Models for Discrete Longitudinal Data by Geert Molenberghs

Models for Discrete Longitudinal Data by Geert Molenberghs

Introduction.- Motivating Studies.- Generalized Linear Models.- Linear Mixed Models for Gaussian Longitudinal Data.- Model Families.- The Strength of Marginal Models.- Likelihood-based Models.- Generalized Estimating Equations.- Pseudo-likelihood.- Fitting Marginal Models with SAS.- Conditional Models.- Pseudo-likehood.- From Subject-Specific to Random-Effects Models.- Generalized Linear Mixed Models (GLMM).- Fitting Generalized Linear Mixed Models with SAS.- Marginal Versus Random-Effects Models.- Ordinal Data.- The Epilepsy Data.- Non-linear Models.- Psuedo-likelihood for a Hierarchical Model.- Random-effects Models with Serial Correlation.- Non-Gaussian Random Effects.- Joint Continuous and Discrete Responses.- High-dimensional Multivariate Repeated Measurements.- Missing Data Concepts.- Simple Methods, Direct Likelikhood and WGEE.- Multiple Imputation and the Expectation-Maximization Algorithm.- Selection Models.- Pattern-mixture Models.- Sensitivity Analysis.- Incomplete Data and SAS.

From the reviews:

"Strengths of this book include its breadth of topics, excellent organization and clarity of writing..I highly recommend this book to my colleagues and students." -Justine Shults for the Journal of Biopharmaceutical Statistics, Issue 3, 2006

"Models for Discrete Longitudinal Data is an excellent choice for any statistician with an interest in analyzing discrete longitudinal data. It covers all of the theoretical and applied aspects in this area and is organized in such a way to serve as a handy reference guide for applied statisticians, especially those in biomedical fields. I learned a great deal from this book, and I recommend it highly to others." -John Williamson for the Journal of the American Statistical Association, September 2006

"This book complements Verbeke and Molenberghs (2000), which focused on models based on the multivariate normal distribution. … This book covers the alternative models and approaches in a methodical and accessible manner. The emphasis in the book is on presenting methods for solving practical problems, and the authors succeed admirably in this. … The material is clearly presented … . This book is very welcome, and will undoubtedly prove to be useful and influential." (B. J. T. Morgan, Short Book Reviews, Vol. 26 (2), 2006)

"This book provides a comprehensive treatment of modeling approaches for non-Gaussian repeated measures … . the book shows how the different approaches can be implemented within the SAS software package. The text is so organized that the reader can skip the software-oriented chapters and sections without breaking the logical flow. … It is a very important, modern and useful book for statisticians." (T. Postelnicu, Zentralblatt MATH, Vol. 1093 (19), 2006)

"This book … concentrates on models for non-normally distributed longitudinal data, like binary or categorical data. … The book under review is a comprehensivecollection of latest models for non-normally distributed longitudinal data. … Models for Discrete Longitudinal Data addresses interested (and experienced) students and lectures as well as practitioners looking for solutions of everyday problems." (K. Webel, Advances in Statistical Analysis, Vol. 91 (2), 2007)

SKU	Unavailable
ISBN 13	9781441920430
ISBN 10	1441920439
Title	Models for Discrete Longitudinal Data
Author	Geert Molenberghs
Series	Springer Series In Statistics
Condition	Unavailable
Binding Type	Paperback
Publisher	Springer-Verlag New York Inc.
Year published	2010-12-01
Number of pages	687
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.