Abstract
A robust adaptive estimation procedure for location estimation problems is developed. Classification in this procedure is done on the basis of skewness and tailweight, using two statistics that are ratios of linear functions of sample order statistics. The associated estimators are of the general type known as M-estimators, Following the development of the adaptive location estimation procedure, an application to the k population selection problem is given. Monte Carlo results show the superiority of the adaptive procedure to the sample means procedure, the rank sum procedure, and the previously developed adaptive procedure of Randles, Ramberg, and Hogg.
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