A class of cluster-weighted models for a vector of continuous random variables is proposed. This class provides an extension to cluster-weighted modelling of multivariate and correlated responses that let the researcher free to use a different vector of covariates for each response. The class also includes parsimonious models obtained by imposing suitable constraints on the component-covariance matrices of either the responses or the covariates. Conditions for model identifiability are illustrated and discussed. Maximum likelihood estimation is carried out by means of an expectation-conditional maximisation algorithm. The effectiveness and usefulness of the proposed models are shown through the analysis of simulated and real datasets. (C) 2022 Elsevier B.V. All rights reserved.

Multivariate cluster-weighted models based on seemingly unrelated linear regression

Giuliano Galimberti;Gabriele Soffritti
2022

Abstract

A class of cluster-weighted models for a vector of continuous random variables is proposed. This class provides an extension to cluster-weighted modelling of multivariate and correlated responses that let the researcher free to use a different vector of covariates for each response. The class also includes parsimonious models obtained by imposing suitable constraints on the component-covariance matrices of either the responses or the covariates. Conditions for model identifiability are illustrated and discussed. Maximum likelihood estimation is carried out by means of an expectation-conditional maximisation algorithm. The effectiveness and usefulness of the proposed models are shown through the analysis of simulated and real datasets. (C) 2022 Elsevier B.V. All rights reserved.
Cecilia Diani; Giuliano Galimberti; Gabriele Soffritti
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/897816
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