the First Edition.

1. Introduction.

1.1 Models.

1.2 Factors, Levels, Cells, Effects And Data.

1.3 Fixed Effects Models.

1.4 Random Effects Models.

1.5 Linear Mixed Models (Lmms).

1.6 Fixed Or Random?

1.7 Inference.

1.8 Computer Software.

1.9 Exercises.

2. One-Way Classifications.

2.1 Normality And Fixed Effects.

2.2 Normality, Random Effects And MLE.

2.3 Normality, Random Effects And REM1.

2.4 More On Random Effects And Normality.

2.5 Binary Data: Fixed Effects.

2.6 Binary Data: Random Effects.

2.7 Computing.

2.8 Exercises.

3. Single-Predictor Regression.

3.1 Introduction.

3.2 Normality: Simple Linear Regression.

3.3 Normality: A Nonlinear Model.

3.4 Transforming Versus Linking.

3.5 Random Intercepts: Balanced Data.

3.6 Random Intercepts: Unbalanced Data.

3.7 Bernoulli - Logistic Regression.

3.8 Bernoulli - Logistic With Random Intercepts.

3.9 Exercises.

4. Linear Models (LMs).

4.1 A General Model.

4.2 A Linear Model For Fixed Effects.

4.3 Mle Under Normality.

4.4 Sufficient Statistics.

4.5 Many Apparent Estimators.

4.6 Estimable Functions.

4.7 A Numerical Example.

4.8 Estimating Residual Variance.

4.9 Comments On The 1- And 2-Way Classifications.

4.10 Testing Linear Hypotheses.

4.11 T-Tests And Confidence Intervals.

4.12 Unique Estimation Using Restrictions.

4.13 Exercises.

5. Generalized Linear Models (GLMs).

5.1 Introduction.

5.2 Structure Of The Model.

5.3 Transforming Versus Linking.

5.4 Estimation By Maximum Likelihood.

5.5 Tests Of Hypotheses.

5.6 Maximum Quasi-Likelihood.

5.7 Exercises.

6. Linear Mixed Models (LMMs).

6.1 A General Model.

6.2 Attributing Structure To VAR(y).

6.3 Estimating Fixed Effects For V Known.

6.4 Estimating Fixed Effects For V Unknown.

6.5 Predicting Random Effects For V Known.

6.6 Predicting Random Effects For V Unknown.

6.7 Anova Estimation Of Variance Components.

6.8 Maximum Likelihood (Ml) Estimation.

6.9 Restricted Maximum Li…