论文标题
基于最小α差异的副群体模型中半参数估计的模拟研究
A simulation study of semiparametric estimation in copula models based on minimum Alpha-Divergence
论文作者
论文摘要
本文的目的是引入两种半参数方法，以估计Copula参数。这些方法基于使用局部似然概率转化方法和真正的copula密度函数的copula密度的非参数估计之间的最小α差。进行了一项蒙特卡洛研究，以根据Hellinger距离和Neyman Divergence作为Alpha Divergence的特殊情况来衡量这些方法的性能。将模拟结果与众所周知的双变量copula模型中的常规估计方法进行比较，将最大伪样估计（MPL）估计比较。这些结果表明，基于最小伪Hellinger距离估计的提议方法在较小的样本量和弱依赖性情况下的性能良好。参数估计方法应用于水文中的真实数据集。
The purpose of this paper is to introduce two semiparametric methods for the estimation of copula parameter. These methods are based on minimum Alpha-Divergence between a non-parametric estimation of copula density using local likelihood probit transformation method and a true copula density function. A Monte Carlo study is performed to measure the performance of these methods based on Hellinger distance and Neyman divergence as special cases of Alpha-Divergence. Simulation results are compared to the Maximum Pseudo-Likelihood (MPL) estimation as a conventional estimation method in well-known bivariate copula models. These results show that the proposed method based on Minimum Pseudo Hellinger Distance estimation has a good performance in small sample size and weak dependency situations. The parameter estimation methods are applied to a real data set in Hydrology.
