Abstract
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.
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