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Group contingency test for two or several independent samples
  • 时间：0
  • 分类：O212.1[理学—概率论与数理统计;理学—数学] O657.71[理学—分析化学;理学—化学]
  • 作者机构：[1] Department of Probability and Statistics, School of Mathematical Sciences, Peking University, Beijing 100871, China
  • 相关基金：This research is supported-by the National Natural Science Foundation of China under Grant No. 10731010 and Ph.D. Program Foundation of Ministry of Education of China under Grant No. 20090001110005.
  • 相关项目：复杂删失数据的统计分析及其应用
关键词： 独立样本, 气相色谱法, 运行测试, 非参数检验, 试验, 秩和检验, 堪萨斯州, 仿真结果, Clustering, group contingency test, nonparametric test
中文摘要：

这份报纸建议新、没有分发的测试叫了组意外事故测试(GC，为短) 为测试，二或几个独立人士取样。与传统的 nonparametric 测试相比， GC 测试趋于基于样品，和它的地点探索更多的信息 -- ，规模 -- ，并且 shapesensitive。作者进行比较 GC 的研究为测试几件样品为二个样品案例与 Wilcoxon 等级和测试(W) ， Kolmogorov-Smirnov 测试(KS ) 和 Wald-Wolfowitz 跑测试(WW ) ，并且与 Kruskal-Wallis (KW ) 测试的某模拟。模拟结果表明那 GC 测试通常超过另外的方法。

英文摘要：

This paper proposes a new and distribution-free test called ＂Group Contingency＂ test （GC, for short） for testing two or several independent samples. Compared with traditional nonparametric tests, GC test tends to explore more information based on samples, and it＇s location-, scale-, and shapesensitive. The authors conduct some simulation studies comparing GC test with Wilcoxon rank sum test （W）, Kolmogorov-Smirnov test （KS） and Wald-Wolfowitz runs test （WW） for two sample case, and with Kruskal-Wallis （KW） for testing several samples. Simulation results reveal that GC test usually outperforms other methods.

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