Overdispersion Models in SAS

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SAS Institute, Feb 1, 2012 - Mathematics - 406 pages
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Overdispersion Models in SAS provides a friendly methodology-based introduction to the ubiquitous phenomenon of overdispersion. A basic yet rigorous introduction to the several different overdispersion models, an effective omnibus test for model adequacy, and fully functioning commented SAS codes are given for numerous examples. The examples, many of which use the GLIMMIX, GENMOD, and NLMIXED procedures, cover a variety of fields of application, including pharmaceutical, health care, and consumer products. The book is ideal as a textbook for an M.S.-level introductory course on estimation methods for overdispersion and generalized linear models as well as a first reading for students interested in pursuing this fertile area of research for further study. Topics covered include quasi-likelihood models; likelihood overdispersion binomial, Poisson, and multinomial models; generalized overdispersion linear models (GLOM); goodness-of-fit for overdispersion binomial models; Kappa statistics; marginal and conditional models; generalized estimating equations (GEE); ratio estimation; small sample bias correction of GEE; generalized linear mixed models (GLMM); and generalized linear overdispersion mixed models (GLOMM).

This book is part of the SAS Press program.

 

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Contents

Chapter 2 Generalized Linear Models
15
Chapter 3 Quasilikelihood Functions
51
Chapter 4 Likelihood Models for Overdispersed Binomial Responses
77
Chapter 5 Goodnessoffit Tests for Overdispersed Binomial Models
115
Chapter 6 Likelihood Models for Overdispersed Count Responses
159
Chapter 7 Likelihood Models for Overdispersed Multinomial Responses
201
Chapter 8 A Twostage Maximum Likelihood Estimation Procedure
261
Chapter 9 Generalized Estimating Equations Techniques for Marginal Models
283
Chapter 10 Generalized Linear Overdispersion Mixed Models
337
End of Journey
375
List of Abbreviations
377
References
379
Index
387
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About the author (2012)

Jorge G. Morel, Ph.D., is principal statistician for Procter and Gamble, where he specializes in SAS/STAT and SAS/IML. A SAS user since 1981, he received his Ph.D. in statistics from Iowa State University and holds two M.S. degrees in the same field. He is a member of the American Statistical Association, and he has been published widely in the Journal of Biopharmaceutical Statistics, Biometrical Journal, and Applied Statistics, among other publications.

Nagaraj K. Neerchal, Ph.D., is Professor of Statistics and Chair of the Department of Mathematics and Statistics at the University of Maryland, Baltimore County. A SAS user since 1982, he specializes in SAS/STAT and SAS/ETS. Coauthor of more than a dozen journal articles, he received his B.S. and M.S. from the Indian Statistical Institute and his Ph.D. from Iowa State University. Dr. Neerchal received the Distinguished Achievement Award and Medal from the American Statistical Association's Section on Statistics and the Environment in 2000.

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