The Masking Breakdown Point of Multivariate Outlier Identification Rules
研究了多元数据中同时识别多个异常值的规则，探讨了估计量的有限样本崩溃点如何影响这些规则的掩蔽行为，为选择稳健估计量提供理论依据。
Abstract In this article, we consider simultaneous outlier identification rules for multivariate data, generalizing the concept of so-called α outlier identifiers, as presented by Davies and Gather for the case of univariate samples. Such multivariate outlier identifiers are based on estimators of location and covariance. Therefore, it seems reasonable that characteristics of the estimators influence the behavior of outlier identifiers. Several authors mentioned that using estimators with low finite-sample breakdown point is not recommended for identifying outliers. To give a formal explanation, we investigate how the finite-sample breakdown points of estimators used in these identification rules influence the masking behavior of the rules.