Introduction to Generalized Linear Models

Autor: George Henry Dunteman, Moon-Ho R. Ho

Jazyk:

Väzba: Brožovaná

Vydavateľ: SAGE Publications Inc

Dostupnosť: Skladom u dodávateľa

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Do you have data that is not normally distributed and don't know how to analyze it using generalized...

Informácie o knihe

Autor

George Henry Dunteman, Moon-Ho R. Ho

Jazyk

Angličtina

Väzba

Kniha - Brožovaná

Vydalo

2005

Stránok

EAN

9780761920847

ISBN

0761920846

Enbook ID

04684187

Vydavateľ

SAGE Publications Inc

Hmotnosť

122

Rozmery

276 x 143 x 6

Kompletný popis

Do you have data that is not normally distributed and don't know how to analyze it using generalized linear models (GLM)? Beginning with a discussion of fundamental statistical modeling concepts in a multiple regression framework, the authors extend these concepts to a GLM (including Poisson regression. logistic regression, and proportional hazards models) and demonstrate the similarity of various regression models to GLM. Each procedure is illustrated using real life data sets, and the computer instructions and results will be presented for each example.Throughout the book, there is an emphasis on link functions and error distribution and how the model specifications translate into likelihood functions that can, through maximum likelihood estimation be used to estimate the regression parameters and their associated standard errors. This book provides readers with basic modeling principles that are applicable to a wide variety of situations. This title provides an accessible but thorough introduction to GLM, exponential family distribution, and maximum likelihood estimation. It includes discussion on checking model adequacy and description on how to use SAS to fit GLM. It describes the connection between survival analysis and GLM. This book is an ideal text for social science researchers who do not have a strong statistical background, but would like to learn more advanced techniques having taken an introductory course covering regression analysis.

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Introduction to Generalized Linear Models

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