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Advances in Data Analysis and Classification
Erschienen in: Advances in Data Analysis and Classification 1/2022

15.11.2021 | Regular Article

Multivariate cluster weighted models using skewed distributions

verfasst von: Michael P. B. Gallaugher, Salvatore D. Tomarchio, Paul D. McNicholas, Antonio Punzo

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 1/2022

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Abstract

Much work has been done in the area of the cluster weighted model (CWM), which extends the finite mixture of regression model to include modelling of the covariates. Although many types of distributions have been considered for both the response(s) and covariates, to our knowledge skewed distributions have not yet been considered in this paradigm. Herein, a family of 24 novel CWMs is considered which allows both the responses and covariates to be modelled using one of four skewed distributions (the generalized hyberbolic and three of its skewed special cases, i.e., the skew-t, the variance-gamma and the normal-inverse Gaussian distributions) or the normal distribution. Parameter estimation is performed using the expectation-maximization algorithm and both simulated and real data are used for illustration.
Vorheriger Artikel Unobserved classes and extra variables in high-dimensional discriminant analysis
Nächster Artikel Strong consistency of the MLE under two-parameter Gamma mixture models with a structural scale parameter

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Metadaten
Titel
Multivariate cluster weighted models using skewed distributions
verfasst von
Michael P. B. Gallaugher
Salvatore D. Tomarchio
Paul D. McNicholas
Antonio Punzo
Publikationsdatum
15.11.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Advances in Data Analysis and Classification / Ausgabe 1/2022
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI
https://doi.org/10.1007/s11634-021-00480-5

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