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June 2011 Copula Gaussian graphical models and their application to modeling functional disability data
Adrian Dobra, Alex Lenkoski
Ann. Appl. Stat. 5(2A): 969-993 (June 2011). DOI: 10.1214/10-AOAS397

Abstract

We propose a comprehensive Bayesian approach for graphical model determination in observational studies that can accommodate binary, ordinal or continuous variables simultaneously. Our new models are called copula Gaussian graphical models (CGGMs) and embed graphical model selection inside a semiparametric Gaussian copula. The domain of applicability of our methods is very broad and encompasses many studies from social science and economics. We illustrate the use of the copula Gaussian graphical models in the analysis of a 16-dimensional functional disability contingency table.

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Adrian Dobra. Alex Lenkoski. "Copula Gaussian graphical models and their application to modeling functional disability data." Ann. Appl. Stat. 5 (2A) 969 - 993, June 2011. https://doi.org/10.1214/10-AOAS397

Information

Published: June 2011
First available in Project Euclid: 13 July 2011

zbMATH: 1232.62046
MathSciNet: MR2840183
Digital Object Identifier: 10.1214/10-AOAS397

Keywords: Bayesian inference , Gaussian graphical models , latent variable model , Markov chain Monte Carlo

Rights: Copyright © 2011 Institute of Mathematical Statistics

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Vol.5 • No. 2A • June 2011
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