Indexed by:Journal paper Document Code:000344384800009 First Author:Juan Wu Correspondence Author:Juan Wu Co-author:Xue Wang,Stephen G. Walker Journal:Journal of Statistical Computation and Simulation Included Journals:SCI Affiliation of Author(s):Huazhong University of Science and Technology Place of Publication:UK Discipline:Science First-Level Discipline:Statistics Document Type:J Volume:1 Issue:85 Page Number:103-116 ISSN No.:0094-9655 Key Words:Bayesian nonparametric estimation; copula; Gaussian copula; Gibbs sampling; slice sampling Date of Publication:2015-04-01 Abstract:Acopula can fully characterize the dependence of multiple variables. The purpose of this paper is to provide a Bayesian nonparametric approach to the estimation of a copula, and we do this by mixing over a class of parametric copulas. In particular, we show that any bivariate copula density can be arbitrarily accurately approximated by an infinite mixture of Gaussian copula density functions. The model can be estimated by Markov Chain Monte Carlo methods and the model is demonstrated on both simulated and real data sets. Links to published journals:http://dx.doi.org/10.1080/00949655.2013.806508