摘要翻译:
本文基于种群特征值分块无限分散的渐近理论,在Wishart矩阵种群特征值的检验和区间估计问题上得到了一些新的实用结果。这种新的渐近理论是由作者Takemura和Sheena(2005)和Sheena和Takemura(2007a,b)发展起来的,并在决策理论框架中应用于总体协方差矩阵的点估计问题。在本文中,我们将它应用于一些测试和区间估计问题。我们证明了基于这类渐近性的逼近一般比传统的大样本渐近性好得多。
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英文标题:
《Inference on Eigenvalues of Wishart Distribution Using Asymptotics with
respect to the Dispersion of Population Eigenvalues》
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作者:
Yo Sheena and Akimichi Takemura
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最新提交年份:
2007
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分类信息:
一级分类:Mathematics 数学
二级分类:Statistics Theory 统计理论
分类描述:Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计:例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类:Statistics 统计学
二级分类:Statistics Theory 统计理论
分类描述:stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近,贝叶斯推论,决策理论,估计,基础,推论,检验。
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英文摘要:
In this paper we derive some new and practical results on testing and interval estimation problems for the population eigenvalues of a Wishart matrix based on the asymptotic theory for block-wise infinite dispersion of the population eigenvalues. This new type of asymptotic theory has been developed by 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 of population covariance matrix in a decision theoretic framework. In this paper we apply it to some testing and interval estimation problems. We show that the approximation based on this type of asymptotics is generally much better than the traditional large-sample asymptotics for the problems.
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PDF链接:
https://arxiv.org/pdf/704.2278