| Log-Linear Models and Logistic Regression 1997 Edition Contributor(s): Christensen, Ronald (Author) |
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ISBN: 0387982477 ISBN-13: 9780387982472 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover Published: September 1997 Annotation: 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 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 aresimple and, unlike |
| Additional Information |
| BISAC Categories: - Mathematics | Probability & Statistics - Multivariate Analysis |
| Dewey: 519.535 |
| LCCN: 97012465 |
| Series: Springer Texts in Statistics |
| Physical Information: 1.28" H x 6.48" W x 9.52" (1.80 lbs) 484 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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) |