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Matrix Euclidean norm Wielandt inequalities and their applications to statistics

机译:矩阵欧几里得范Wielandt不等式及其在统计中的应用

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摘要

In this paper we shall establish a new matrix inequality which will fill the gap that there has not been any matrix Euclidean norm version of the Wielandt inequality in the literature yet. This inequality can be used to present an upper bound of a new measure of association which plays a very important role in statistics, especially in multivariate analysis. A new alternative based on Euclidean norm for relative gain of the covariance adjusted estimator of parameters is provided.
机译:在本文中,我们将建立一个新的矩阵不等式,以填补文献中尚未有Wielandt不等式的任何矩阵欧几里得范数版本的空白。这个不等式可以用来表示一种新的关联度量的上限,该度量在统计中,尤其是在多元分析中起着非常重要的作用。提供了一种基于欧几里得范数的新替代方法,用于参数的协方差调整估计量的相对增益。

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