Robust Mixture Regression Based on the Mixture of Slash Distributions

DOI

10.2991/jsta.d.200304.001How to use a DOI?

Keywords

EM algorithm; Normal mixture regression; Outliers; Slash distribution

Abstract

The traditional estimation of Mixture regression models is based on the normal assumption of component errors and thus is sensitive to outliers and heavy-tailed errors. In this paper, we propose a robust Mixture regression models in which a mixture of slash distributions is assumed for the errors. Using the fact that the slash distribution can be written as a scale mixture of a normal and a latent distribution, we also estimate model parameters an expectation-maximization (EM) algorithm. The results of our simulation show that based on AIC and BIC criterion, the proposed robust regression model mixture on slash distribution dominates the robust regression based the normal and the t distribution. Finally, the proposed model is compared with other procedures, based on a real data set.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

TY  - JOUR
AU  - Hadi Saboori
AU  - Ghobad Barmalzan
AU  - Mahdi Doostparast
PY  - 2020
DA  - 2020/04/09
TI  - Robust Mixture Regression Based on the Mixture of Slash Distributions
JO  - Journal of Statistical Theory and Applications
SP  - 118
EP  - 132
VL  - 19
IS  - 2
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.d.200304.001
DO  - 10.2991/jsta.d.200304.001
ID  - Saboori2020
ER  -