Two-sample homogeneity tests based on divergence measures
Two-sample homogeneity tests based on divergence measures
复制标题
基于散度测度的两样本同质性检验
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
R. Fried
中科院分区：
文献类型：
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作者：
Max Wornowizki;R. Fried
Ali和Silvey（J r Stat Soc（B）28：131-142，1996）提出的F-Diverence的概念提供了一组丰富的距离，例如分布对之间的测量值。而是考虑相应的概率密度函数之间的差异治疗差异估计的问题，以及随后的两个样本均匀性的测试，我们提出了一个非参数估计量，用于F-Diverence，这是基于内核密度估计和链条平滑的。正如我们在广泛的模拟中所显示的那样，与现有的几个非现有差异估计器相比，新方法的性能稳定，并且相当良好。基于各种差异估计量的统计测试的均匀性问题与不对称的差异测试以及几种传统的参数和非参数过程相比，在不同的分布假设和模拟中的替代方案下进行了差异。与传统方法相比，如果分布在位置上没有差异。离子迁移率数据数据。
The concept of f-divergences introduced by Ali and Silvey (J R Stat Soc (B) 28:131–142, 1996) provides a rich set of distance like measures between pairs of distributions. Divergences do not focus on certain moments of random variables, but rather consider discrepancies between the corresponding probability density functions. Thus, two-sample tests based on these measures can detect arbitrary alternatives when testing the equality of the distributions. We treat the problem of divergence estimation as well as the subsequent testing for the homogeneity of two-samples. In particular, we propose a nonparametric estimator for f-divergences in the case of continuous distributions, which is based on kernel density estimation and spline smoothing. As we show in extensive simulations, the new method performs stable and quite well in comparison to several existing non- and semiparametric divergence estimators. Furthermore, we tackle the two-sample homogeneity problem using permutation tests based on various divergence estimators. The methods are compared to an asymptotic divergence test as well as to several traditional parametric and nonparametric procedures under different distributional assumptions and alternatives in simulations. It turns out that divergence based methods detect discrepancies between distributions more often than traditional methods if the distributions do not differ in location only. The findings are illustrated on ion mobility spectrometry data.