Generalized Linear Models: A Bayesian Perspective (Biostatistics (New York, N.Y.), 5.) (Hardcover)
by Dipak K. Dey (Editor), Sujit K. Ghosh (Editor), Bani K. Mallick (Editor)

Editorial Reviews

Statistical Methods in Medical Research
"…both accessible and valuable."

Review
both accessible and valuable. Anyone interested in applying the Bayesian GLMs should have a copy of this book on their shelf.
-Statistical Methods in Medical Research

Book Description
This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs and containing over 1000 references and equations, Generalized Linear Models considers parametric and semiparametric approaches to overdispersed GLMs, presents methods of analyzing correlated binary data using latent variables. It also proposes a semiparametric method to model link functions for binary response data, and identifies areas of important future research and new applications of GLMs.

Book Info
Describes how to perform, conceptualize, and critique traditional generalized linear models from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. DLC: Linear models (Statistics).

Product Details

Hardcover: 440 pages
Publisher: CRC; 1 edition (May 25, 2000)
Language: English
ISBN-10: 0824790340
ISBN-13: 978-0824790349
Product Dimensions: 10 x 7.2 x 1 inches

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