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Variable selection in discriminant analysis in the presence of outliers

机译:在异常值存在下判别分析的可变选择

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摘要

An important problem in discriminant analysis that has received little attention in the literature, is the effect of outliers when variable selection is the first step in the analysis. In this paper we consider two methods of dealing with this problem. The classification performance of these methods, and the classification performance if possible outliers are ignored, are compared in a simulation study In the configurations studied, we find that none of the approaches consistently outperforms the others.
机译:在文献中接受了很少关注的判别分析中的一个重要问题,是异常值在变量选择是分析中的第一步时的效果。在本文中,我们考虑了两种处理这个问题的方法。这些方法的分类性能,以及分类性能,如果可能的异常值被忽略,则在所研究的配置中进行仿真研究进行比较,我们发现没有任何方法始终如一地优于其他方法。

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