Generalized Linear and Nonlinear Models for Correlated Data

Additional Book Details

Edward Vonesh's Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS is devoted to the analysis of correlated response data using SAS, with special emphasis on applications that require the use of generalized linear models or generalized nonlinear models.

Written in a clear, easy-to-understand manner, it provides applied statisticians with the necessary theory, tools, and understanding to conduct complex analyses of continuous and/or discrete correlated data in a longitudinal or clustered data setting. Using numerous and complex examples, the book emphasizes real-world applications where the underlying model requires a nonlinear rather than linear formulation and compares and contrasts the various estimation techniques for both marginal and mixed-effects models. The SAS procedures MIXED, GENMOD, GLIMMIX, and NLMIXED as well as user-specified macros will be used extensively in these applications. In addition, the book provides detailed software code with most examples so that readers can begin applying the various techniques immediately.

Book Review

"Dr. Vonesh has written a clear and comprehensive treatise on the analysis of correlated data using SAS. In particular, he describes generalized linear and nonlinear models that address four types of correlated data encountered in statistical practice: repeated measurements including longitudinal data, clustered data, spatially correlated data, and multivariate data.

This book will be extremely valuable to any practitioner who analyzes correlated data sets. Graduate programs in biostatistics and other branches of applied statistics should consider the adoption of this book on its own, or as a supplement, for an advanced course in statistical methods."

Vernon M. Chinchilli

Penn State

Hershey College of Medicine

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