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Bayesian Gaussian Copula Factor Models for Mixed Data.

Publication ,  Journal Article
Murray, JS; Dunson, DB; Carin, L; Lucas, JE
Published in: Journal of the American Statistical Association
June 2013

Gaussian factor models have proven widely useful for parsimoniously characterizing dependence in multivariate data. There is a rich literature on their extension to mixed categorical and continuous variables, using latent Gaussian variables or through generalized latent trait models acommodating measurements in the exponential family. However, when generalizing to non-Gaussian measured variables the latent variables typically influence both the dependence structure and the form of the marginal distributions, complicating interpretation and introducing artifacts. To address this problem we propose a novel class of Bayesian Gaussian copula factor models which decouple the latent factors from the marginal distributions. A semiparametric specification for the marginals based on the extended rank likelihood yields straightforward implementation and substantial computational gains. We provide new theoretical and empirical justifications for using this likelihood in Bayesian inference. We propose new default priors for the factor loadings and develop efficient parameter-expanded Gibbs sampling for posterior computation. The methods are evaluated through simulations and applied to a dataset in political science. The models in this paper are implemented in the R package bfa.

Duke Scholars

Published In

Journal of the American Statistical Association

DOI

EISSN

1537-274X

ISSN

0162-1459

Publication Date

June 2013

Volume

108

Issue

502

Start / End Page

656 / 665

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Murray, J. S., Dunson, D. B., Carin, L., & Lucas, J. E. (2013). Bayesian Gaussian Copula Factor Models for Mixed Data. Journal of the American Statistical Association, 108(502), 656–665. https://doi.org/10.1080/01621459.2012.762328
Murray, Jared S., David B. Dunson, Lawrence Carin, and Joseph E. Lucas. “Bayesian Gaussian Copula Factor Models for Mixed Data.Journal of the American Statistical Association 108, no. 502 (June 2013): 656–65. https://doi.org/10.1080/01621459.2012.762328.
Murray JS, Dunson DB, Carin L, Lucas JE. Bayesian Gaussian Copula Factor Models for Mixed Data. Journal of the American Statistical Association. 2013 Jun;108(502):656–65.
Murray, Jared S., et al. “Bayesian Gaussian Copula Factor Models for Mixed Data.Journal of the American Statistical Association, vol. 108, no. 502, June 2013, pp. 656–65. Epmc, doi:10.1080/01621459.2012.762328.
Murray JS, Dunson DB, Carin L, Lucas JE. Bayesian Gaussian Copula Factor Models for Mixed Data. Journal of the American Statistical Association. 2013 Jun;108(502):656–665.

Published In

Journal of the American Statistical Association

DOI

EISSN

1537-274X

ISSN

0162-1459

Publication Date

June 2013

Volume

108

Issue

502

Start / End Page

656 / 665

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1603 Demography
  • 1403 Econometrics
  • 0104 Statistics