论文标题
新的ECDF两样本测试统计量
A New ECDF Two-Sample Test Statistic
论文作者
论文摘要
经验累积分布函数（ECDF）已被用来检验以下假设：两个样本来自相同的分布，因为Kolmogorov和Smirnov的开创性贡献。本文描述了与Kolmogorov-Smirnov相同的条件下可用的统计量，但比该静脉中的其他现有测试提供了更多的功率。我展示了一种有效的（保守的）程序，用于产生有限样本的p值。我概述了该统计数据与其两个主要前任之间的密切关系。我还提供了一个公共R软件包（Cran：Twosamples [2018]），该程序以$ O（n \ log（n））$时间在$ o（n）$内存中实现测试过程。使用软件包的功能，我进行了几项模拟研究，显示了功能的改进。
Empirical cumulative distribution functions (ECDFs) have been used to test the hypothesis that two samples come from the same distribution since the seminal contribution by Kolmogorov and Smirnov. This paper describes a statistic which is usable under the same conditions as Kolmogorov-Smirnov, but provides more power than other extant tests in that vein. I demonstrate a valid (conservative) procedure for producing finite-sample p-values. I outline the close relationship between this statistic and its two main predecessors. I also provide a public R package (CRAN: twosamples [2018]) implementing the testing procedure in $O(N\log(N))$ time with $O(N)$ memory. Using the package's functions, I perform several simulation studies showing the power improvements.
