In spite of the fact that a number of books have been published on this subject recently, this volume presents rigorous and self-contained coverage of the topic. The goal of the book is to fill the gap in the existing literature. Most of the available texts on mixed models concentrate on the discussion of basic properties of the models with a large number of examples. In contrast, this book provides in-depth mathematical coverage of mixed models' statistical properties and numerical algorithms. State-of-the-art methodologies are discussed, among them the linear mixed-effects model, the growth curve model, the generalized growth curve model, robust models, models with linear covariance structures, models for binary and count clustered data, the generalized estimation equation approach, nonlinear mixed models, and diagnostics. Special attention is given to algorithms and their implementations. Several appendices make the text self-contained. Applications include tumor regrowth and statistical analysis of shapes and images.

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