REDI computes a robust linear regression based on Rousseeuw's REsistant DIagnostic described in P. J. Rousseeuw and A. M. Leroy, Robust Regression and Outlier Detection, Wiley, 1987, pp. 238-245. Like other "high-breakdown" estimators (including least median of squares and least trimmed squares), REDI detects and downweights outliers and leverage points which may distort the regression estimate. REDI is computed by a resampling scheme. The user chooses the number of subsamples by setting the option ITERATIONS. Its default value, 3000 subsamples, should be adequate for most applications. The user should consult the Rousseeuw-Leroy book for additional details.
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