Modified Least Trimmed Quantile Regression to Overcome Effects of Leverage Points

被引：0

作者：

Midi, Habshah ^{[1
]}

Alshaybawee, Taha ^{[1
]}

Alshaybawee, Taha ^{[2
]}

Alguraibawi, Mohammed ^{[3
]}

机构：

[1] Midi, Habshah

[2] Alshaybawee, Taha

[3] Alshaybawee, Taha

[4] Alguraibawi, Mohammed

来源：

Midi, Habshah (habshahmidi@gmail.com) | 1600年 / Hindawi Limited, 410 Park Avenue, 15th Floor, 287 pmb, New York, NY 10022, United States卷 / 2020期

关键词：

Least squares approximations - Regression analysis;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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. © 2020 Habshah Midi et al.

引用

共 50 条

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