Unmasking Multiple Outliers in Multivariate Data

Title & Authors
Unmasking Multiple Outliers in Multivariate Data
Yoo Jong-Young;

Abstract
We proposed a procedure for detecting of multiple outliers in multivariate data. Rousseeuw and van Zomeren (1990) have suggested the robust distance $\small{RD_i}$ by using the Resampling Algorithm. But $\small{RD_i}$ are based on the assumption that X is in the general position.(X is said to be in the general position when every subsample of size p+1 has rank p) From the practical points of view, this is clearly unrealistic. In this paper, we proposed a computing method for approximating MVE, which is not subject to these problems. The procedure is easy to compute, and works well even if subsample is singular or nearly singular matrix.
Keywords
Mahalanobis distances;Minimum volume ellipsoid;Masking effects;Swamping effects;Resampling algorithm;
Language
Korean
Cited by
References
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