عنوان

Generalized linear models and extensions /

شابک

1597182257

شابک

9781597182256

شابک اشتباه

1597182265 (ePub)

شابک اشتباه

1597182273 (Mobi)

شابک اشتباه

9781597182263 (ePub)

شابک اشتباه

9781597182270 (Mobi)

عنوان اصلي

Generalized linear models and extensions /

نام عام مواد

[Book]

نام نخستين پديدآور

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.

وضعيت ويراست

Fourth edition.

محل نشرو پخش و غیره

College Station, Texas :

نام ناشر، پخش کننده و غيره

Stata Press,

تاریخ نشرو بخش و غیره

[2018]

نام خاص و کميت اثر

xxx, 598 pages :

ساير جزييات

illustrations ;

ابعاد

24 cm

متن يادداشت

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

متن يادداشت

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.

بدون عنوان

0

متن يادداشت

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. --

موضوع مستند نشده

Linear models (Statistics)

موضوع مستند نشده

Linear Models.

موضوع مستند نشده

Linear models (Statistics)

شماره

شماره رده

نشانه اثر

شماره رده

کد سيستم

مستند نام اشخاص تاييد نشده

Hardin, James W., (James William)

مستند نام اشخاص تاييد نشده

Hilbe, Joseph M.,1944-

تاريخ عمليات

20200824160018.0

قواعد فهرست نويسي ( بخش توصيفي )

rda

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y