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Logistic Regression Models

Joseph M. Hilbe
€210.80
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A01=Joseph M. Hilbe
advanced categorical data modeling applications
AIC Statistic
Author_Joseph M. Hilbe
BIC Statistic
binary logistic algorithm
Binomial Logistic Models
binomial response modeling
Category=PBT
Category=PS
Con Dence Interval
Continuous Predictor
Covariate Pattern
De Ned
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exact inference techniques
Exact Logistic Regression
Excel
Full Maximum Likelihood Method
Gee Model
Generalized Linear Models
Likelihood Ratio Test LR Chi2
Log Likelihood
Logistic Discriminant Analysis
Logistic Model
logistic regression models
Logistic Regression Number
LOGIT Model
LR Chi2
Multinomial Logistic Regression
multinomial regression analysis
overdispersion
overdispersion correction
Parallel Lines Assumption
Parallel Regression Assumption
penalized estimation methods
Proportional Odds Model
Pseudo R2
R code
Random Effects Logistic Models
residual analysis
SAS
Single Parameter Exponential Family
standardized coefficients
survey data statistics

Product details

  • ISBN 9781420075755
  • Weight: 1370g
  • Dimensions: 156 x 234mm
  • Publication Date: 11 May 2009
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

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.
About Joseph M. Hilbe
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA and Arizona State University, Tempe, AZ

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