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Bayesian Gaussian Copula Factor Models for Mixed Data.

dc.contributor.author Carin, Lawrence
dc.contributor.author Dunson, David B
dc.contributor.author Lucas, Joseph E
dc.contributor.author Murray, JS
dc.coverage.spatial United States
dc.date.accessioned 2014-07-22T16:07:13Z
dc.date.issued 2013-06-01
dc.identifier http://www.ncbi.nlm.nih.gov/pubmed/23990691
dc.identifier.issn 0162-1459
dc.identifier.uri http://hdl.handle.net/10161/8942
dc.description.abstract 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.
dc.language eng
dc.relation.ispartof J Am Stat Assoc
dc.relation.isversionof 10.1080/01621459.2012.762328
dc.subject Extended rank likelihood
dc.subject Factor analysis
dc.subject High dimensional
dc.subject Latent variables
dc.subject Parameter expansion
dc.subject Semiparametric
dc.title Bayesian Gaussian Copula Factor Models for Mixed Data.
dc.type Journal article
pubs.author-url http://www.ncbi.nlm.nih.gov/pubmed/23990691
pubs.begin-page 656
pubs.end-page 665
pubs.issue 502
pubs.organisational-group Basic Science Departments
pubs.organisational-group Biostatistics & Bioinformatics
pubs.organisational-group Duke
pubs.organisational-group Duke Institute for Brain Sciences
pubs.organisational-group Electrical and Computer Engineering
pubs.organisational-group Institutes and Provost's Academic Units
pubs.organisational-group Mathematics
pubs.organisational-group Pratt School of Engineering
pubs.organisational-group School of Medicine
pubs.organisational-group Social Science Research Institute
pubs.organisational-group Statistical Science
pubs.organisational-group Trinity College of Arts & Sciences
pubs.organisational-group University Institutes and Centers
pubs.publication-status Published
pubs.volume 108


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