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" Generalized linear models and extensions / "
James W. Hardin, Department of Epidemiology and Biostatistics, University of South Carolina, Joseph M. Hilbe, Statistics, School of Social and Family Dynamics, Arizona State University.
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BL
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| Record Number
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892896
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| Main Entry
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Hardin, James W., (James William)
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| Title & Author
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Generalized linear models and extensions /\ James W. Hardin, Department of Epidemiology and Biostatistics, University of South Carolina, Joseph M. Hilbe, Statistics, School of Social and Family Dynamics, Arizona State University.
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| Edition Statement
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Fourth edition.
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| Publication Statement
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College Station, Texas :: Stata Press,, [2018]
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| Page. NO
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xxx, 598 pages :: illustrations ;; 24 cm
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| ISBN
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1597182257
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: 9781597182256
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1597182265 (ePub)
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1597182273 (Mobi)
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9781597182263 (ePub)
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9781597182270 (Mobi)
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| Bibliographies/Indexes
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Includes bibliographical references (pages 577-587) and indexes.
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| Contents
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I : Foundation of generalized linear models -- II : Continuous response models -- III : Binomial response models -- IV : Count response models -- V : Multinomial response models -- VI : Extensions to the GLM -- VII : Stata software.
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| Abstract
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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. --
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| Subject
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Linear models (Statistics)
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| Subject
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Linear Models.
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Linear models (Statistics)
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| Dewey Classification
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005.369 STATA
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| LC Classification
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QA276.H355 2018
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| NLM classification
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83.03bcl
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| Added Entry
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Hilbe, Joseph M.,1944-
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