In this paper, we consider one-step outlier identifiation rules for multivariate data, generalizing the concept of so-called alpha outlier identifiers, as presented in Davies and Gather (1993) for the case of univariate samples. We investigate, how the finite-sample breakdown points of estimators used in these identification rules influence the masking behaviour of the rules.
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Becker et al. (1997) studied this question.
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