Default Prior Distributions and Efficient Posterior Computation in Bayesian Factor Analysis.

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

SUBMITTER: Ghosh J 

PROVIDER: S-EPMC3755784 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature