Chapter 4. Introduction to Generalized Linear Models
Published Online: 26 MAR 2003
DOI: 10.1002/0471249688.ch4
Copyright © 2002 John Wiley & Sons, Inc.
Book Title
Categorical Data Analysis, Second Edition
Additional Information
How to Cite
Agresti, A. (2003) Introduction to Generalized Linear Models, in Categorical Data Analysis, Second Edition, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/0471249688.ch4
Publication History
- Published Online: 26 MAR 2003
- Published Print: 3 JUL 2002
Book Series:
ISBN Information
Print ISBN: 9780471360933
Online ISBN: 9780471249689
- Summary
- Chapter
Keywords:
- generalized linear models;
- logistic regression;
- Poisson regression;
- overdispersion;
- quasi likelihood;
- generalized additive models;
- maximum likelihood;
- Newton–Raphson method
Summary
Chapter 4 presents an introduction to generalized linear models. It introduces the most important models for binary data (logistic regression) and for counts (Poisson regression). It also gives a theoretical presentation of derivation of likelihood equations and methods of maximum likelihood model-fitting that apply for all models in this class. The chapter also presents extensions such as quasi likelihood and generalized additive models.