4 papers · 1 filter
Model-based clustering via skewed matrix-variate cluster-weighted models
Michael P. B. Gallaugher, Salvatore D. Tomarchio, Paul D. McNicholas +1
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 a matrix-va…
Parsimonious Hidden Markov Models for Matrix-Variate Longitudinal Data
Salvatore D. Tomarchio, Antonio Punzo, Antonello Maruotti
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 matrix-variat…
Matrix Normal Cluster-Weighted Models
Salvatore D. Tomarchio, Paul D. McNicholas, Antonio Punzo
Finite mixtures of regressions with fixed covariates are a commonly used model-based clustering methodology to deal with regression data. However, they assume assignment independen…
Multivariate Cluster Weighted Models Using Skewed Distributions
Michael P. B. Gallaugher, Salvatore D. Tomarchio, Paul D. McNicholas +1
Much work has been done in the area of the cluster weighted model (CWM), which extends the finite mixture of regression model to include modelling of the covariates. Although many…