论文标题
小弱依赖样品的Copula模型中的最大伪样估计
Maximum pseudo-likelihood estimation in copula models for small weakly dependent samples
论文作者
论文摘要
最大伪可能（MPL）是一种半参数估计方法，通常用于从数据中获得copula模型中的依赖性参数。已经表明，尽管保持一致，并且在某些情况下，MPL估计可以高估依赖水平，尤其是对于小弱依赖样品的依赖水平。我们表明，MPL方法使用订单统计的预期值，我们建议使用中位数或相同订单统计的模式。在一项仿真研究中，我们将提出的估计量的有限样本性能与基于Kendall的Tau和Spearman的Rho的原始MPL和原始MPL和反转方法估计器进行了比较。我们的结果表明，经过修改的MPL估计器，尤其是基于订单统计模式的MPL估计器具有更好的有限样本性能，同时仍享受原始MPL方法的大样本属性。
Maximum pseudo-likelihood (MPL) is a semiparametric estimation method often used to obtain the dependence parameters in copula models from data. It has been shown that despite being consistent, and in some cases efficient, MPL estimation can overestimate the level of dependence especially for small weakly dependent samples. We show that the MPL method uses the expected value of order statistics and we propose to use instead the median or the mode of the same order statistics. In a simulation study we compare the finite-sample performance of the proposed estimators with that of the original MPL and the inversion method estimators based on Kendall's tau and Spearman's rho. Our results indicate that the modified MPL estimators, especially the one based on the mode of the order statistics, have better finite-sample performance, while still enjoying the large-sample properties of the original MPL method.
