Journal article
Default Prior Distributions and Efficient Posterior Computation in Bayesian Factor Analysis
Journal of Computational and Graphical Statistics, Vol.18(2), pp.306-320
01/01/2009
DOI: 10.1198/jcgs.2009.07145
PMCID: PMC3755784
PMID: 23997568
Abstract
Factor analytic models are widely used in social sciences. These models have also proven useful for sparse modeling of the covariance structure in multidimensional data. Normal prior distributions for factor loadings and inverse gamma prior distributions for residual variances are a popular choice because of their conditionally conjugate form. However, such prior distributions require elicitation of many hyperparameters and tend to result in poorly behaved Gibbs samplers. In addition, one must choose an informative specification, as high variance prior distributions face problems due to impropriety of the posterior distribution. This article proposes a default, heavy-tailed prior distribution specification, which is induced through parameter expansion while facilitating efficient posterior computation. We also develop an approach to allow uncertainty in the number of factors. The methods are illustrated through simulated examples and epidemiology and toxicology applications. Data sets and computer code used in this article are available online.
Details
- Title: Subtitle
- Default Prior Distributions and Efficient Posterior Computation in Bayesian Factor Analysis
- Creators
- Joyee GhoshDavid B Dunson
- Resource Type
- Journal article
- Publication Details
- Journal of Computational and Graphical Statistics, Vol.18(2), pp.306-320
- DOI
- 10.1198/jcgs.2009.07145
- PMID
- 23997568
- PMCID
- PMC3755784
- NLM abbreviation
- J Comput Graph Stat
- ISSN
- 1061-8600
- eISSN
- 1537-2715
- Publisher
- Taylor & Francis
- Language
- English
- Date published
- 01/01/2009
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9983985807302771
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