Bayesian nonparametric estimation of a copula
Bayesian nonparametric estimation of a copula
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DOI：
10.1080/00949655.2013.806508
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发表时间：
2015-01
影响因子：
1.2
通讯作者：
Juan Wu;Xue Wang;S. Walker
中科院分区：
文献类型：
--
作者：
Juan Wu;Xue Wang;S. Walker
Copula可以完全刻画多个变量的相关性。本文的目的是提供一种估计Copula的贝叶斯非参数方法，我们通过在一类参数Copula上混合来实现这一点。特别地，我们证明了任何二元Copula密度都可以由GaussianCopula密度函数的无限混合来任意精确地逼近。该模型可以用马尔科夫链蒙特卡罗方法进行估计，并在模拟和真实数据集上进行了验证。
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.