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.