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Two Preliminary Tests for Discriminant Analysis

机译:判别分析的两个初步测试

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摘要

The object of discriminant analysis is to allocate a new individual to one of many a priori known populations. The preliminary test concerns the question whether or not the new individual comes from a new population. Two tests corresponding to such a problem are given in this article; they are developed by the approach proposed by Bar-Hen (1996) in which the asymptotic distribution of the vector of estimated distances between populations play the crucial role. The first test is designed for heteroscedastic multivariate normal populations and utilizes the Bhattacharyya distance, while the second test uses the Mahalanobis distance and is suitable for the homoscedastic case. Although establishing the critical value for a given null hypothesis and chosen significance level requires a solution of multi-integral equation, it can be found simply through Monte Carlo simulations. The performance of the tests is illustrated by some agriculture data resulting from a trial on distinctness among certain varieties of maize.
机译:判别分析的目的是将一个新个体分配给许多先验已知种群之一。初步测试涉及新个人是否来自新人群的问题。本文给出了两个与该问题相对应的测试;它们是由Bar-Hen(1996)提出的方法开发的,其中人口之间估计距离的向量的渐近分布起着至关重要的作用。第一个测试是针对异方差多元正态总体设计的,并使用了Bhattacharyya距离,而第二个测试则使用了Mahalanobis距离,适用于同方差情况。尽管为给定的零假设和选定的显着性水平确定临界值需要多积分方程的解,但可以通过蒙特卡洛模拟轻松找到它。通过对某些玉米品种之间的独特性进行试验得出的一些农业数据可以说明该试验的性能。

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