Generalized Linear Models: A Bayesian Perspective

November 3, 2019|
Generalized Linear Models: A Bayesian Perspective by Dipak K. Dey
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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.
Dipak K. Dey, Sujit K. Ghosh , Bani K. Mallick
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Title:Generalized Linear Models: A Bayesian PerspectiveFormat:PaperbackProduct dimensions:440 pages, 9.69 X 6.85 X 0 inShipping dimensions:440 pages, 9.69 X 6.85 X 0 inPublished:November 3, 2019Publisher:Crc Press LlcLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0367398605

ISBN - 13:9780367398606

Appropriate for ages: All ages

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