The purpose of this paper is to present robust estimates for the regression parameters in the general linear model. We start with a family of $M$-estimators, and using the observations, we estimate the asymptotic efficiency of each member in the family. Then we choose the estimate in the family with greatest estimated asymptotical efficiency. We prove that this procedure has the same asymptotical efficiency as the member of the family with the greatest asymptotical efficiency for the unknown distribution of the error.