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媒体推荐Praise for the First Edition: These are ... well-written chapters ... . The book contains challenging problems in exercises and is suitable to be a textbook in a graduate-level course on estimating functions. The references are up-to-date and exhaustive. ... I enjoyed reading [this book] and recommend [it] very highly to the statistical community. -Journal of Statistical Computation and Simulation, February 2005 [The book] is comprehensive and covers much useful material with formulas presented in detail ... a useful and recommendable book both for those who already work with GEE methods and for newcomers to the field. -Per Kragh Andersen, University of Copenhagen, Statistics in Medicine, 2004 Generalized Estimating Equations is the first and only book to date dedicated exclusively to generalized estimating equations (GEE). I find it to be a good reference text for anyone using generalized linear models (GLIM). The authors do a good job of not only presenting the general theory of GEE models, but also giving explicit examples of various correlation structures, link functions and a comparison between population-averaged and subject-specific models. Furthermore, there are sections on the analysis of residuals, deletion diagnostics, goodness-of-fit criteria, and hypothesis testing. Good data-driven examples that give comparisons between different GEE models are provided throughout the book. Perhaps the greatest strength of this book is its completeness. It is a thorough compendium of information from the GEE literature. Overall, Generalized Estimating Equations contains a unique survey of GEE models in an attempt to unify notation and provide the most in-depth treatment of GEEs. I believe that it serves as a valuable reference for researchers, teachers, and students who study and practice GLIM methodology. -Journal of the American Statistics Association, March 2004 Generalized Estimating Equations is a good introductory book for analysing continuous and discrete data using GEE methods ... . This book is easy to read, and it assumes that the reader has some background in GLM. Many examples are drawn from biomedical studies and survey studies, and so it provides good guidance for analysing correlated data in these and other areas. -Technometrics, 2003
作者简介James W. Hardin is the Division Director of Biostatistics and an associate professor in the Department of Epidemiology and Biostatistics at the University of South Carolina. He is also an affiliated faculty in the Institute for Families in Society. Professor Hardin was the initial author of Stata's xtgee command and has authored numerous articles and software applications related to GEE and associated models. Professor Hilbe and he have authored three editions of the popular Generalized Linear Models and Extensions and co-authored Stata's current glm command. He has also co-authored (with P. Good) four editions of the well-accepted Common Errors in Statistics (and How to Avoid Them). Joseph M. Hilbe is a Solar System Ambassador with the Jet Propulsion Laboratory, an adjunct professor of statistics at Arizona State University, and an Emeritus Professor at the University of Hawaii. An elected fellow of the American Statistical Association and elected member of the International Statistical Institute (ISI), Professor Hilbe is president of the International Astrostatistics Association as well as chair of the ISI Sports Statistics and Astrostatistics committees. He has authored two editions of the bestseller Negative Binomial Regression, Logistic Regression Models, and Astrostatistical Challenges for the New Astronomy. He has also co-authored Methods of Statistical Model Estimation (with A. Robinson), Quasi-Least Squares Regression (with J. Shults), and R for Stata Users (with R. Muenchen).
目录Introduction Notational Conventions and Acronyms A Short Review of Generalized Linear Models Software Exercises Model Construction and Estimating Equations Independent Data Estimating the Variance of the Estimates Panel Data Estimation Summary Exercises R code for Selected Output Generalized Estimating Equations Population-Averaged (PA) and Subject-Specific (SS) Models The PA-GEE for GLMs The SS-GEE for GLMs The GEE2 for GLMs GEEs for Extensions of GLMs Further Developments and Applications Missing Data Choosing an Appropriate Model Summary Exercises R Code for Selected Output Residuals, Diagnostics, and Testing Criterion Measures Analysis of Residuals Deletion Diagnostics Goodness of Fit (Population-Averaged Models) Testing Coefficients in the PA-GEE Model Assessing the MCAR Assumption of PA-GEE Models Summary Exercises Programs and Datasets Programs Datasets References Author Index Subject Index
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