It is well-known that real data often contain outliers. The term outlier usually refers to a case,
usually denoted by a row of the n× d data matrix. In recent times a different type has come …

For the purpose of explaining multivariate outlyingness, it is shown that the squared
Mahalanobis distance of an observation can be decomposed into outlyingness contributions …

The cellwise robust M regression estimator is introduced as the first estimator of its kind that
intrinsically yields both a map of cellwise outliers consistent with the linear model, and a …

Data sets derived from practical experiments often pose challenges for (robust) statistical
methods. In high-dimensional data sets, more variables than observations are recorded and …

We propose a data-analytic method for detecting cellwise outliers. Given a robust
covariance matrix, outlying cells (entries) in a row are found by the cellHandler technique …

Cellwise contamination remains a challenging problem for data scientists, particularly in
research fields that require the selection of sparse features. Traditional robust methods may …

Abstract Models must be checked for consistency with actual observations before they can
be used for forecasting. This can be done either before data are assimilated through …

Geochemical pattern recognition has long been of interest for geologists to reveal
geochemical anomalies associated with mineralization. In regional-scale exploration …

Social Question Answering sites (SQAs) are online platforms that allow Internet users to ask
questions, and obtain answers from others in the community. SQAs have been marred by …

Cellwise outliers are widespread in real world data analysis. Traditional robust methods may
fail when applied to datasets under such contamination. We introduce a variable selection …