Hilbe Logistic Regression Models

1. Auflage 2009
ISBN: 978-1-4200-7577-9
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 656 Seiten

Reihe: Chapman & Hall/CRC Texts in Statistical Science

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.

Zielgruppe

Graduate and senior undergraduate students.

Autoren/Hrsg.

Hilbe, Joseph M.

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Preface
Introduction
The 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 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.