Improved nonparametric estimation of location vectors in multivariate regression models

Nonparametric estimation of the location parameter vector is considered when uncertain prior information (UPI) about the regression parameters is available. The asymptotic properties of shrinkage and preliminary test estimators using quadratic loss function are appraised. It is demonstrated that the positive-rule estimator asymptotically dominates the usual Stein-type estimator. However, both shrinkage estimators are superior to the usual estimators. The relative dominance picture of the estimators is presented analytically as well as graphically.