Influence functions of the Spearman and Kendall correlation measures

作者：Christophe Croux, Catherine Dehon · 发表于：Statistical Methods & Applications · 年份：2010 · DOI：10.1007/s10260-010-0142-z · 被引用次数：650 · 研究领域：Advanced Statistical Methods and Models、Statistical Methods and Bayesian Inference、Statistical Methods and Inference

Nonparametric correlation estimators as the Kendall and Spearman correlation are widely used in the applied sciences. They are often said to be robust, in the sense of being resistant to outlying observations. In this paper we formally study their robustness by means of their influence functions and gross-error sensitivities. Since robustness of an estimator often comes at the price of an increased variance, we also compute statistical efficiencies at the normal model. We conclude that both the Spearman and Kendall correlation estimators combine a bounded and smooth influence function with a high efficiency. In a simulation experiment we compare these nonparametric estimators with correlations based on a robust covariance matrix estimator.