Although powerful and flexible, the method of generalized linear models (GLM) is limited in its ability to accurately deal with longitudinal and clustered data. Developed specifically to accommodate these data types, the method of Generalized Estimating Equations (GEE) extends the GLM algorithm to accommodate the correlated data encountered in health research, social science, biology, and other related fields.
Generalized Estimating Equations provides the first complete treatment of GEE methodology in all of its variations. After introducing the subject and reviewing GLM, the authors examine the different varieties of generalized estimating equations and compare them with other methods, such as fixed and random effects models. The treatment then moves to residual analysis and goodness of fit, demonstrating many of the graphical and statistical techniques applicable to GEE analysis.
With its careful balance of origins, applications, relationships, and interpretation, this book offers a unique opportunity to gain a full understanding of GEE methods, from their foundations to their implementation. While equally valuable to theorists, it includes the mathematical and algorithmic detail researchers need to put GEE into practice.
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These are ... well written chapters ... . The book contains challenging problems in exercises and is suitable to be a text book 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, Vol. 75, No. 2, Feb. 2005
[The book] is comprehensive and covers much useful material with formulas presented in detail...this is a useful and recommendable book both for those who already work with GEE methods and for newcomers to the field.
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.
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Book Description Chapman and Hall/CRC, 2002. Hardcover. Book Condition: New. Never used!. Bookseller Inventory # P111584883073
Book Description Chapman and Hall/CRC, 2002. Hardcover. Book Condition: New. Brand New!. Bookseller Inventory # VIB1584883073