Minimax Estimators for the Location Vectors of Spherically Symmetric Densities
研究了在二次损失下估计K个位置参数（K≥3）时，当最佳不变估计的坐标呈球对称分布时，传统Stein估计在有限样本下的风险表现优于某些常规方法。
The estimation of K ( K ≥ 3) location parameters is considered under quadratic loss when the coordinates of the best invariant estimators are spherically symmetrically distributed. Under these stochastic mechanisms traditional Stein estimators are evaluated for finite samples and shown to have a risk performance superior to some conventional rules.