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A method is presented based on augmented data sets for combining biased and robust regression techniques. The estimates are constrained robust estimates, using an appropriately chosen ridge, shrinkage or principal components constraint. Examples are provided to illustrate the ability of the procedure to shrink the estimated coefficients and to automatically detect and discount the effects of observations with large random error components.
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