Generalized Estimating Equations

  • Andreas Ziegler

Part of the Lecture Notes in Statistics book series (LNS, volume 204)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Andreas Ziegler
    Pages 1-10
  3. Andreas Ziegler
    Pages 11-20
  4. Andreas Ziegler
    Pages 21-28
  5. Andreas Ziegler
    Pages 29-49
  6. Andreas Ziegler
    Pages 119-131
  7. Back Matter
    Pages 133-144

About this book

Introduction

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.

Keywords

biometrics clustered data econometrics generalized estimating equations generalized linear model generalized method of moments maximum likelihood method pseudo maximum likelihood method psychometrics

Authors and affiliations

  • Andreas Ziegler
    • 1
  1. 1.Institute for Medical Biometry and Statistics, University Hospital Schleswig-HolsteinUniversity of LübeckLübeckGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-0499-6
  • Copyright Information Springer Science+Business Media, LLC 2011
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-0498-9
  • Online ISBN 978-1-4614-0499-6
  • Series Print ISSN 0930-0325
  • About this book