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LAD Regression for Detecting Outliers in Response and Explanatory Variables

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  • Dodge, Yadolah
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    Abstract

    Least absolute deviations regression resists outliers in the response variable but is relatively sensitive to outlying observations in the explanatory variables. In this paper a simple solution is proposed to overcome this problem. This is achieved by minimizing the absolute values of vertical and horizontal deviations in turn. Two algorithms are proposed: one for the simple and one for the multiple regression case. The methods presented have been tested on a variety of data and have proven to be quite effective.

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    File URL: http://www.sciencedirect.com/science/article/B6WK9-45K12X4-9/2/661144c5850a34581ec382a3bf6c694a
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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 61 (1997)
    Issue (Month): 1 (April)
    Pages: 144-158

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    Handle: RePEc:eee:jmvana:v:61:y:1997:i:1:p:144-158

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    Related research

    Keywords: least absolute deviations regression inverse least absolute deviations regression robust regression outliers leverage points;

    References

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    1. Dielman, Terry E. & Rose, Elizabeth L., 1996. "A note on hypothesis testing in LAV multiple regression: A small sample comparison," Computational Statistics & Data Analysis, Elsevier, vol. 21(4), pages 463-470, April.
    2. Dielman, Terry E. & Rose, Elizabeth L., 1995. "A bootstrap approach to hypothesis testing in least absolute value regression," Computational Statistics & Data Analysis, Elsevier, vol. 20(2), pages 119-130, August.
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    Cited by:
    1. Sun, Rui-Bo & Wei, Bo-Cheng, 2004. "On influence assessment for LAD regression," Statistics & Probability Letters, Elsevier, vol. 67(2), pages 97-110, April.
    2. Yadolah Dodge & Ali Hadi, 1999. "Simple graphs and bounds for the elements of the hat matrix," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(7), pages 817-823.

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