A Monte Carlo Study of Two Robust Alternatives of Least Square Regression Estimation
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.
Published Versions
Hill, Richard W. and Paul W. Holland. "Two Robust Alternatives To Least-Squared Regression," Journal of the American Statistical Association, 1977, v72(360), 828-833.
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