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Bayesian Model Selection in Factor Analytic Models

  • Joyee Ghosh
  • David B. Dunson
Part of the Lecture Notes in Statistics book series (LNS, volume 192)

Abstract

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).

Keywords

Markov Chain Monte Carlo Gibbs Sampler Marginal Likelihood Reversible Jump Markov Chain Monte Carlo Posterior Model Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research was supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Department of Statistical ScienceDuke UniversityDurham
  2. 2.Biostatistics BranchNational Institute of Environmental Health Sciences, RTPUSA

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