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Robust Group Identification and Variable Selection in Regression
Joint Authors
Alkenani, Ali
Dikheel, Tahir R.
Source
Journal of Probability and Statistics
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-20
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
The elimination of insignificant predictors and the combination of predictors with indistinguishable coefficients are the two issues raised in searching for the true model.
Pairwise Absolute Clustering and Sparsity (PACS) achieves both goals.
Unfortunately, PACS is sensitive to outliers due to its dependency on the least-squares loss function which is known to be very sensitive to unusual data.
In this article, the sensitivity of PACS to outliers has been studied.
Robust versions of PACS (RPACS) have been proposed by replacing the least squares and nonrobust weights in PACS with MM-estimation and robust weights depending on robust correlations instead of person correlation, respectively.
A simulation study and two real data applications have been used to assess the effectiveness of the proposed methods.
American Psychological Association (APA)
Alkenani, Ali& Dikheel, Tahir R.. 2017. Robust Group Identification and Variable Selection in Regression. Journal of Probability and Statistics،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1186259
Modern Language Association (MLA)
Alkenani, Ali& Dikheel, Tahir R.. Robust Group Identification and Variable Selection in Regression. Journal of Probability and Statistics No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1186259
American Medical Association (AMA)
Alkenani, Ali& Dikheel, Tahir R.. Robust Group Identification and Variable Selection in Regression. Journal of Probability and Statistics. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1186259
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1186259