Generalized Estimating Equations - Ziegler, Andreas; SpringerLink (Online service) (New York, NY : Springer New York, 2011)
Generalized estimating equations have become increasingly popular in biometrical, econometrical, and psychometrical applications because they overcome the classical assumptions of statistics, i.e. independence and normality, which are too restrictive for many problems. Therefore, the main goal of this book is to give a systematic presentation of the original generalized estimating equations (GEE) and some of its further developments. Subsequently, the emphasis is put on the unification of various GEE approaches. This is done by the use of two different estimation techniques, the pseudo maximum likelihood (PML) method and the generalized method of moments (GMM). The author details the statistical foundation of the GEE approach using more general estimation techniques. The book could therefore be used as basis for a course to graduate students in statistics, biostatistics, or econometrics, and will be useful to practitioners in the same fields.
The linear exponential family -- The quadratic exponential family -- Generalized linear models -- Maximum likelihood method -- Quasi maximum likelihood method -- Pseudo maximum likelihood method based on the linear exponential family -- Quasi generalized pseudo maximum likelihood method based on the linear exponential family -- Algorithms for solving the generalized estimating equations and the relation to the jack-knife estimator of variance -- Pseudo maximum likelihood estimation based on the quadratic exponential family -- Generalized method of moment estimation.
Keywords: Statistics; Mathematical statistics; Statistics; Statistical Theory and Methods