Logistic regression models / Joseph M. Hilbe

Preface.. Chapter 1 Introduction.. 1.1 The Normal Model.. 1.2 Foundation of the Binomial Model.. 1.3 Historical and Software Considerations.. 1.4 Chapter Profiles.. Chapter 2 Concepts Related to the Logistic Model.. 2.1 2×2 Table Logistic Model.. 2.2 2×k Table Logistic Model.. 2.3 Modeling a Quantitative Predictor.. 2.4 Logistic Modeling Designs..

Incluye bibliografía: páginas 613-623 e índice: páginas 625-637

Chapter 14 Other Types of Logistic-Based Models.. 14.1 Survey Logistic Models.. 14.1.1 Interpretation.. 14.2 Scobit-Skewed Logistic Regression.. 14.3 Discriminant Analysis.. 14.3.1 Dichotomous Discriminant Analysis.. 14.3.2 Canonical Linear Discriminant Analysis.. 14.3.3 Linear Logistic Discriminant Analysis.. Exercises.. Chapter 15 Exact Logistic Regression.. 15.1 Exact Methods.. 15.2 Alternative Modeling Methods.. 15.2.1 Monte Carlo Sampling Methods.. 15.2.2 Median Unbiased Estimation.. 15.2.3 Penalized Logistic Regression.. Exercises.. Conclusion.. Appendix A: Brief Guide to Using Stata Commands.. Appendix B: Stata and R Logistic Models.. Appendix C: Greek Letters and Major Functions.. Appendix D: Stata Binary Logistic Command.. Appendix E: Derivation of the Beta-Binomial.. Appendix F: Likelihood Function of the Adaptive Gauss-Hermite Quadrature Method of Estimation.. Appendix G: Data Sets.. Appendix H: Marginal Effects and Discrete Change.. References.. Author Index.. Subject Index

Logistic Regression Models presents an overview of the full range of logistic models, including binary, proportional, ordered, partially ordered, and unordered categorical response regression procedures. Other topics discussed include panel, survey, skewed, penalized, and exact logistic models. The text illustrates how to apply the various models to health, environmental, physical, and social science data. Examples illustrate successful modeling The text first provides basic terminology and concepts, before explaining the foremost methods of estimation (maximum likelihood and IRLS) appropriate for logistic models. It then presents an in-depth discussion of related terminology and examines logistic regression model development and interpretation of the results. After focusing on the construction and interpretation of various interactions, the author evaluates assumptions and goodness-of-fit tests that can be used for model assessment. He also covers binomial logistic regression, varieties of overdispersion, and a number of extensions to the basic binary and binomial logistic model. Both real and simulated data are used to explain and test the concepts involved. The appendices give an overview of marginal effects and discrete change as well as a 30-page tutorial on using Stata commands related to the examples used in the text. Stata is used for most examples while R is provided at the end of the chapters to replicate examples in the text. eng