The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 5, Number 2A (2011), 969-993.
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.
Ann. Appl. Stat., Volume 5, Number 2A (2011), 969-993.
First available in Project Euclid: 13 July 2011
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Dobra, Adrian; Lenkoski, Alex. Copula Gaussian graphical models and their application to modeling functional disability data. Ann. Appl. Stat. 5 (2011), no. 2A, 969--993. doi:10.1214/10-AOAS397. https://projecteuclid.org/euclid.aoas/1310562213
- Supplementary material: C++ implementation of copula Gaussian graphical models. We provide source code for the methodology described in this paper. Our program takes advantage of cluster computing to run several Markov chains in parallel. By using this code, one can replicate the analyses of the Rochdale data and the NLTCS functional disability data for which we give sample input files.