吴娟
论文成果
Bayesian nonparametric inference for a multivariate copula function
发布时间：2018-09-29 点击次数：
论文类型：期刊论文
论文编号：62H12
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：Methodology and Computing in Applied Probability
收录刊物：SCI
所属单位：Huazhong University of Science and Technology
学科门类：理学
一级学科：统计学
文献类型：J
卷号：3
期号：16
页面范围：747-763
ISSN号：1387-5841
发表时间：2014-09-29
摘要：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.