Error bounds for asymptotic approximations of the linear discriminant function when the sample sizes and dimensionality are large
Error bounds for asymptotic approximations of the linear discriminant function when the sample sizes and dimensionality are large
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DOI：
10.1006/jmva.1999.1862
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发表时间：
2000-04-01
影响因子：
1.6
通讯作者：
Fujikoshi, Y
中科院分区：
文献类型：
--
作者：
Fujikoshi, Y
本文研究了当线性判别函数用于将一个观测值分类为来自两个具有共同协方差矩阵的多元正态总体之一时，期望误分类概率（EPMC）的渐近近似的理论精度。所考虑的渐近近似是在样本容量和维数都很大的情况下。我们给出了明确的误差界的渐近逼近的EPMC，一般的近似结果的基础上。本文还讨论了EPMC的渐近展开式及其误差界的获得方法。(C)北京大学出版社.
Theoretical accuracies are studied For asymtotic approximations of the expected probabilities of misclassification (EPMC) when the linear discriminant function is used to classify an observation as coining from one of two multivariate normal populations with a common covariance matrix. The asymptotic approximations considered are the ones under the situation where both the sample sizes and the demensionality are large. We give explicit error bounds for asymptotic approximations of EPMC, based on a general approximation result. We also discuss with a method of obtaining asymptotic expansions for EPMC and their error bounds. (C) 2000 Academic Press.