论文类型：期刊论文
论文编号：000344384800009
第一作者：Juan Wu
通讯作者：Juan Wu
合写作者：Xue Wang,Stephen G. Walker
发表刊物：Journal of Statistical Computation and Simulation
收录刊物：SCI
所属单位：Huazhong University of Science and Technology
刊物所在地：UK
学科门类：理学
一级学科：统计学
文献类型：J
卷号：1
期号：85
页面范围：103-116
ISSN号：0094-9655
关键字：Bayesian nonparametric estimation; copula; Gaussian copula; Gibbs sampling; slice sampling
发表时间：2015-04-01
摘要：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.
发布期刊链接：http://dx.doi.org/10.1080/00949655.2013.806508