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Group contingency test for two or several independent samples. (English) Zbl 1338.93231
Summary: 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 (WW) runs test for two sample cases, and with Kruskal-Wallis (KW) for testing several samples. Simulation results reveal that GC test usually outperforms other methods.
Reviewer: Reviewer (Berlin)
93C57 Sampled-data control/observation systems
62G10 Nonparametric hypothesis testing
62H30 Classification and discrimination; cluster analysis (statistical aspects)
Full Text: DOI
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