Minimaxity of empirical bayes estimators shrinking toward the grand mean when variances are unequal
Minimaxity of empirical bayes estimators shrinking toward the grand mean when variances are unequal
复制标题
当方差不相等时，经验贝叶斯估计量的极小极大值向总均值收缩
DOI：
10.1080/03610929608831687
复制
发表时间：
1996
影响因子：
0.8
通讯作者：
Yuan
中科院分区：
文献类型：
--
作者：
Nobuo Shinozaki;Yuan
多元正态均值的经验贝叶斯估计是考虑当成分是独立的，具有不等的方差。给出了估计量在和下极大极小的一个充分条件。当共同先验分布为均值和方差未知的正态分布时的平方误差损失函数。对于向某些回归估计收缩的估计量，也给出了类似的结果。
Empirical Bayes estimators of a multivariate normal mean are considered when the components are independent and have unequal variances. A sufficient condition is given for the estimators to be minimax under sum. of squared error loss function when the common prior distribution is given by normal one with unkown mean and variance. A similar result is also given for the estimators which shrink toward some regression estimate.