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 …

The r package pcadapt performs genome scans to detect genes under selection based on
population genomic data. It assumes that candidate markers are outliers with respect to how …

We consider the problem of estimating the mean and covariance of a distribution from iid
samples in the presence of a fraction of malicious noise. This is in contrast to much recent …

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 …

When analyzing data, outlying observations cause problems because they may strongly
influence the result. Robust statistics aims at detecting the outliers by searching for the …

This revised book provides a thorough explanation of the foundation of robust methods,
incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and …

Nonparametric correlation estimators as the Kendall and Spearman correlation are widely
used in the applied sciences. They are often said to be robust, in the sense of being resistant …

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The minimum covariance determinant (MCD) method is a highly robust estimator of
multivariate location and scatter, for which a fast algorithm is available. Since estimating the …