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 / Cecilia Diani; Giuliano Galimberti; Gabriele Soffritti. - In: COMPUTATIONAL STATISTICS & DATA ANALYSIS. - ISSN 0167-9473. - ELETTRONICO. - 171:(2022), pp. 107451.1-107451.24. [10.1016/j.csda.2022.107451]

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.
2022
Multivariate cluster-weighted models based on seemingly unrelated linear regression / Cecilia Diani; Giuliano Galimberti; Gabriele Soffritti. - In: COMPUTATIONAL STATISTICS & DATA ANALYSIS. - ISSN 0167-9473. - ELETTRONICO. - 171:(2022), pp. 107451.1-107451.24. [10.1016/j.csda.2022.107451]
Cecilia Diani; Giuliano Galimberti; Gabriele Soffritti
File in questo prodotto:
Eventuali allegati, non sono esposti

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/897816
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact