Introduction

  • Ronald Christensen
Part of the Springer Texts in Statistics book series (STS)

Abstract

This book is concerned with the analysis of cross-classified categorical data using log-linear models. Log-linear models have two great advantages: they are flexible and they are interpretable. Log-linear models have all the modeling flexibility that is associated with analysis of variance and regression. They also have natural interpretations in terms of odds and frequently have interpretations in terms of independence. Unlike many books on log-linear models, this book also examines the important special cases known as logistic regression and logistic discrimination. There is a wide literature on log-linear models and a number of books have been written on the subject. Some additional references that I can recommend are: Agresti (1984), Anderson (1980), Bishop, Fienberg, and Holland (1975), Everitt (1977), Fienberg (1980), Haberman (1974), Plackett (1981), Read and Cressie (1988), and Santner and Duffy (1989). Cox and Snell (1989) and Hosmer and Lemeshow (1989) have written books on logistic regression. One reason I can recommend these is that they are all quite different from each other and from this book. There are differences in level, emphasis, and approach. This is by no means an exhaustive list; other good books are available.

Keywords

Conditional Probability Multinomial Distribution Hair Color Political Affiliation Blue Ball 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1990

Authors and Affiliations

  • Ronald Christensen
    • 1
  1. 1.Department of Mathematics and StatisticsUniversity of New MexicoAlbuquerqueUSA

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