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Bayesian nonparametric estimation of a copula

Wu, Juan, Wang, Xue, Walker, Stephen G. (2015) Bayesian nonparametric estimation of a copula. Journal of Statistical Computation and Simulation, 85 (1). pp. 103-116. ISSN 0094-9655. (doi:10.1080/00949655.2013.806508) (KAR id:37210)

Abstract

A copula 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.

Item Type: Article
DOI/Identification number: 10.1080/00949655.2013.806508
Uncontrolled keywords: Bayesian nonparametric estimation; copula; Gaussian copula; Gibbs sampling; slice sampling.
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Xue Wang
Date Deposited: 10 Dec 2013 10:07 UTC
Last Modified: 01 Jan 2024 00:00 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/37210 (The current URI for this page, for reference purposes)

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