
Generalized Linear Models and Extensions
Fourth Edition
Stata Press
4th Edition
Published on 27. April 2018
Book
Paperback/Softback
598 pages
978-1-59718-225-6 (ISBN)
Description
Generalized linear models (GLMs) extend linear regression to models with a non-Gaussian, or even discrete, response. GLM theory is predicated on the exponential family of distributions-a class so rich that it includes the commonly used logit, probit, and Poisson models. Although one can fit these models in Stata by using specialized commands (for example, logit for logit models), fitting them as GLMs with Stata's glm command offers some advantages. For example, model diagnostics may be calculated and interpreted similarly regardless of the assumed distribution.
This text thoroughly covers GLMs, both theoretically and computationally, with an emphasis on Stata. The theory consists of showing how the various GLMs are special cases of the exponential family, showing general properties of this family of distributions, and showing the derivation of maximum likelihood (ML) estimators and standard errors. Hardin and Hilbe show how iteratively reweighted least squares, another method of parameter estimation, are a consequence of ML estimation using Fisher scoring.
This text thoroughly covers GLMs, both theoretically and computationally, with an emphasis on Stata. The theory consists of showing how the various GLMs are special cases of the exponential family, showing general properties of this family of distributions, and showing the derivation of maximum likelihood (ML) estimators and standard errors. Hardin and Hilbe show how iteratively reweighted least squares, another method of parameter estimation, are a consequence of ML estimation using Fisher scoring.
More details
Edition
4th edition
Language
English
Place of publication
College Station
United States
Target group
College/higher education
Professional and scholarly
Academic, Postgraduate, and Professional Practice & Development
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 238 mm
Width: 182 mm
Thickness: 40 mm
Weight
1237 gr
ISBN-13
978-1-59718-225-6 (9781597182256)
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Schweitzer Classification
Other editions
Previous edition

James W. Hardin | Joseph M. Hilbe
Generalized Linear Models and Extensions, Third Edition
Book
06/2012
3rd Edition
Stata Press
€110.36
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Persons
James W. Hardin is a professor and the Biostatistics division head in the Department of Epidemiology and Biostatistics at the University of South Carolina. He is also the associate dean for Faculty Affairs and Curriculum of the Arnold School of Public Health at the University of South Carolina.
Joseph M. Hilbe was a professor emeritus at the University of Hawaii and an adjunct professor of sociology and statistics at Arizona State University.
Joseph M. Hilbe was a professor emeritus at the University of Hawaii and an adjunct professor of sociology and statistics at Arizona State University.
Author
University of South Carolina, Columbia, USA
California Institute of Technology, Pasadena, and Arizona State University, Tempe, USA
Content
Foundations of Generalized Linear Models. GLMs. GLM estimation algorithms. Analysis of fit. Continuous Response Models. The Gaussian family. The gamma family. The inverse Gaussian family. The power family and link. Binomial Response Models. The binomial-logit family. The general binomial family. The problem of overdispersion. Count Response Models. The Poisson family. The negative binomial family. Other count-data models. Multinomial Response Models. Unordered-response family. The ordered-response family. Extensions to the GLM. Extending the likelihood. Clustered data. Bivariate and multivariate models. Bayesian GLMs. Stata Software. Programs for Stata. Data synthesis.