Multivariate cluster-weighted models based on seemingly unrelated linear regression

Cecilia Diani, Giuliano Galimberti and Gabriele Soffritti

Computational Statistics & Data Analysis, 2022, vol. 171, issue C

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.

Keywords: Cluster analysis; ECM algorithm; Gaussian mixture model; Multivariate linear regression; Parsimonious model (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:171:y:2022:i:c:s0167947322000317

DOI: 10.1016/j.csda.2022.107451

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