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.
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ABSTRACT Generalized linear models (GLMs) offer a unifying class of models which are widely used in regression analysis. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
dynamic generalized linear models, posterior model probabilities, prior model probabilities, probit normal model, student retention data, determination using predictive distributions, ith historical study, exchangeable random effects, correlated ordinal data, proposal transition density, posterior ordinate, posterior density estimation, correlated binary data, reparameterization technique, binary regression models, polychotomous response data, small area effects, small area estimation, elicited ratings, variable subset selection, largest posterior probability, correlated random effects, multivariate probit model, item response model, generalized linear mixed models Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, New York, Journal of the Royal Statistical Society, Oxford University Press, Department of Statistics, John Wiley, Marcel Dekker, Statistical Science, Six Cities, The Annals of Statistics, Stochastic Search Variable Selection, Imperial College, University of Connecticut, References Albert, Repeat Steps, University of Arkansas, Statistica Sinica, Baton Rouge, Bayesian Estimates of the Mean-Ranking Measures, Biostatistics Unit, Cambridge University Press, Canadian Journal of Statistics, Cath Only, Duke University, Journal of Chemical Physics New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
ABSTRACT Generalized linear models (GLMs) offer a unifying class of models which are widely used in regression analysis. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
dynamic generalized linear models, posterior model probabilities, prior model probabilities, probit normal model, student retention data, determination using predictive distributions, ith historical study, exchangeable random effects, correlated ordinal data, proposal transition density, posterior ordinate, posterior density estimation, correlated binary data, reparameterization technique, binary regression models, polychotomous response data, small area effects, small area estimation, elicited ratings, variable subset selection, largest posterior probability, correlated random effects, multivariate probit model, item response model, generalized linear mixed models Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, New York, Journal of the Royal Statistical Society, Oxford University Press, Department of Statistics, John Wiley, Marcel Dekker, Statistical Science, Six Cities, The Annals of Statistics, Stochastic Search Variable Selection, Imperial College, University of Connecticut, References Albert, Repeat Steps, University of Arkansas, Statistica Sinica, Baton Rouge, Bayesian Estimates of the Mean-Ranking Measures, Biostatistics Unit, Cambridge University Press, Canadian Journal of Statistics, Cath Only, Duke University, Journal of Chemical Physics New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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This book cites 62
books:
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- Markov Chain Monte Carlo in Practice (Chapman & Hall/CRC Interdisciplinary Statistics) by W.R. Gilks on 8 pages
- Statistical Decision Theory: Foundations, Concepts, and Methods (Springer Series in Statistics) by J.O. Berger on page 17, page 52, and page 240
- Statistical Decision Theory and Bayesian Analysis (Springer Series in Statistics) by James O. Berger on page 17, page 52, and page 240
- Bayesian Data Analysis, Second Edition (Chapman & Hall/CRC Texts in Statistical Science) by Andrew Gelman on page 106, page 193, and page 212
- Categorical Data Analysis (Wiley Series in Probability and Statistics) by Alan Agresti on page 239, and page 284
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