The size of the largest nonidentifiable outlier as a performance criterion for multivariate outlier identification: the case of high-dimensional data

Abstract

Various procedures exist for identifying outliers in multivariate data. To decide which identification rule should be chosen, several performance criteria can be used. We investigate here the problem of multivariate simultaneous outlier identification and concentrate on the criterion of the size of the largest nonidentifiable outlier. Four outlier identification rules are compared with respect to this criterion. Our main focus is on a comparison of the rules in high-dimensional data situations, and we present the results of a simulation study in an accordingly chosen 10-dimensional setting.