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 same 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 and still enjoy the large-sample properties of the original MPL method.
翻译:最大假象(MPL)是一种半参数估计方法,通常用来从数据中获得相交模型中的依赖参数,已经表明,尽管这种估计是一致的,而且在同一情况下是有效的,但MPL估计可能高估依赖程度,特别是对于依赖性弱的小样本而言。我们表明,MPL方法使用定序统计的预期值,我们提议用中位值或相同定序统计的模式替代中位值或同一定序统计的模式。在一项模拟研究中,我们比较了拟议定位数的有限抽样性能与最初的MPL和基于Kendall's Tau 和Spearman rho 的定流方法的测算器。我们的结果显示,修改的MPL估计器,特别是基于定序统计模式的测算器,有更好的定序性性能,并且仍然享有最初的MPL方法的大样特性。