Flexible Bayesian Estimation of Elliptical Copulas
提出一种贝叶斯框架下估计椭圆型连接函数生成子的方法，利用B样条密度混合评估似然，比现有方法更简单稳健，附有Matlab代码。
Elliptical copulas provide flexibility in modeling the dependence structure of a random vector. They are often parameterized with a correlation matrix and a scalar function, called generator. The estimation of the generator can be challenging, because it is a functional parameter. In this paper, we provide a rigorous approach to estimating the generator in a Bayesian framework, which is simpler, more robust, and outperforms existing estimation methods in the literature. A major contribution of this paper is a robust method of evaluating the elliptical copula likelihood by using mixtures of B-spline densities. The Matlab code used for the simulation study is available in the supplementary material.