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Diagnostic Robust Generalized Potential Based on Index Set Equality (DRGP (ISE)) for the identification of high leverage points in linear model

Author

Listed:
  • Hock Ann Lim

    (Infrastructure University Kuala Lumpur)

  • Habshah Midi

    (University Putra Malaysia)

Abstract

High leverage points have tremendous effect in linear regression analysis. When a group of high leverage points is present in a dataset, the existing detection methods fail to detect them correctly. This problem is due to the masking and swamping effects. We propose the Diagnostic Robust Generalized Potentials Based on Index Set Equality (DRGP(ISE)) in this regard. The DRGP(ISE) takes off from the Diagnostic Robust Generalized Potential Based on Minimum Volume Ellipsoid (DRGP(MVE)). However, the running time of ISE is much faster than MVE. Monte Carlo simulation study and numerical data indicate that DRGP(ISE) works excellently to detect the actual high leverage points and reduce masking and swamping effects in a linear model.

Suggested Citation

  • Hock Ann Lim & Habshah Midi, 2016. "Diagnostic Robust Generalized Potential Based on Index Set Equality (DRGP (ISE)) for the identification of high leverage points in linear model," Computational Statistics, Springer, vol. 31(3), pages 859-877, September.
    • Handle: RePEc:spr:compst:v:31:y:2016:i:3:d:10.1007_s00180-016-0662-6
    DOI: 10.1007/s00180-016-0662-6
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    References listed on IDEAS

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    1. Sudhanshu Kumar MISHRA, 2008. "A New Method Of Robust Linear Regression Analysis: Some Monte Carlo Experiments," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 3(3(5)_Fall), pages 261-268.
    2. M. Habshah & M. R. Norazan & A.H.M. Rahmatullah Imon, 2009. "The performance of diagnostic-robust generalized potentials for the identification of multiple high leverage points in linear regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(5), pages 507-520.
    3. Hadi, Ali S., 1992. "A new measure of overall potential influence in linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 14(1), pages 1-27, June.
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