Modified least trimmed quantile regression to overcome effects of leverage points
Quantile regression estimates are robust for outliers in y direction but are sensitive to leverage points. The least trimmed quantile regression (LTQReg) method is put forward to overcome the effect of leverage points. The LTQReg method trims higher residuals based on trimming percentage specified b...
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| Format: | Article |
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Hindawi
2020
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| _version_ | 1825952266331357184 |
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| author | Midi, Habshah Alshaybawee, Taha Alguraibawi, Mohammed |
| author_facet | Midi, Habshah Alshaybawee, Taha Alguraibawi, Mohammed |
| author_sort | Midi, Habshah |
| collection | UPM |
| description | Quantile regression estimates are robust for outliers in y direction but are sensitive to leverage points. The least trimmed quantile regression (LTQReg) method is put forward to overcome the effect of leverage points. The LTQReg method trims higher residuals based on trimming percentage specified by the data. However, leverage points do not always produce high residuals, and hence, the trimming percentage should be specified based on the ratio of contamination, not determined by a researcher. In this paper, we propose a modified least trimmed quantile regression method based on reweighted least trimmed squares. Robust Mahalanobis’ distance and GM6 weights based on Gervini and Yohai’s (2003) cutoff points are employed to determine the trimming percentage and to detect leverage points. A simulation study and real data are considered to investigate the performance of our proposed methods. |
| first_indexed | 2024-03-06T10:42:21Z |
| format | Article |
| id | upm.eprints-86805 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T10:42:21Z |
| publishDate | 2020 |
| publisher | Hindawi |
| record_format | dspace |
| spelling | upm.eprints-868052023-01-13T09:00:42Z http://psasir.upm.edu.my/id/eprint/86805/ Modified least trimmed quantile regression to overcome effects of leverage points Midi, Habshah Alshaybawee, Taha Alguraibawi, Mohammed Quantile regression estimates are robust for outliers in y direction but are sensitive to leverage points. The least trimmed quantile regression (LTQReg) method is put forward to overcome the effect of leverage points. The LTQReg method trims higher residuals based on trimming percentage specified by the data. However, leverage points do not always produce high residuals, and hence, the trimming percentage should be specified based on the ratio of contamination, not determined by a researcher. In this paper, we propose a modified least trimmed quantile regression method based on reweighted least trimmed squares. Robust Mahalanobis’ distance and GM6 weights based on Gervini and Yohai’s (2003) cutoff points are employed to determine the trimming percentage and to detect leverage points. A simulation study and real data are considered to investigate the performance of our proposed methods. Hindawi 2020-06-12 Article PeerReviewed Midi, Habshah and Alshaybawee, Taha and Alguraibawi, Mohammed (2020) Modified least trimmed quantile regression to overcome effects of leverage points. Mathematical Problems in Engineerin, 2020. art. no. 1243583. pp. 1-13. ISSN 1024-123X; ESSN: 1563-5147 https://www.hindawi.com/journals/mpe/2020/1243583/ 10.1155/2020/1243583 |
| spellingShingle | Midi, Habshah Alshaybawee, Taha Alguraibawi, Mohammed Modified least trimmed quantile regression to overcome effects of leverage points |
| title | Modified least trimmed quantile regression to overcome effects of leverage points |
| title_full | Modified least trimmed quantile regression to overcome effects of leverage points |
| title_fullStr | Modified least trimmed quantile regression to overcome effects of leverage points |
| title_full_unstemmed | Modified least trimmed quantile regression to overcome effects of leverage points |
| title_short | Modified least trimmed quantile regression to overcome effects of leverage points |
| title_sort | modified least trimmed quantile regression to overcome effects of leverage points |
| work_keys_str_mv | AT midihabshah modifiedleasttrimmedquantileregressiontoovercomeeffectsofleveragepoints AT alshaybaweetaha modifiedleasttrimmedquantileregressiontoovercomeeffectsofleveragepoints AT alguraibawimohammed modifiedleasttrimmedquantileregressiontoovercomeeffectsofleveragepoints |