NDLI: Empirical Bayes Gibbs sampling

Content Provider	Paperity
Author	Casella, George
Abstract	The wide applicability of Gibbs sampling has increased the use of more complex and multiâ€�level hierarchical models. To use these models entails dealing with hyperparameters in the deeper levels of a hierarchy. There are three typical methods for dealing with these hyperparameters: specify them, estimate them, or use a â€˜flatâ€™ prior. Each of these strategies has its own associated problems. In this paper, using an empirical Bayes approach, we show how the hyperparameters can be estimated in a way that is both computationally feasible and statistically valid.
Starting Page	485
Ending Page	500
File Format	HTM / HTML
ISSN	14654644
DOI	10.1093/biostatistics/2.4.485
Issue Number	4
Journal	Biostatistics
Volume Number	2
e-ISSN	14684357
Language	English
Publisher	Oxford University Press
Publisher Date	2001-12-01
Access Restriction	Open
Subject Keyword	Bayesian computation Hierarchical models Likelihood Consistency Empirical bayes
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Medicine Statistics, Probability and Uncertainty

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Empirical Bayes Gibbs sampling