Two matrix-variate distributions, both elliptical heavy-tailed generalization of the matrix-
variate normal distribution, are introduced. They belong to the normal scale mixture family …

Matrix-variate data analysis has increasingly attracted interest in the statistical literature over
the recent years, especially in the model-based clustering framework. Here, we firstly …

Cluster-weighted models (CWMs) extend finite mixtures of regressions (FMRs) in order to
allow the distribution of covariates to contribute to the clustering process. In this article, we …

In recent years, the analysis of three‐way data has become ever more prevalent in the
literature. It is becoming increasingly common to analyse such data by means of matrix …

With the rise of the" big data" phenomenon in recent years, data is coming in many different
complex forms. One example of this is multi-way data that come in the form of higher-order …

Over the years, data have become increasingly higher dimensional, which has prompted an
increased need for dimension reduction techniques. This is perhaps especially true for …

Finite mixtures of regressions (FMRs) are powerful clustering devices used in many
regression-type analyses. Unfortunately, real data often present atypical observations that …

Real data is taking on more and more complex structures, raising the necessity for more
flexible and parsimonious statistical methodologies. Tensor-variate (or multi-way) structures …

Robust clustering of high-dimensional data is an important topic because clusters in real
datasets are often heavy-tailed and/or asymmetric. Traditional approaches to model-based …

A least-squares bilinear clustering framework for modelling three-way data, where each
observation consists of an ordinary two-way matrix, is introduced. The method combines …