  We consider estimation of a multivariate normal mean vector under sum of
squared error loss. We propose a new class of smooth estimators parameterized
by \alpha dominating the James-Stein estimator. The estimator for \alpha=1
corresponds to the generalized Bayes estimator with respect to the harmonic
prior. When \alpha goes to infinity, the estimator converges to the James-Stein
positive-part estimator. Thus the class of our estimators is a bridge between
the admissible estimator (\alpha=1) and the inadmissible estimator
(\alpha=\infty). Although the estimators have quasi-admissibility which is a
weaker optimality than admissibility, the problem of determining whether or not
the estimator for \alpha>1 admissible is still open.
