Mixed models : theory and applications with R / Eugene Demidenko.

"Mixed modeling is one of the most promising and exciting areas of statistical analysis, enabling the analysis of nontraditional, clustered data that may come in the form of shapes or images. This book provides in-depth mathematical coverage of mixed models' statistical properties and numerical algorithms, as well as applications such as the analysis of tumor regrowth, shape, and image. The new edition includes significant updating, over 300 exercises, stimulating chapter projects and model simulations, inclusion of R subroutines, and a revised text format. The target audience continues to be graduate students and researchers. An author-maintained web site is available with solutions to exercises and a compendium of relevant data sets"-- Provided by publisher.

Includes bibliographical references and index.

Print version record and CIP data provided by publisher.

Preface -- Preface to the Second Edition -- R software and functions -- Data Sets -- Open Problems in Mixed Models -- 1. Introduction: Why Mixed Models? -- 2. MLE for the LME Model -- 3. Statistical Properties of the LME Model -- 4. Growth Curve Model and Generalizations -- 5. Meta-analysis Model -- 6. Nonlinear Marginal Model -- 7. Generalized Linear Mixed Models -- 8. Nonlinear Mixed Effects Model -- 9. Diagnostics and Influence Analysis -- 10. Tumor Regrowth Curves -- 11. Statistical Analysis of Shape -- 12. Statistical Image Analysis -- 13. Appendix: Useful Facts and Formulas.