Logistic Regression Models

By (author) Hilbe, Joseph M.

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. Apply the models to your own data Data 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.

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[目次]

  • Preface Introduction The Normal Model Foundation of the Binomial Model Historical and Software Considerations Chapter Profiles Concepts Related to the Logistic Model 2 x 2 Table Logistic Model 2 x k Table Logistic Model Modeling a Quantitative Predictor Logistic Modeling Designs Estimation Methods Derivation of the IRLS Algorithm IRLS Estimation Maximum Likelihood Estimation Derivation of the Binary Logistic Algorithm Terms of the Algorithm Logistic GLM and ML Algorithms Other Bernoulli Models Model Development Building a Logistic Model Assessing Model Fit: Link Specification Standardized Coefficients Standard Errors Odds Ratios as Approximations of Risk Ratios Scaling of Standard Errors Robust Variance Estimators Bootstrapped and Jackknifed Standard Errors Stepwise Methods Handling Missing Values Modeling an Uncertain Response Constraining Coefficients Interactions Introduction Binary X Binary Interactions Binary X Categorical Interactions Binary X Continuous Interactions Categorical X Continuous Interaction Thoughts about Interactions Analysis of Model Fit Traditional Fit Tests for Logistic Regression Hosmer-Lemeshow GOF Test Information Criteria Tests Residual Analysis Validation Models Binomial Logistic Regression Overdispersion Introduction The Nature and Scope of Overdispersion Binomial Overdispersion Binary Overdispersion Real Overdispersion Concluding Remarks Ordered Logistic Regression Introduction The Proportional Odds Model Generalized Ordinal Logistic Regression Partial Proportional Odds Multinomial Logistic Regression Unordered Logistic Regression Independence of Irrelevant Alternatives Comparison to Multinomial Probit Alternative Categorical Response Models Introduction Continuation Ratio Models Stereotype Logistic Model Heterogeneous Choice Logistic Model Adjacent Category Logistic Model Proportional Slopes Models Panel Models Introduction Generalized Estimating Equations Unconditional Fixed Effects Logistic Model Conditional Logistic Models Random Effects and Mixed Models Logistic Regression Other Types of Logistic-Based Models Survey Logistic Models Scobit-Skewed Logistic Regression Discriminant Analysis Exact Logistic Regression Exact Methods Alternative Modeling Methods 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 Exercises and R Code appear at the end of most chapters.

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この本の情報

書名 Logistic Regression Models
著作者等 Hilbe, Joseph M.
シリーズ名 Chapman & Hall/CRC Texts in Statistical Science
出版元 Chapman & Hall/CRC
刊行年月 2011.03.23
ページ数 656p
ISBN 9781420075779
言語 英語
出版国 アメリカ合衆国
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