Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS

by Edward F. Vonesh

Overview

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.

This book is part of the SAS Press program.

Product Details

ISBN-13:	9781629592305
Publisher:	SAS Institute
Publication date:	07/07/2014
Sold by:	Barnes & Noble
Format:	eBook
Pages:	552
File size:	8 MB

About the Author

Edward F. Vonesh, Ph.D., is self-employed as managing member of Vonesh Statistical Consulting, LLC, as well as a part-time employee of Northwestern University, where he supports research in his capacity as professor in the Department of Preventive Medicine. The author of numerous professional papers, he has published in the Journal of the American Statistical Association, Biometrika, Biometrics, and Statistics in Medicine. After twenty-nine years at Baxter Healthcare, Dr. Vonesh recently retired from his position as a senior Baxter research scientist. A SAS user since 1974, he received his B.S. and M.S. from Northern Illinois University and his Ph.D. in biostatistics from the University of Michigan and is a Fellow of the American Statistical Association.