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Robust mixture regression estimation based on least : trimmed median method by using several models
Joint Authors
Hilmi, Nahid
Shaban, Batul
al-Jawhari, Mirfat
Source
Journal of Statistical Sciences
Issue
Vol. 2022, Issue 16 (30 Jun. 2022), pp.99-123, 25 p.
Publisher
Arab Institute for Training and Research in Statistics
Publication Date
2022-06-30
Country of Publication
Jordan
No. of Pages
25
Main Subjects
Abstract EN
In this paper we provide one of the robust mixture regression estimators, least trimmed median (LTM) method.
It is known that mixture regression models are used to investigate the relationship between variables that come from unknown latent groups and to model heterogenous datasets.
In general, the error terms are assumed to be normal in the mixture regression model.
However, the estimators under normality assumption are sensitive to outliers.
Therefore, we introduced a robust mixture regression procedure based on the LTM-estimation method to combat with the outliers in the data.
In this paper we handle LTM method by using three mixture regression models; Laplace, t and normal distributions.
A simulation study is applied to illustrate the performance of the proposed estimators over the counterparts in terms of dealing with outliers.
American Psychological Association (APA)
Shaban, Batul& Hilmi, Nahid& al-Jawhari, Mirfat. 2022. Robust mixture regression estimation based on least : trimmed median method by using several models. Journal of Statistical Sciences،Vol. 2022, no. 16, pp.99-123.
https://search.emarefa.net/detail/BIM-1427194
Modern Language Association (MLA)
Shaban, Batul…[et al.]. Robust mixture regression estimation based on least : trimmed median method by using several models. Journal of Statistical Sciences No. 16 (Jun. 2022), pp.99-123.
https://search.emarefa.net/detail/BIM-1427194
American Medical Association (AMA)
Shaban, Batul& Hilmi, Nahid& al-Jawhari, Mirfat. Robust mixture regression estimation based on least : trimmed median method by using several models. Journal of Statistical Sciences. 2022. Vol. 2022, no. 16, pp.99-123.
https://search.emarefa.net/detail/BIM-1427194
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 122-123
Record ID
BIM-1427194