In this paper we derive some new and practical results on testing andinterval estimation problems for the population eigenvalues of a Wishart matrixbased on the asymptotic theory for block-wise infinite dispersion of thepopulation eigenvalues. This new type of asymptotic theory has been developedby the present authors in Takemura and Sheena (2005) and Sheena and Takemura(2007a,b) and in these papers it was applied to point estimation problem ofpopulation covariance matrix in a decision theoretic framework. In this paperwe apply it to some testing and interval estimation problems. We show that theapproximation based on this type of asymptotics is generally much better thanthe traditional large-sample asymptotics for the problems.
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机译:在本文中,我们基于种群特征值的块状无限分散的渐近理论,得出了有关Wishart矩阵特征值的测试和区间估计问题的一些新的实用结果。这种新的渐近理论是由作者在Takemura and Sheena(2005)和Sheena and Takemura(2007a,b)开发的,并在这些论文中被用于决策理论框架中的人口协方差矩阵的点估计问题。在本文中,我们将其应用于一些测试和区间估计问题。我们表明,基于这种渐近性的逼近通常比传统的大样本渐近性好得多。
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