Logistic regression models

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Features

• Examines the theoretical foundation of many logistic models, including binary, ordered, multinomial, panel, and exact
• Describes how each type of model is established, interpreted, and evaluated as to its goodness of fit
• Analyzes the models using Stata
• Offers R code at the end of most chapters to enable R users to duplicate the output displayed in the text
• Includes numerous exercises and real-world examples from the medical, ecological, physical, and social sciences
• Provides the example data sets online in Stata, R, Excel, SAS, SPSS, and Limdep formats
  

Table of Contents

Preface
Introduction
The Normal Model  
Binomial Model Foundation of the  
Historical and Software Considerations  
Chapter Profiles  Concepts Related to the Logistic Model 2 × 2 Table Logistic Model  2 ×  k Table Logistic Model Modeling a Quantitative Predictor  Designs Logistic Modeling Estimation Methods Derivation of the IRLS Algorithm  IRLS Estimation Maximum Likelihood Estimation Algorithm Derivation of the Binary Logistic 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  Interactions Binary X Continuous 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 Logistic Regression Multinomial Logistic Regression Unordered Independence of Irrelevant Alternatives  Comparison to Multinomial Probit Categorical Alternative Response Models Introduction  Models Continuation Ratio  Logistic Model Stereotype  Choice Heterogeneous Logistic Model  Adjacent Category Logistic Model  Proportional Slopes Models Panel Models Introduction  Generalized Estimating Equations Unconditional Logistic Model Fixed Effects  Models Conditional Logistic  and Mixed Models Random Effects Logistic regression Other Types of Logistic Models-Based Survey Logistic Models  Scobit-Skewed Logistic Regression  Analysis Discriminant Logistic Regression Exact Exact Methods  Alternative Methods Modeling 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 Gauss-Hermite Quadrature Adaptive Estimation Method of  Appendix G: Data Sets  Appendix H: Marginal Effects and Discrete Change  References

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关键词：regression regressio logistic regress ogistic foundation exercises including examples physical

Thanks for sharing! This is a great book. It's the first time to see an electronic version. Strongly recommended.

这书的作者JOSEPH HILBE是Stata软件的创作者之一。很有名。logistic 和 glm 程序是他写的。好书一本。建议加精。

顶一下

非常感谢！

好书。

Thanks for sharing