Admissible Estimator of the Eigenvalues of Variance-Covariance Matrix for Multivariate Normal Distributions--Detailed Proof--
Yo Sheena, Akimichi Takemura
在尺度不变平方误差损失下，给出了多元正态分布的方差-协方差阵特征值的一个可容许估计。
An admissible estimator of the eigenvalues of the variance-covariance matrix is given for multivariate normal distributions with respect to the scale-invariant squared error loss.
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研究非正态模型的非线性收缩协方差估计，提出无边际非线性收缩估计器MENS，给出其经验谱分布收敛性及渐近最优性，建立相变，通过模拟和回测证实理论，展示其在高维配置和决策中的实用价值。
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本文提出一种基于谱随机积分的多元Matérn协方差模型扩展方法，解决了协方差结构有效性问题，允许非对称协方差与局部结构灵活建模，并通过环境数据验证了其捕捉协方差非对称现象的有效性。
In this paper, we address the problem of estimating a covariance matrix of a multivariate Gaussian distribution, relative to a Stein loss function, from a decision theoretic point of view. We investigate the case where the covariance matrix is invertible and the case when it is non--invertible in a unified approach.
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