Bayesian Gaussian Copula Factor Models for Mixed Data.
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.
Type
Journal articleSubject
Extended rank likelihoodFactor analysis
High dimensional
Latent variables
Parameter expansion
Semiparametric
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https://hdl.handle.net/10161/8942Published Version (Please cite this version)
10.1080/01621459.2012.762328Publication Info
Murray, Jared S; Dunson, David B; Carin, Lawrence; & Lucas, Joseph E (2013). Bayesian Gaussian Copula Factor Models for Mixed Data. J Am Stat Assoc, 108(502). pp. 656-665. 10.1080/01621459.2012.762328. Retrieved from https://hdl.handle.net/10161/8942.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Lawrence Carin
Professor of Electrical and Computer Engineering
Lawrence Carin earned the BS, MS, and PhD degrees in electrical engineering at the
University of Maryland, College Park, in 1985, 1986, and 1989, respectively. In 1989
he joined the Electrical Engineering Department at Polytechnic University (Brooklyn)
as an Assistant Professor, and became an Associate Professor there in 1994. In September
1995 he joined the Electrical and Computer Engineering (ECE) Department at Duke University,
where he is now a Professor. He was ECE Department Chair from 2011
David B. Dunson
Arts and Sciences Distinguished Professor of Statistical Science
My research focuses on developing new tools for probabilistic learning from complex
data - methods development is directly motivated by challenging applications in ecology/biodiversity,
neuroscience, environmental health, criminal justice/fairness, and more. We seek
to develop new modeling frameworks, algorithms and corresponding code that can be
used routinely by scientists and decision makers. We are also interested in new inference
framework and in studying theoretical properties
Joseph E. Lucas
Associate Research Professor in the Social Science Research Institute
This author no longer has a Scholars@Duke profile, so the information shown here reflects
their Duke status at the time this item was deposited.
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