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Bayes and empirical Bayes methods for data analysis

Author: Bradley P Carlin; Thomas A Louis
Publisher: London ; New York : Chapman & Hall, ©1996.
Series: Monographs on statistics and applied probability (Series), 69.
Edition/Format:   Print book : English : 1st edView all editions and formats
Summary:
"As a result of recent advances in computing and the consequent ability to evaluate complex models, Bayes and empirical Bayes (EB) methods have increased in popularity for data analysis. Bayes and Empirical Bayes Methods for Data Analysis introduces these methods, demonstrates their usefulness in challenging applied settings and shows how they can be implemented using modern Markov chain Monte Carlo (MCMC)  Read more...
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Details

Document Type: Book
All Authors / Contributors: Bradley P Carlin; Thomas A Louis
ISBN: 0412056119 9780412056116
OCLC Number: 35110631
Description: xvi, 399 pages : illustrations ; 23 cm.
Contents: Approaches for statistical inference --
Motivating vignettes --
Personal probability --
Missing data --
Bioassay --
Attenuation adjustment --
Defining the approaches --
The Bayes-frequentist controversy --
Some basic Bayesian models --
A Gaussian/Gaussian (normal/normal) model --
A beta/binomial model --
The Bayes approach --
Prior distributions --
Elicited priors --
Conjugate priors --
Noninformative priors --
Other prior construction methods --
Bayesian inference --
Point estimation --
Interval estimation --
Hypothesis testing and Bayes factors --
Example: Consumer preference data --
Model assessment --
Diagnostic measures --
Model averaging --
Nonparametric methods --
The empirical Bayes approach --
Nonparametric EB (NPEB) point estimation --
Compound sampling models --
Simple NPEB (Robbins' method) --
Example: Accident data --
Parametric EB (PEB) point estimation --
Gaussian/Gaussian models --
Beta/binomial model --
EB performance of the PEB --
Stein estimation --
Computation via the EM algorithm --
EM for PEB --
Computing the observed information --
EM for NPEB --
Speeding convergence and generalizations --
Interval estimation --
Morris' approach --
Marginal posterior approach --
Bias correction approach --
Generalization to regression structures --
Performance of Bayes procedures --
Bayesian processing --
Univariate stretching with a two-point prior --
Multivariate Gaussian model --
Frequentist performance: Point estimates --
Gaussian/Gaussian model --
Beta/binomial model --
Generalization.
Series Title: Monographs on statistics and applied probability (Series), 69.
Responsibility: Bradley P. Carlin and Thomas A. Louis.

Abstract:

Using currently available techniques and software, this text provides a detailed understanding of the modern computational methods needed to implement relevant Bayes and empirical Bayes methods. It  Read more...

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