Regression with Linear Predictors

Name: Regression with Linear Predictors
Brand: Springer, Berlin
SKU: 9781441971692
Price: 1311 CZK
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Autor: Per Kr. Andersen, Lene Th. Skovgaard

Jazyk:

Vazba: Pevná

Vydavatel: Springer, Berlin

Dostupnost: Skladem u dodavatele v malém množství

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This text provides, in a non-technical language, a unified treatment of regression models for differ...

Informace o knize

Autor

Per Kr. Andersen, Lene Th. Skovgaard

Jazyk

Angličtina

Vazba

Kniha - Pevná

Vydáno

2010

Stránek

494

EAN

9781441971692

ISBN

1441971696

Enbook ID

02106804

Vydavatel

Springer, Berlin

Hmotnost

898

Rozměry

155 x 235 x 40

Kompletní popis

This text provides, in a non-technical language, a unified treatment of regression models for different outcome types, such as linear regression, logistic regression, and Cox regression. This is done by focusing on the many common aspects of these models, in particular the linear predictor, which combines the effects of all explanatory variables into a function which is linear in the unknown parameters. Specification and interpretation of various choices of parametrization of the effects of the covariates (categorical as well as quantitative) and interaction among these are elaborated upon. The merits and drawbacks of different link functions relating the linear predictor to the outcome are discussed with an emphasis on interpretational issues, and the fact that different research questions arise from adding or deleting covariates from the model is emphasized in both theory and practice.§Regression models with a linear predictor are commonly used in fields such as clinical medicine, epidemiology, and public health, and the book, including its many worked examples, builds on the authors' more than thirty years of experience as teachers, researchers and consultants at a biostatistical department. The book is well-suited for readers without a solid mathematical background and is accompanied by Web pages documenting in R, SAS, and STATA, the analyses presented throughout the text.

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Regression with Linear Predictors

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