Logistic Regression Models

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 modelingThe 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. Apply the models to your own dataData files for examples and questions used in the text as well as code for user-authored commands are provided on the book’s website, formatted in Stata, R, Excel, SAS, SPSS, and Limdep. See Professor Hilbe discuss the book.

PrefaceIntroductionThe Normal Model Foundation of the Binomial Model Historical and Software Considerations Chapter Profiles Concepts Related to the Logistic Model 2 × 2 Table Logistic Model 2 × k Table Logistic ModelModeling a Quantitative Predictor Logistic Modeling DesignsEstimation Methods Derivation of the IRLS Algorithm IRLS EstimationMaximum Likelihood EstimationDerivation of the Binary Logistic Algorithm Terms of the Algorithm Logistic GLM and ML Algorithms Other Bernoulli ModelsModel Development Building a Logistic ModelAssessing Model Fit: Link SpecificationStandardized Coefficients Standard ErrorsOdds Ratios as Approximations of Risk RatiosScaling of Standard Errors Robust Variance Estimators Bootstrapped and Jackknifed Standard Errors Stepwise Methods Handling Missing Values Modeling an Uncertain Response Constraining CoefficientsInteractionsIntroduction Binary X Binary Interactions Binary X Categorical Interactions Binary X Continuous InteractionsCategorical X Continuous InteractionThoughts about InteractionsAnalysis of Model Fit Traditional Fit Tests for Logistic Regression Hosmer–Lemeshow GOF Test Information Criteria TestsResidual AnalysisValidation ModelsBinomial Logistic Regression Overdispersion Introduction The Nature and Scope of Overdispersion Binomial OverdispersionBinary Overdispersion Real Overdispersion Concluding RemarksOrdered Logistic Regression Introduction The Proportional Odds Model Generalized Ordinal Logistic Regression Partial Proportional OddsMultinomial Logistic Regression Unordered Logistic RegressionIndependence of Irrelevant Alternatives Comparison to Multinomial ProbitAlternative Categorical Response Models Introduction Continuation Ratio Models Stereotype Logistic Model Heterogeneous Choice Logistic Model Adjacent Category Logistic Model Proportional Slopes ModelsPanel Models Introduction Generalized Estimating EquationsUnconditional Fixed Effects Logistic Model Conditional Logistic Models Random Effects and Mixed Models Logistic RegressionOther Types of Logistic-Based ModelsSurvey Logistic Models Scobit-Skewed Logistic Regression Discriminant AnalysisExact Logistic Regression Exact Methods Alternative Modeling MethodsConclusion 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 IndexExercises and R Code appear at the end of most chapters.

Hilbe, Joseph M.