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A Monte Carlo Study of Two Robust Alternatives of Least Square Regression Estimation

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  • Richard W. Hill
  • Paul W. Holland

Abstract

We give some Monte Carlo results on the performance of two robust alternatives to least squares regression estimation - least absolute residuals and the one-step "sine" estimator. We show how to scale the residuals for the sine estimator to achieve constant efficiency at the Gaussian across various choices of X-matrix and give some results for the contaminated Gaussian distribution.

Suggested Citation

  • Richard W. Hill & Paul W. Holland, 1974. "A Monte Carlo Study of Two Robust Alternatives of Least Square Regression Estimation," NBER Working Papers 0058, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:0058
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    References listed on IDEAS

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    1. Paul W. Holland, 1973. "Monte Carlo for Robust Regression: The Swindle Unmasked," NBER Working Papers 0010, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Various, 1975. "Staff Reports on Research Under Way," NBER Chapters, in: Understanding Economic Change, pages 9-120, National Bureau of Economic Research, Inc.
    2. Roy E. Welsch, 1975. "Confidence Regions for Robust Regression," NBER Working Papers 0111, National Bureau of Economic Research, Inc.

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    1. David C. Hoaglin, 1973. "Monte Carlo Techniques in Studying Robust Estimators," NBER Working Papers 0016, National Bureau of Economic Research, Inc.

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