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Multivariate cluster-weighted models based on seemingly unrelated linear regression

Author

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  • Diani, Cecilia
  • Galimberti, Giuliano
  • Soffritti, Gabriele

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.

Suggested Citation

  • Diani, Cecilia & Galimberti, Giuliano & Soffritti, Gabriele, 2022. "Multivariate cluster-weighted models based on seemingly unrelated linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    • Handle: RePEc:eee:csdana:v:171:y:2022:i:c:s0167947322000317
    DOI: 10.1016/j.csda.2022.107451
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