Combining Minimax Shrinkage Estimators
本文提出新的极小化极大多重收缩估计量，允许对坐标分组和收缩目标进行多种设定，并通过真实与模拟数据评估了自适应组合相关估计问题和自适应聚类收缩估计量的表现。
Abstract When one estimates a multivariate normal mean, the use of Stein estimation entails both the grouping of coordinates and the selection of a set of targets toward which to shrink each group. In this article we propose new minimax multiple shrinkage estimators that allow for multiple specifications of these aspects. We provide examples that are evaluated on real and simulated data, including an estimator that adaptively resolves the issue of combining possibly related estimation problems and an adaptive clustering shrinkage estimator. The construction and properties of these estimators are shown to follow from the application of the multiple shrinkage results of George (1986a) to general partitioned shrinkage estimators.