Generalized Linear Models with Random Effects: Unified Analysis Via H-Likelihood (Monographs on Statistics and Applied Probability, 106)

Lee, Youngjo; Nelder, John A; Pawitan, Yudi

ISBN 10: 1584886315 ISBN 13: 9781584886310

Editorial: Chapman & Hall/CRC, Boca Raton, Florida, USA, 2006

Idioma: Ingl�s

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Monographs on Statistics and Applied Probability, 106. Hardcover in very good condition. Pages are clean, and text and illustrations are clear. TH.

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Detalles bibliogr�ficos

T�tulo

Generalized Linear Models with Random Effects: Unified Analysis Via H-Likelihood (Monographs on Statistics and Applied Probability, 106)

Editor

Chapman & Hall/CRC, Boca Raton, Florida, USA

Fecha de publicaci�n

2006

Idioma

Ingl�s

ISBN 10

1584886315

ISBN 13

9781584886310

Encuadernaci�n

Hardcover

Estado

Very Good

Estado de la sobrecubierta

No Dust Jacket

Tipo de libro

Used

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Sinopsis

Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range of applications, including combining information over trials (meta-analysis), analysis of frailty models for survival data, genetic epidemiology, and analysis of spatial and temporal models with correlated errors. Written by pioneering authorities in the field, this reference provides an introduction to various theories and examines likelihood inference and GLMs. The authors show how to extend the class of GLMs while retaining as much simplicity as possible. By maximizing and deriving other quantities from h-likelihood, they also demonstrate how to use a single algorithm for all members of the class, resulting in a faster algorithm as compared to existing alternatives. Complementing theory with examples, many of which can be run by using the code supplied on the accompanying CD, this book is beneficial to statisticians and researchers involved in the above applications as well as quality-improvement experiments and missing-data analysis.

Rese�a del editor

Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range of applications, including combining information over trials (meta-analysis), analysis of frailty models for survival data, genetic epidemiology, and analysis of spatial and temporal models with correlated errors.

Written by pioneering authorities in the field, this reference provides an introduction to various theories and examines likelihood inference and GLMs. The authors show how to extend the class of GLMs while retaining as much simplicity as possible. By maximizing and deriving other quantities from h-likelihood, they also demonstrate how to use a single algorithm for all members of the class, resulting in a faster algorithm as compared to existing alternatives.

Complementing theory with examples, many of which can be run by using the code supplied on the accompanying CD, this book is beneficial to statisticians and researchers involved in the above applications as well as quality-improvement experiments and missing-data analysis.

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