Generalized linear models : a Bayesian perspective

著者

書誌事項

Generalized linear models : a Bayesian perspective

edited by Dipak K. Dey, Sujit K. Ghosh, Bani K. Mallick

(Biostatistics, 5)

Marcel Dekker, c2000

大学図書館所蔵 件 / 12

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注記

Includes bibliographies and index

内容説明・目次

内容説明

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.

目次

Part 1 Extending the GLMs. Part 2 Categorical and longitudinal data. Part 3 Semiparametric approaches. Part 4 Model diagnositics and value selection in GLMs. Part 5 Challenging problems in GLMs.

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