Bayesian Model Selection in Factor Analytic Models
Factor analytic models are widely used in social science applications to study latent traits, such as intelligence, creativity, stress, and depression, that cannot be accurately measured with a single variable. In recent years, there has been a rise in the popularity of factor models due to their flexibility in characterizing multivari-ate data. For example, latent factor regression models have been used as a dimensionality reduction tool for modeling of sparse covariance structures in genomic applications (West, 2003; Carvalho et al. 2008). In addition, structural equation models and other generalizations of factor analysis are widely useful in epidemi-ologic studies involving complex health outcomes and exposures (Sanchez et al., 2005). Improvements in Bayesian computation permit the routine implementation of latent factor models via Markov chain Monte Carlo (MCMC) algorithms, and a very broad class of models can be fitted easily using the freely available software package WinBUGS. The literature on methods for fitting and inferences in latent factor models is vast (for recent books, see Loehlin, 2004; Thompson, 2004).
KeywordsMarkov Chain Monte Carlo Gibbs Sampler Marginal Likelihood Reversible Jump Markov Chain Monte Carlo Posterior Model Probability
This research was supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences.
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