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 …

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 …

Financial institutions increasingly rely upon data-driven methods for develo** fraud
detection systems, which are able to automatically detect and block fraudulent transactions …

Perturbations targeting the graph structure have proven to be extremely effective in reducing
the performance of Graph Neural Networks (GNNs), and traditional defenses such as …

Abstract The usual Minimum Covariance Determinant (MCD) estimator of a covariance
matrix is robust against casewise outliers. These are cases (that is, rows of the data matrix) …

This paper proposes a novel technique that aims at detecting the effect of damage on
structural frequency signals as “bad” outliers. To this end, a procedure is developed based …

Statistics is the science of extracting useful information from data, and a statistical model is
used to provide a useful approximation to some of the important characteristics of the …

Diagnostic tools must rely on robust high-breakdown methodologies to avoid distortion in
the presence of contamination by outliers. However, a disadvantage of having a single, even …

The importance of statistical methods in finding patterns and trends in otherwise
unstructured and complex large sets of data has grown over the past decade, as the amount …

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 …