Unmasking Multivariate Outliers and Leverage Points: Comment

Abstract

Detecting outliers in a multivariate point cloud is not trivial, especially when there are several outliers. The classical identification method does not always find them, because it is based on the sample mean and covariance matrix, which are themselves affected by the outliers. To avoid this masking effect, we propose to compute distances based on very robust estimates of location and covariance. In the case of regression data, the outliers also may be unmasked by using a highly robust regression method. A new display is proposed in which the robust regression residuals are plotted versus the robust distances