Logistic regression models

Select an Action

Title:

Logistic regression models

Author:

Hilbe, Joseph M., 1944-

ISBN:

9781420075755

Personal Author:

Hilbe, Joseph M., 1944-

Publication Information:

Boca Raton : CRC Press, c2009.

Physical Description:

xviii, 637 p. : ill. ; 25 cm.

Series:

Chapman & Hall/CRC texts in statistical science series.

Texts in statistical science

Texts in statistical science.

General Note:

"A Chapman & Hall book."

Contents:

Concepts Related to the Logistic Model -- Estimation Methods -- Derivation of the Binary Logistic Algorithm -- Model Development -- Interactions -- Analysis of Model Fit -- Binomial Logistic Regression -- Overdispersion -- Ordered Logistic Regression -- Multinomial Logistic Regression -- Alternative Categorical Response Models -- Panel Models -- Other Types of Logistic-Based Models -- Exact Logistic Regression -- 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.

Subject Term:

Logistic regression analysis -- Data processing.

Copies:

Available:*

Library	Material Type	Item Barcode	Shelf Number	Copy	Status
Searching... 1:IIEMSA	1:GEN-BOOK	33168025537792	519.536 H641L 2009	1	Searching... Unknown

Bound With These Titles

On Order

Summary

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.