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 …

Mixtures of multivariate leptokurtic-normal distributions have been recently introduced in the
clustering literature based on mixtures of elliptical heavy-tailed distributions. They have the …

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 …

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

Quite often real data exhibit non-normal features, such as asymmetry and heavy tails, and
present a latent group structure. In this paper, we first propose the multivariate skew shifted …

Given the nature of time series and their vast applications, it is essential to find clustering
algorithms that depict their real-life properties. Among the features that can hugely effect the …

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 …

In this article, extensions to the recently introduced concept of pairwise overlap between
mixture components are proposed. The notion of overlap is useful for studying the …

The cluster-weighted model (CWM) is a model-based clustering approach that utilizes a
mixture of regression models to cluster data points based on both a response variable Y and …

The interaction between Human Papillomavirus (HPV) infection and genes play a crucial
role in the occurrence and development of HPV-related diseases. HPV-related genes were …