Loglinear Models And Logistic Regression (Springer Texts In Statistics),Used

As The New Title Indicates, This Second Edition Of Loglinear 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 14. Intermediate Topics Are Presented In Chapters 5 Through 8. Generalized Linear Models Are Presented In Ch Ter 9. The Matrix Approach To Loglinear Models And Logistic Regression Is Presented In Chapters 1012, 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 Loglog 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).