A Quartile Test for Differences in Distribution
提出一个基于样本四分位数的非参数统计量来检验分布差异，模拟表明其功效与Kolmogorov-Smirnov检验相当，且在位置和离散度均不同时更敏感。
Abstract We propose a simple nonparametric statistic using sample quartiles to test differences in distribution. Simulation results suggest that the test is about equal in power over a wide range of alternatives to the familiar procedure of Kolmogorov and Smirnov. When the two distributions compared differ in both location and dispersion, the quartile test may be more sensitive than the Kolmogorov-Smirnov, Wilcoxon rank-sum, Siegel-Tukey, and runs tests.