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A note on the consistency of the maximum likelihood estimator under multivariate linear cluster-weighted models

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  • Galimberti, Giuliano
  • Soffritti, Gabriele

Abstract

This letter illustrates simple assumptions for proving consistency of the maximum likelihood estimator under multivariate Gaussian and Student’s t linear cluster-weighted models, which allow density estimation, clustering and linear regression analysis with continuous random predictors in presence of unobserved heterogeneity.

Suggested Citation

  • Galimberti, Giuliano & Soffritti, Gabriele, 2020. "A note on the consistency of the maximum likelihood estimator under multivariate linear cluster-weighted models," Statistics & Probability Letters, Elsevier, vol. 157(C).
    • Handle: RePEc:eee:stapro:v:157:y:2020:i:c:s0167715219302767
    DOI: 10.1016/j.spl.2019.108630
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    Cited by:

    1. Michael P. B. Gallaugher & Salvatore D. Tomarchio & Paul D. McNicholas & Antonio Punzo, 2022. "Multivariate cluster weighted models using skewed distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(1), pages 93-124, March.

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