Approximate Minimax Estimation of a Bounded Normal Mean via Stochastic Mirror Ascent
用随机镜像上升近似极小极大估计有界正态均值
This paper presents a computational approach to find an approximately minimax estimator for the classical Bounded Normal Mean problem. The suggested procedure is the Bayes estimator corresponding to an approximately least-favorable distribution obtained from a stochastic mirror ascent routine for concave maximization. The paper shows that both the approximately least-favorable distribution and the approximately minimax estimator are indeed close (in a sense we make precise)…