Brazilian Journal of Probability and Statistics (2003), 17, pp. 91–105. c©Associação Brasileira de Estat́ıstica Expected posterior priors in factor analysis (original) (raw)

Abstract: Bayesian inference in factor analytic models has received re-newed attention in recent years, partly due to computational advances but also partly to applied focuses generating factor structures as exemplified by recent work in financial time series modeling. The focus of our current work is to investigate the commonly overlooked problem of prior specification and sensitivity in factor models. We accomplish that by implementing Pérez and Berger’s (1999). Expected Posterior (EP) prior distributions. As opposed to alternative objective priors, such as Jeffreys ’ prior and Bernardo’s prior, EP prior has several important theoretical and practical properties, with its straightforward computation through MCMC methods and coherence when comparing multiple models perhaps the most important ones. Key words: Bayes ’ factors; Bayesian inference; expected posterior prior; latent factor models; model selection criteria; model uncertainty. 1