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Modified Least Trimmed Quantile Regression to Overcome Effects of Leverage Points

Author

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  • Habshah Midi
  • Taha Alshaybawee
  • Mohammed Alguraibawi

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.

Suggested Citation

  • Habshah Midi & Taha Alshaybawee & Mohammed Alguraibawi, 2020. "Modified Least Trimmed Quantile Regression to Overcome Effects of Leverage Points," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, June.
    • Handle: RePEc:hin:jnlmpe:1243583
    DOI: 10.1155/2020/1243583
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    Cited by:

    1. Holt, Emily G. & Sunter, Deborah A., 2024. "National disparities in residential energy tax credits in the United States," Energy, Elsevier, vol. 300(C).

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