Abstract
We define a robust procedure to “correct” a regression estimate β ̂ 0 along the directions in predictor space where the fit is worse. When β ̂ 0 is the least median of squares estimate, the “corrected estimate” has a smaller maximum asymptotic bias under contamination, and a much better finite-sample behavior than β ̂ 0.
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