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	Group contingency test for two or several independent samples
	Zhang, Hexin ; Fang, Xiangzhong ; Ma, Xiaojing
	2011
关键词	Clustering group contingency test nonparametric test
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.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000297798800012&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Mathematics, Interdisciplinary Applications; SCI(E); EI; 中国科学引文数据库(CSCD); 0; ARTICLE; 6; 1183-1192; 24
语种	英语
出处	EI ; SCI
出版者	journal of systems science complexity
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/157568]
专题	数学科学学院
推荐引用方式 GB/T 7714	Zhang, Hexin,Fang, Xiangzhong,Ma, Xiaojing. Group contingency test for two or several independent samples. 2011-01-01.

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