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Simultaneous equivariant estimation of the parameters of matrix scale and matrix location-scale models

机译:矩阵比例尺和矩阵位置比例尺模型的参数同时等变估计

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Eaton and Olkin (1987) discussed the problem of best equivariant estimator of the matrix scale parameter with respect to different scalar loss functions. Edwin Prabakaran and Chandrasekar (1994) developed simultaneous equivariant estimation approach and illustrated the method with examples. The problems considered in this paper are simultaneous equivariant estimation of the parameters of (i) a matrix scale model and (ii) a multivariate location-scale model. By considering matrix loss function (Klebanov, Linnik and Ruhin, 1971) a characterization of matrix minimum risk equivariant (MMRE) estimator of the matrix parameter is obtained in each case. Illustrative examples are provided in which MMRE estimators are obtained with respect to two matrix loss functions.
机译:Eaton和Olkin(1987)讨论了关于不同标量损失函数的矩阵尺度参数的最佳等变估计问题。埃德温·普拉巴卡兰(Edwin Prabakaran)和钱德拉塞卡(Chandrasekar)(1994)开发了同时等变估计方法,并通过实例说明了该方法。本文考虑的问题是(i)矩阵比例模型和(ii)多元位置比例模型的参数同时等变估计。通过考虑矩阵损失函数(Klebanov,Linnik和Ruhin,1971),可以在每种情况下获得矩阵参数的矩阵最小风险等方(MMRE)估计量的特征。提供了说明性示例,其中针对两个矩阵损失函数获得了MMRE估计器。

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