Modified Least Trimmed Quantile Regression to Overcome Effects of Leverage Points

Habshah Midi, Taha Alshaybawee and Mohammed Alguraibawi

Mathematical Problems in Engineering, 2020, vol. 2020, 1-13

Abstract:

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.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2020/1243583.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2020/1243583.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1243583

DOI: 10.1155/2020/1243583

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().