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Intervals Which Leave the Minimum Sum of Absolute Errors Regression Unchanged

Author

Listed:
  • Subhash C. Narula
  • Vince A. Sposito
  • John F. Wellington

Abstract

One of the appealing properties of the minimum sum of absolute errors (MSAE) regression is its resistance to outliers and long‐tailed error distributions. Just like the sample median, the MSAE estimators are influenced by all the observations but determined by only a subset of the observations. The MSAE estimates are not altered if the value of the response (or predictor) variable for an observation associated with a non‐O residual is within a certain interval. In this paper we develop procedures to determine such intervals for the simple linear regression model.

Suggested Citation

  • Subhash C. Narula & Vince A. Sposito & John F. Wellington, 1993. "Intervals Which Leave the Minimum Sum of Absolute Errors Regression Unchanged," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 42(2), pages 369-378, June.
    • Handle: RePEc:bla:jorssc:v:42:y:1993:i:2:p:369-378
    DOI: 10.2307/2986239
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    Cited by:

    1. Chang, Xiao-Wen & Qu, Leming, 2005. "Erratum to "Wavelet estimation of partially linear models" [Computational Statistics and Data Analysis 47/1 (2004) 31-48]," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 677-677, April.
    2. Narula, Subhash C. & Wellington, John F., 2002. "Sensitivity analysis for predictor variables in the MSAE regression," Computational Statistics & Data Analysis, Elsevier, vol. 40(2), pages 355-373, August.

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