1. Introduction: Why Mixed Models?
2. MLE for 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.
"…an excellent book and it thoroughly covers new developments in mixed models in addition to the classical mixed model approaches." (Biometrics, March 2006)
"Statisticians would like very much to read this book." (Journal of Statistical Computation and Simulation, January 2006)
"…I recommend this book and congratulate the author for his dedication…" (Annals of Biomedical Engineering, October 2005)
"…has a wealth of information. I recommend this book to anyone working in mixed models." (Journal of Biopharmaceutical Statistics, July/August 2005)
"…a very welcome addition and…a good companion to other mixed models texts…" (Statistical Methods in Medical Research, Vol. 14, 2005)
"…intended for professionals and students in a broad range of fields such as cancer research, computer science, engineering and industry." (Zentralblatt Math, Vol.1055, No.06, 2005)
"The book is useful for statisticians who are interested in mathematical statistics and those who are interested in applications." (Mathematical Reviews, 2005e)
"Written with the statistician/mathematician in mind, the computer engineer will find the content also useful." (E-STREAMS, February 2005)