Robust multivariate least angle regression
The least angle regression selection (LARS) algorithms that use the classical sample means, variances, and correlations between the original variables are very sensitive to the presence of outliers and other contamination. To remedy this problem, a simple modification of this algorithm is to replace...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Science Society of Thailand
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/63147/1/Robust%20multivariate%20least%20angle%20regression.pdf |
| _version_ | 1825932555835146240 |
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| author | Uraibi, Hassan Sami Midi, Habshah Rana, Sohel |
| author_facet | Uraibi, Hassan Sami Midi, Habshah Rana, Sohel |
| author_sort | Uraibi, Hassan Sami |
| collection | UPM |
| description | The least angle regression selection (LARS) algorithms that use the classical sample means, variances, and correlations between the original variables are very sensitive to the presence of outliers and other contamination. To remedy this problem, a simple modification of this algorithm is to replace the non-robust estimates with their robust counterparts. Khan, Van Aelst, and Zamar employed the robust correlation for winsorized data based on adjusted winsorization correlation as a robust bivariate correlation approach for plug-in LARS. However, the robust least angle regression selection has some drawbacks in the presence of multivariate outliers. We propose to incorporate the Olive and Hawkins reweighted and fast consistent high breakdown estimator into the robust plug-in LARS method based on correlations. Our proposed method is tested by using a numerical example and a simulation study. |
| first_indexed | 2024-03-06T09:43:50Z |
| format | Article |
| id | upm.eprints-63147 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:43:50Z |
| publishDate | 2017 |
| publisher | Science Society of Thailand |
| record_format | dspace |
| spelling | upm.eprints-631472018-09-28T03:26:25Z http://psasir.upm.edu.my/id/eprint/63147/ Robust multivariate least angle regression Uraibi, Hassan Sami Midi, Habshah Rana, Sohel The least angle regression selection (LARS) algorithms that use the classical sample means, variances, and correlations between the original variables are very sensitive to the presence of outliers and other contamination. To remedy this problem, a simple modification of this algorithm is to replace the non-robust estimates with their robust counterparts. Khan, Van Aelst, and Zamar employed the robust correlation for winsorized data based on adjusted winsorization correlation as a robust bivariate correlation approach for plug-in LARS. However, the robust least angle regression selection has some drawbacks in the presence of multivariate outliers. We propose to incorporate the Olive and Hawkins reweighted and fast consistent high breakdown estimator into the robust plug-in LARS method based on correlations. Our proposed method is tested by using a numerical example and a simulation study. Science Society of Thailand 2017 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/63147/1/Robust%20multivariate%20least%20angle%20regression.pdf Uraibi, Hassan Sami and Midi, Habshah and Rana, Sohel (2017) Robust multivariate least angle regression. ScienceAsia, 43 (1). pp. 56-60. ISSN 1513-1874 http://www.scienceasia.org/content/viewabstract.php?ms=7066&v=56&abst=1 10.2306/scienceasia1513-1874.2017.43.056 |
| spellingShingle | Uraibi, Hassan Sami Midi, Habshah Rana, Sohel Robust multivariate least angle regression |
| title | Robust multivariate least angle regression |
| title_full | Robust multivariate least angle regression |
| title_fullStr | Robust multivariate least angle regression |
| title_full_unstemmed | Robust multivariate least angle regression |
| title_short | Robust multivariate least angle regression |
| title_sort | robust multivariate least angle regression |
| url | http://psasir.upm.edu.my/id/eprint/63147/1/Robust%20multivariate%20least%20angle%20regression.pdf |
| work_keys_str_mv | AT uraibihassansami robustmultivariateleastangleregression AT midihabshah robustmultivariateleastangleregression AT ranasohel robustmultivariateleastangleregression |