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

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.

• Publication year

  2011

• Venue

  The Annals of Applied Statistics

• Publication date

  2011-06-01

• Fields of study

  Medicine, Mathematics, Economics, Computer Science

• Identifiers
  DOI 10.1214/10-AOAS397arXiv 1108.1680
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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