A collection of robust Mahalanobis distances for multivariate outlier detection is proposed,
based on the notion of shrinkage. Robust intensity and scaling factors are optimally …

In this paper we develop multivariate outlier tests based on the high-breakdown Minimum
Covariance Determinant estimator. The rules that we propose have good performance …

Outlier detection is an integral component of statistical modelling and estimation. For high-
dimensional data, classical methods based on the Mahalanobis distance are usually not …

Abstract The Forward Search is a powerful general method, incorporating flexible data-
driven trimming, for the detection of outliers and unsuspected structure in data and so for …

Diagnostic tools must rely on robust high-breakdown methodologies to avoid distortion in
the presence of contamination by outliers. However, a disadvantage of having a single, even …

Data outliers or other data inhomogeneities lead to a violation of the assumptions of
traditional statistical estimators and methods. Robust statistics offers tools that can reliably …

Multivariate outlier identification requires the choice of reliable cut-off points for the robust
distances that measure the discrepancy from the fit provided by high-breakdown estimators …

Many different methods for statistical data editing can be found in the literature but only few
of them are based on robust estimates (for example such as BACON-EEM, epidemic …

In high reliability standards fields such as automotive, avionics or aerospace, the detection
of anomalies is crucial. An efficient methodology for automatically detecting multivariate …

It is well known that trimmed estimators of multivariate scatter, such as the Minimum
Covariance Determinant (MCD) estimator, are inconsistent unless an appropriate factor is …