Indexed by:Journal paper Document Code:62H12 First Author:Juan Wu Correspondence Author:Juan Wu Co-author:Xue Wang,Stephen G. Walker Journal:Methodology and Computing in Applied Probability Included Journals:SCI Affiliation of Author(s):Huazhong University of Science and Technology Discipline:Science First-Level Discipline:Statistics Document Type:J Volume:3 Issue:16 Page Number:747-763 ISSN No.:1387-5841 Date of Publication:2014-09-29 Abstract:The paper presents a general Bayesian nonparametric approach for estimating a high dimensional copula. We first introduce the skew–normal copula, which we then extend to an infinite mixture model. The skew–normal copula fixes some limitations in the Gaussian copula. An MCMC algorithm is developed to draw samples from the correct posterior distribution and the model is investigated using both simulated and real applications.