Group Identification and Variable Selection in Quantile Regression
Using the Pairwise Absolute Clustering and Sparsity (PACS) penalty, we proposed the regularized quantile regression QR method (QR-PACS). The PACS penalty achieves the elimination of insignificant predictors and the combination of predictors with indistinguishable coefficients (IC), which are the two...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2019/8504174 |
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| _version_ | 1832568192652804096 |
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| author | Ali Alkenani Basim Shlaibah Msallam |
| author_facet | Ali Alkenani Basim Shlaibah Msallam |
| author_sort | Ali Alkenani |
| collection | DOAJ |
| description | Using the Pairwise Absolute Clustering and Sparsity (PACS) penalty, we proposed the regularized quantile regression QR method (QR-PACS). The PACS penalty achieves the elimination of insignificant predictors and the combination of predictors with indistinguishable coefficients (IC), which are the two issues raised in the searching for the true model. QR-PACS extends PACS from mean regression settings to QR settings. The paper shows that QR-PACS can yield promising predictive precision as well as identifying related groups in both simulation and real data. |
| format | Article |
| id | doaj-art-60a11ace0ef84f418bb84d15c96042b4 |
| institution | Kabale University |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-60a11ace0ef84f418bb84d15c96042b42025-02-03T00:59:35ZengWileyJournal of Probability and Statistics1687-952X1687-95382019-01-01201910.1155/2019/85041748504174Group Identification and Variable Selection in Quantile RegressionAli Alkenani0Basim Shlaibah Msallam1Department of Statistics, College of Administration and Economics, University of Al-Qadisiyah, IraqSupervision & Scientific Evaluation Office, Ministry of Higher Education and Scientific Research, IraqUsing the Pairwise Absolute Clustering and Sparsity (PACS) penalty, we proposed the regularized quantile regression QR method (QR-PACS). The PACS penalty achieves the elimination of insignificant predictors and the combination of predictors with indistinguishable coefficients (IC), which are the two issues raised in the searching for the true model. QR-PACS extends PACS from mean regression settings to QR settings. The paper shows that QR-PACS can yield promising predictive precision as well as identifying related groups in both simulation and real data.http://dx.doi.org/10.1155/2019/8504174 |
| spellingShingle | Ali Alkenani Basim Shlaibah Msallam Group Identification and Variable Selection in Quantile Regression Journal of Probability and Statistics |
| title | Group Identification and Variable Selection in Quantile Regression |
| title_full | Group Identification and Variable Selection in Quantile Regression |
| title_fullStr | Group Identification and Variable Selection in Quantile Regression |
| title_full_unstemmed | Group Identification and Variable Selection in Quantile Regression |
| title_short | Group Identification and Variable Selection in Quantile Regression |
| title_sort | group identification and variable selection in quantile regression |
| url | http://dx.doi.org/10.1155/2019/8504174 |
| work_keys_str_mv | AT alialkenani groupidentificationandvariableselectioninquantileregression AT basimshlaibahmsallam groupidentificationandvariableselectioninquantileregression |