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