Finite mixtures of regressions with fixed covariates are a commonly used model-based
clustering methodology to deal with regression data. However, they assume assignment …

This paper introduces a novel two-sample test for a broad class of orthogonally invariant
positive definite symmetric matrix distributions. Our test is the first of its kind, and we derive …

Two families of parsimonious mixture models are introduced for model-based clustering.
They are based on two multivariate distributions-the shifted exponential normal and the tail …

Clustering is a fundamental part in data mining. Recent years have gone up in research on
related fields owing to the widespread existence of time series in various fields. To describe …

In social sciences, studies are often based on questionnaires asking participants to express
ordered responses several times over a study period. We present a model-based clustering …

With the steady growth of computer technologies, the application of statistical techniques to
analyze extensive datasets has garnered substantial attention. The analysis of three-way …

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 …

Abstract Hidden Markov models (HMMs) have been extensively used in the univariate and
multivariate literature. However, there has been an increased interest in the analysis of …

The evaluation of the teaching efficiency, under different points of view, is an important
aspect for the university system because it helps managers to improve more and more the …