The goal of robust statistics is to develop methods that are robust against outliers in the data.
We emphasize on high breakdown estimators, which can deal with heavy contamination in …

Since 1960, many theoretical efforts have been devoted to develop statistical procedures
that are resistant to small deviations from the assumptions, ie robust with respect to outliers …

When applying a statistical method in practice it often occurs that some observations deviate
from the usual assumptions. However, many classical methods are sensitive to outliers. The …

In this paper we consider regression data possibly contaminated with the presence of
multiple outliers. The goal of this work is to compare the information which can be extracted …

During the last decades, a lot of effort has been made in the development of robust statistical
methods. Nowadays, many robust counterparts to traditional non-robust methods are …

Users of statistical packages need to be aware of the influence that outlying data points can
have on their statistical analyses. Robust procedures provide formal methods to spot these …

Starting with 2020 volume, the journal Metron has decided to celebrate the centenary since
its foundation with three special issues. This volume is dedicated to robust statistics. A …

The least-squares method is currently the most popular approach to estimation because of
tradition and ease of computation. However, real data sets frequently contain outliers, which …

Robust statistics have slowly become familiar to all practitioners. Books entirely devoted to
the subject (eg [RA Maronna, RD Martin, VJ Yohai, Robust Statistics: Theory and Methods …

In the robust statistics literature, a wide variety of models have been devel% oped to cope
with outliers in a rather large number of scenarios. Nevertheless, a recurrent problem for the …