Bayesian Analysis
- Bayesian Anal.
- Volume 1, Number 3 (2006), 515-534.
Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)
Full-text: Open access
Abstract
Various noninformative prior distributions have been suggested for scale parameters in hierarchical models. We construct a new folded-noncentral-$t$ family of conditionally conjugate priors for hierarchical standard deviation parameters, and then consider noninformative and weakly informative priors in this family. We use an example to illustrate serious problems with the inverse-gamma family of "noninformative" prior distributions. We suggest instead to use a uniform prior on the hierarchical standard deviation, using the half-$t$ family when the number of groups is small and in other settings where a weakly informative prior is desired. We also illustrate the use of the half-$t$ family for hierarchical modeling of multiple variance parameters such as arise in the analysis of variance.
Article information
Source
Bayesian Anal., Volume 1, Number 3 (2006), 515-534.
Dates
First available in Project Euclid: 22 June 2012
Permanent link to this document
https://projecteuclid.org/euclid.ba/1340371048
Digital Object Identifier
doi:10.1214/06-BA117A
Mathematical Reviews number (MathSciNet)
MR2221284
Zentralblatt MATH identifier
1331.62139
Keywords
Bayesian inference conditional conjugacy folded-noncentral-$t$ distribution half-$t$ distribution hierarchical model multilevel model noninformative prior distribution weakly informative prior distribution
Citation
Gelman, Andrew. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Anal. 1 (2006), no. 3, 515--534. doi:10.1214/06-BA117A. https://projecteuclid.org/euclid.ba/1340371048
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See also
- Related item: William J. Brown, David Draper. A comparison of Bayesian and likelihood-based methods for fitting multilevel
models. Bayesian Anal., Vol. 1, Iss. 3 (2006), 473-514.Project Euclid: euclid.ba/1340371047
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