期刊
BIOMETRIKA
卷 93, 期 3, 页码 537-554出版社
OXFORD UNIV PRESS
DOI: 10.1093/biomet/93.3.537
关键词
covariance selection; graphical model; Markov chain Monte Carlo; multivariate analysis; non-Gaussian data
A Gaussian copula regression model gives a tractable way of handling a multivariate regression when some of the marginal distributions are non-Gaussian. Our paper presents a general Bayesian approach for estimating a Gaussian copula model that can handle any combination of discrete and continuous marginals, and generalises Gaussian graphical models to the Gaussian copula framework. Posterior inference is carried out using a novel and efficient simulation method. The methods in the paper are applied to simulated and real data.
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