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Two‐sample test based on classification probability
Statistical Analysis and Data Mining ( IF 1.3 ) Pub Date : 2019-10-09 , DOI: 10.1002/sam.11438
Haiyan Cai 1 , Bryan Goggin 1 , Qingtang Jiang 1
Affiliation  

Robust classification algorithms have been developed in recent years with great success. We take advantage of this development and recast the classical two‐sample test problem in the framework of classification. Based on the estimates of classification probabilities from a classifier trained from the samples, a test statistic is proposed. We explain why such a test can be a powerful test and compare its performance in terms of the power and efficiency with those of some other recently proposed tests with simulation and real‐life data. The test proposed is nonparametric and can be applied to complex and high‐dimensional data wherever there is a classifier that provides consistent estimate of the classification probability for such data.

中文翻译:

基于分类概率的两样本检验

近年来,已经开发了鲁棒的分类算法,并取得了巨大的成功。我们利用这一发展,并在分类框架中重现了经典的两样本测试问题。基于对样本进行分类的分类器对分类概率的估计,提出了检验统计量。我们解释了为什么这样的测试可以成为强大的测试,并在功率和效率方面将其性能与其他最近提出的带有模拟和真实数据的测试进行比较。提出的测试是非参数的,可以在存在可为此类数据的分类概率提供一致估计的分类器的情况下,应用于复杂的高维数据。
更新日期:2019-10-09
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