Model-based clustering via skewed matrix-variate cluster-weighted models

Cluster-weighted models (CWMs) extend finite mixtures of regressions (FMRs) in order to allow the distribution covariates contribute clustering process. In this article, we introduce 24 matrix-variate CWMs which are obtained by allowing both responses and each cluster be modelled one four existing skewed distributions or normal distribution. Endowed with greater flexibility, our able handle kind data a more suitable manner. As by-product, FMRs also introduced. Maximum likelihood parameter estimates derived using an expectation-conditional maximization algorithm. Parameter recovery, classification assessment, capability Bayesian information criterion detect underlying groups investigated simulated data. Lastly, CWMs, along CWM FMRs, applied two real datasets for illustrative purposes.