|
Activity Number:
|
432
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Wednesday, August 6, 2008 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Section on Bayesian Statistical Science
|
| Abstract - #300866 |
|
Title:
|
Default Priors and Efficient Posterior Computation in Bayesian Factor Analysis
|
|
Author(s):
|
Joyee Ghosh*+ and David Dunson
|
|
Companies:
|
Duke University and Duke University
|
|
Address:
|
943 Morreene Road, Apt. B 16, Durham, NC, 27705,
|
|
Keywords:
|
Bayes factor ; Covariance structure ; Latent variables ; Parameter expansion ; Selection of factors ; Slow mixing
|
|
Abstract:
|
Factor analytic models are widely used in social sciences and are also useful for sparse modeling of the covariance structure in multivariate data. Normal priors for factor loadings and inverse gamma priors for residual variances are a popular choice because of their conditionally conjugate form. However, such priors 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 priors face problems due to impropriety of the posterior. This article proposes a default, heavy tailed prior 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.
|
