摘要翻译：
本文给出了多元正规均值收缩估计的均方误差的非负估计和正估计。在二次损失准则下，所提出的估计对一致最小方差无偏估计(UMVUE)进行了改进。对于收缩估计的MSE矩阵的估计也得到了类似的改进。我们还应用所提出的MSE矩阵的估计量来形成以收缩估计量为中心的置信度集，并通过数值实验证明了它们的有效性。
---
英文标题：
《Improved estimation of the MSEs and the MSE matrices for shrinkage
  estimators of multivariate normal means and their applications》
---
作者：
Hisayuki Hara
---
最新提交年份：
2007
---
分类信息：

一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
--
一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
--

---
英文摘要：
  In this article we provide some nonnegative and positive estimators of the mean squared errors(MSEs) for shrinkage estimators of multivariate normal means. Proposed estimators are shown to improve on the uniformly minimum variance unbiased estimator(UMVUE) under a quadratic loss criterion. A similar improvement is also obtained for the estimators of the MSE matrices for shrinkage estimators. We also apply the proposed estimators of the MSE matrix to form confidence sets centered at shrinkage estimators and show their usefulness through numerical experiments.
---
PDF链接：
https://arxiv.org/pdf/710.1171