On Conditional Applications of Matrix Variate Normal Distribution
On Conditional Applications of Matrix Variate Normal Distribution
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DOI：
10.7508/ijmsi.2010.02.004
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发表时间：
2010-01-01
影响因子：
0.5
通讯作者：
Tabatabaey, S. M. M.
中科院分区：
文献类型：
--
作者：
Iranmanesh, Anis;Arashi, M.;Tabatabaey, S. M. M.
本文以矩阵变量正态分布(MVND)为条件，研究了矩阵t-型族的构造问题，为研究这一族提供了一个新的视角。给出了一些重要的统计特性。所提出的t型族是Dickey工作的扩展[8]。在Kullback-Leibler发散损失下，给出了多维随机噪声分布的列协方差矩阵S的贝叶斯估计。在此基础上，给出了所提结果在多元线性模型的贝叶斯背景下的应用。证明了SEL和KLDL下系数矩阵的贝叶斯估计是相同的。
In this paper, by conditioning on the matrix variate normal distribution (MVND) the construction of the matrix t-type family is considered, thus providing a new perspective of this family. Some important statistical characteristics are given. The presented t-type family is an extension to the work of Dickey [8]. A Bayes estimator for the column covariance matrix S of MVND is derived under Kullback Leibler divergence loss (KLDL). Further an application of the proposed result is given in the Bayesian context of the multivariate linear model. It is illustrated that the Bayes estimators of coefficient matrix under both SEL and KLDL are identical.