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Advances in Data Analysis and Classification

Erschienen in:

23.03.2022 | Regular Article

Robust mixture regression modeling based on two-piece scale mixtures of normal distributions

verfasst von: Atefeh Zarei, Zahra Khodadadi, Mohsen Maleki, Karim Zare

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 1/2023

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Abstract

The inference of mixture regression models (MRM) is traditionally based on the normal (symmetry) assumption of component errors and thus is sensitive to outliers or symmetric/asymmetric lightly/heavy-tailed errors. To deal with these problems, some new mixture regression models have been proposed recently. In this paper, a general class of robust mixture regression models is presented based on the two-piece scale mixtures of normal (TP-SMN) distributions. The proposed model is so flexible that can simultaneously accommodate asymmetry and heavy tails. The stochastic representation of the proposed model enables us to easily implement an EM-type algorithm to estimate the unknown parameters of the model based on a penalized likelihood. In addition, the performance of the considered estimators is illustrated using a simulation study and a real data example.

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Titel: Robust mixture regression modeling based on two-piece scale mixtures of normal distributions
verfasst von: Atefeh Zarei
Zahra Khodadadi
Mohsen Maleki
Karim Zare
Publikationsdatum: 23.03.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: Advances in Data Analysis and Classification / Ausgabe 1/2023
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI: https://doi.org/10.1007/s11634-022-00495-6

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