Copula Gaussian graphical models and their application to modeling functional disability data

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.

Published in at http://dx.doi.org/10.1214/10-AOAS397 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (3)

• Copula Gaussian graphical models and their application to modeling functional disability data
• Describing disability through individual-level mixture models for multivariate binary data
• Wishart distributions for decomposable graphs

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