Real data often contain anomalous cases, also known as outliers. These may spoil the
resulting analysis but they may also contain valuable information. In either case, the ability to …

Outlier detection has been a topic in statistics for centuries. Over mainly the last two
decades, there has been also an increasing interest in the database and data mining …

A new edition of this popular text on robust statistics, thoroughly updated to include new and
improved methods and focus on implementation of methodology using the increasingly …

Digitization as the process of converting information into numbers leads to bigger and more
complex data sets, bigger also with respect to the number of measured variables. This …

The minimum volume ellipsoid (MVE) estimator is based on the smallest volume ellipsoid
that covers h of the n observations. It is an affine equivariant, high‐breakdown robust …

ABSTRACT A multivariate dataset consists of n cases in d dimensions, and is often stored in
an n by d data matrix. It is well-known that real data may contain outliers. Depending on the …

This book gives a non-technical overview of robust data reduction techniques, encouraging
the use of these important and useful methods in practical applications. The main areas …

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 …

The linear regression model remains an important workhorse for data scientists. However,
many data sets contain many more predictors than observations. Besides, outliers, or …

Most algorithms for highly robust estimators of multivariate location and scatter start by
drawing a large number of random subsets. For instance, the FASTMCD algorithm of …