Log-Linear Models and Logistic Regression

1. Front Matter

   Pages i-xv

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2. Introduction

   Pages 1-22

3. Two-Dimensional Tables and Simple Logistic Regression

   Pages 23-68

4. Three-Dimensional Tables

   Pages 69-115

5. Logistic Regression, Logit Models, and Logistic Discrimination

   Pages 116-177

6. Independence Relationships and Graphical Models

   Pages 178-210

7. Model Selection Methods and Model Evaluation

   Pages 211-257

8. Models for Factors with Quantitative Levels

   Pages 258-278

9. Fixed and Random Zeros

   Pages 279-296

10. Generalized Linear Models

    Pages 297-313

11. The Matrix Approach to Log-Linear Models

    Pages 314-362

12. The Matrix Approach to Logit Models

    Pages 363-395

13. Maximum Likelihood Theory for Log-Linear Models

    Pages 396-421

14. Bayesian Binomial Regression

    Pages 422-454

15. Back Matter

    Pages 455-484

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As the new title indicates, this second edition of Log-Linear Models has been modi?ed to place greater emphasis on logistic regression. In addition to new material, the book has been radically rearranged. The fundamental material is contained in Chapters 1-4. Intermediate topics are presented in Chapters 5 through 8. Generalized linear models are presented in Ch- ter 9. The matrix approach to log-linear models and logistic regression is presented in Chapters 10-12, with Chapters 10 and 11 at the applied Ph.D. level and Chapter 12 doing theory at the Ph.D. level. The largest single addition to the book is Chapter 13 on Bayesian bi- mial regression. This chapter includes not only logistic regression but also probit and complementary log-log regression. With the simplicity of the Bayesian approach and the ability to do (almost) exact small sample s- tistical inference, I personally ?nd it hard to justify doing traditional large sample inferences. (Another possibility is to do exact conditional inference, but that is another story.) Naturally,Ihavecleaneduptheminor?awsinthetextthatIhavefound. All examples, theorems, proofs, lemmas, etc. are numbered consecutively within each section with no distinctions between them, thus Example 2.3.1 willcomebeforeProposition2.3.2.Exercisesthatdonotappearinasection at the end have a separate numbering scheme. Within the section in which it appears, an equation is numbered with a single value, e.g., equation (1).

• Generalized linear model
• Likelihood
• Logistic Regression
• SAS
• Variance
• analysis of variance
• binomial

• Department of Mathematics and Statistics, University of New Mexico, Albuquerque, USA

  Ronald Christensen

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