附註:Includes bibliographical references (pages 458-473) and indexes.
Cover -- Preface to the Second Edition -- Preface to the First Edition -- Table of Contents -- 1. Introduction -- 2. Two-Dimensional Tables and Simple Logistic Regression -- 3. Three-Dimensional Tables -- 4. Logistic Regression, Logit Models, and Logistic Discrimination -- 5. Independence Relationships and Graphical Models -- 6. Model Selection Methods and Model Evaluation -- 7. Models for Factors with Quantitative Levels -- 8. Fixed and Random Zeros -- 9. Generalized Linear Models -- 10. The Matrix Approach to Log-Linear Models -- 11. The Matrix Approach to Logit Models -- 12. Maximum Likelihood Theory for Log-Linear Models -- 13. Bayesian Binomial Regression -- Appendix: Tables -- References -- Author Index.
摘要:This book examines statistical models for frequency data. The primary focus is on log-linear models for contingency tables, but in this second edition, greater emphasis has been placed on logistic regression. Topics such as logistic discrimination and generalized linear models are also explored. The treatment is designed for students with prior knowledge of analysis of variance and regression. It builds upon the relationships between these basic models for continuous data and the analogous log- linear and logistic regression models for discrete data. While emphasizing similarities between methods for discrete and continuous data, this book also carefully examines the differences in model interpretations and evaluation that occur due to the discrete nature of the data. Sample commands are given for analyses in SAS, BMFP, and GLIM. Numerous data sets from fields as diverse as engineering, education, sociology, and medicine are used to illustrate procedures and provide exercises. This book incorporates a number of innovative features. It begins with an extensive discussion of odds and odds ratios as well as concrete illustrations of the basic independence models for contingency tables. After developing a sound applied and theoretical basis for the models considered, the book presents detailed discussions of the use of graphical models and of models selection procedures. It then explores models with quantitative factors and generalized linear models, after which the fundamental results are reexamined using powerful matrix methods. Finally, the book gives an extensive treatment of Bayesian procedures for analyzing logistic regression and other regression models for binomial data. Bayesian methods are simple and, unlike.
