Abstract
The traditional Bayesian factor analysis method is extended. In contrast to the case for previous studies, the matrix variate t-distribution is utilized to provide a prior density on the latent factors. This is a natural extension of the traditional model and yields many advantages. The crucial issue is the selection of the number of factors. The marginal likelihood, constructed by asymptotic and computational approaches, is generally utilized for this problem. However, both theoretical and computational problems have arisen. In this paper, the exact marginal likelihood is derived. It enables us to evaluate posterior model probabilities without inducing the above problems. Monte Carlo experiments were conducted to examine the performance of the proposed Bayesian factor analysis modelling methodology. The simulation results show that the proposed methodology performs well.
Original language | English |
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Pages (from-to) | 1717-1726 |
Number of pages | 10 |
Journal | Journal of Multivariate Analysis |
Volume | 100 |
Issue number | 8 |
DOIs | |
Publication status | Published - 2009 Sep |
Externally published | Yes |
Keywords
- Bayesian methods
- Marginal likelihood
- Matrix variate t-distribution
- Model selection
ASJC Scopus subject areas
- Statistics and Probability
- Numerical Analysis
- Statistics, Probability and Uncertainty