Robust Group Identification and Variable Selection in Regression
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 d...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2017/2170816 |
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| _version_ | 1850179251698925568 |
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| author | Ali Alkenani Tahir R. Dikheel |
| author_facet | Ali Alkenani Tahir R. Dikheel |
| author_sort | Ali Alkenani |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9ed7bc3aaa724037a5eba2df452955e6 |
| institution | OA Journals |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-9ed7bc3aaa724037a5eba2df452955e62025-08-20T02:18:33ZengWileyJournal of Probability and Statistics1687-952X1687-95382017-01-01201710.1155/2017/21708162170816Robust Group Identification and Variable Selection in RegressionAli Alkenani0Tahir R. Dikheel1Department of Statistics, College of Administration and Economics, University of Al-Qadisiyah, Al Diwaniyah, IraqDepartment of Statistics, College of Administration and Economics, University of Al-Qadisiyah, Al Diwaniyah, IraqThe 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.http://dx.doi.org/10.1155/2017/2170816 |
| spellingShingle | Ali Alkenani Tahir R. Dikheel Robust Group Identification and Variable Selection in Regression Journal of Probability and Statistics |
| title | Robust Group Identification and Variable Selection in Regression |
| title_full | Robust Group Identification and Variable Selection in Regression |
| title_fullStr | Robust Group Identification and Variable Selection in Regression |
| title_full_unstemmed | Robust Group Identification and Variable Selection in Regression |
| title_short | Robust Group Identification and Variable Selection in Regression |
| title_sort | robust group identification and variable selection in regression |
| url | http://dx.doi.org/10.1155/2017/2170816 |
| work_keys_str_mv | AT alialkenani robustgroupidentificationandvariableselectioninregression AT tahirrdikheel robustgroupidentificationandvariableselectioninregression |