Many real data sets contain numerical features (variables) whose distribution is far from
normal (Gaussian). Instead, their distribution is often skewed. In order to handle such data it …

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) …

Synthetic data generation for tabular data unlike computer vision, is an emerging challenge.
When tabular data needs to be synthesized, it either faces a small dataset problem or …

In the last few years, the number of R packages implementing different robust statistical
methods have increased substantially. There are now numerous packages for computing …

Discriminant analysis (DA) is one of the most popular methods for classification due to its
conceptual simplicity, low computational cost, and often solid performance. In its standard …

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 …

The product moment covariance matrix is a cornerstone of multivariate data analysis, from
which one can derive correlations, principal components, Mahalanobis distances and many …

Multivariate data are typically represented by a rectangular matrix (table) in which the rows
are the objects (cases) and the columns are the variables (measurements). When there are …

Outliers are encountered in all practical situations of data analysis, regardless of the
discipline of application. However, the term outlier is not uniformly defined across all these …

Classification datasets created from chemical processes can be affected by errors, which
impair the accuracy of the models built. This fact highlights the importance of analyzing the …