Generalized Estimating Equations

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.

Chapter 7 Pseudo maximum likelihood estimation based on the quadratic exponential family.

Generalized Estimating Equations; Preface; Organization of the Book; Acknowledgments; Contents; Chapter 1 The linear exponential family; 1.1 Definition; 1.2 Moments; 1.3 Parameterization in the mean; 1.4 Selected properties; 1.5 Examples for univariate linear exponential families; 1.6 Examples for multivariate linear exponential families; 1.7 Relationship to the parameterization in univariategeneralized linear models; Chapter 2 The quadratic exponential family; 2.1 Definition; 2.2 Selected properties; 2.3 Examples for quadratic exponential families.

2.4 The joint distribution of dichotomous random variables2.4.1 The joint distribution of two dichotomous random variables; 2.4.2 The joint distribution of T dichotomous random variables; 2.4.3 Restriction of the parameter space in marginal models; Chapter 3 Generalized linear models; 3.1 Univariate generalized linear models; 3.1.1 Definition; 3.1.2 Parameterization and natural link function; 3.1.3 Examples; 3.1.4 Threshold model for dichotomous dependent data; 3.2 Multivariate generalized linear models; 3.2.1 Definition; 3.2.2 Examples; Chapter 4 Maximum likelihood method; 4.1 Definition.

4.2 Asymptotic properties4.3 Transformations…