عنوان

Generalized linear models and extensions /

(Number (ISBN

1597182257

(Number (ISBN

9781597182256

Erroneous ISBN

1597182265 (ePub)

Erroneous ISBN

1597182273 (Mobi)

Erroneous ISBN

9781597182263 (ePub)

Erroneous ISBN

9781597182270 (Mobi)

Title Proper

Generalized linear models and extensions /

General Material Designation

[Book]

First Statement of Responsibility

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.

Edition Statement

Fourth edition.

Place of Publication, Distribution, etc.

College Station, Texas :

Name of Publisher, Distributor, etc.

Stata Press,

Date of Publication, Distribution, etc.

[2018]

Specific Material Designation and Extent of Item

xxx, 598 pages :

Other Physical Details

illustrations ;

Dimensions

24 cm

Text of Note

Includes bibliographical references (pages 577-587) and indexes.

Text of Note

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.

0

Text of Note

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. --

Linear models (Statistics)

Linear Models.

Linear models (Statistics)

Number

Class number

Book number

Class number

System Code

Hardin, James W., (James William)

Hilbe, Joseph M.,1944-

Date of Transaction

20200824160018.0

Cataloguing Rules (Descriptive Conventions))

rda

Electronic name

[Book]

Y