Bayesian Analysis

A default conjugate prior for variance components in generalized linear mixed models (comment on article by Browne and Draper)

Robert E. Kass and Ranjini Natarajan

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Abstract

For a scalar random-effect variance, Browne and Draper (2005) have found that the uniform prior works well. It would be valuable to know more about the vector case, in which a second-stage prior on the random effects variance matrix ${\bf D}$ is needed. We suggest consideration of an inverse Wishart prior for ${\bf D}$ where the scale matrix is determined from the first-stage variance.

Article information

Source
Bayesian Anal. Volume 1, Number 3 (2006), 535-542.

Dates
First available in Project Euclid: 22 June 2012

Permanent link to this document
https://projecteuclid.org/euclid.ba/1340371049

Digital Object Identifier
doi:10.1214/06-BA117B

Mathematical Reviews number (MathSciNet)
MR2221285

Zentralblatt MATH identifier
1331.62148

Keywords
Choice of prior hierarchical models noninformative priors random effects

Citation

Kass, Robert E.; Natarajan, Ranjini. A default conjugate prior for variance components in generalized linear mixed models (comment on article by Browne and Draper). Bayesian Anal. 1 (2006), no. 3, 535--542. doi:10.1214/06-BA117B. https://projecteuclid.org/euclid.ba/1340371049


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References

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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.