  Robustness to outliers is often a desirable property of statistical
estimators. Indeed many well known estimators offer very good optimal
performance in theory but are unusable in applied contexts because of their
sensitivity to outliers. Of particular interest to the authors is the case of
covariance estimators in adaptive matched filtering schemes in signal
processing applications such as RADAR and SONAR detection, for which a
contamination by outliers of the estimated noise covariance can lead to a great
impact on performances, in particular when these outliers are similar to the
target signal of the matched filter.
  This paper presents a generic method for building partial estimators from
known estimators, which aim at avoiding these issues; the resulting algorithms
are shown for a few chosen cases.
