Citation
Abstract
The widely used least absolute deviation (LAD) estimator with the smoothly clipped absolute deviation (SCAD) penalty function (abbreviated as LAD-SCAD) is known to produce corrupt estimates in the presence of outlying observations. The problem becomes more complicated when the number of predictors diverges. To overcome these problems, the LAD-SCAD based on sure independence screening (SIS) technique is put forward. The SIS method uses the rank correlation screening (RCS) algorithm in the pre-screening step and the traditional Pathwise coordinate descent algorithm for computing the sequence of the regularization parameters in the post screening step for onward model selection. It is now evident that the rank correlation is less robust against outliers. Motivated by these inadequacies, we propose to improvise the LAD-SCAD estimator using robust wrapped correlation screening (WCS) method by replacing the rank correlation in the SIS method with robust wrapped correlation. The proposed estimator is denoted as WCS+LAD-SCAD and will be employed for variable selection. The simulation study and real-life data examples show that the proposed procedure produces more efficient results compared to the existing methods.
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Additional Metadata
Item Type: | Article |
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Divisions: | Faculty of Science Institute for Mathematical Research |
Publisher: | Universiti Putra Malaysia Press |
Keywords: | LAD-SCAD estimators; Robust screening; Ultrahigh dimensional data; Variable selection |
Depositing User: | Mohamad Jefri Mohamed Fauzi |
Date Deposited: | 10 Sep 2021 09:00 |
Last Modified: | 10 Sep 2021 09:00 |
URI: | http://psasir.upm.edu.my/id/eprint/90419 |
Statistic Details: | View Download Statistic |
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