# zbMATH — the first resource for mathematics

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)
##### MSC:
 93C57 Sampled-data control/observation systems 62G10 Nonparametric hypothesis testing 62H30 Classification and discrimination; cluster analysis (statistical aspects)
##### Keywords:
clustering; group contingency; nonparametric test
SoDA
Full Text:
##### References:
 [1] Erich L. Lehmann, Parametric versus nonparametrics: Two alternative methodologies, Journal of Nonparametric Statistics, 2009, 21(4): 397–405. · Zbl 1161.62020 · doi:10.1080/10485250902842727 [2] M. Hollander and D. A. Wolfe, Nonparametric Statistical Methods, 2nd ed., Wiley, New York, 1999. · Zbl 0997.62511 [3] P. Sprent and N. C. Smeeton, Applied Nonparametric Statistical Methods, 3rd edition, Chapman and Hall/CRC, Boca Raton, Florida, 2001. · Zbl 0991.62027 [4] A. Wald and J. Wolfowitz, On a test whether two samples are from the same population, Ann. Math. Stat., 1940, 11(2): 147–162. · Zbl 0023.24802 · doi:10.1214/aoms/1177731909 [5] C. R. Mehta and Nitin R. Patel, A network algorithm for performing fisher’s exact test in $$\times$$ contingency table test, Journal of the American Statistical Association, 1983, 78(382): 427–434. · Zbl 0545.62039 [6] G. J. Gan, C. Q. Ma, and J. H. Wu, Data Clustering: Theory, Algorithms, and Applications, SIAM, Society for Industrial and Applied Mathematics, 2007. · Zbl 1185.68274 [7] J. B. MacQueen, Some methods for classification and analysis of multivariate observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1967. · Zbl 0214.46201 [8] C. R. Magela and S. H. Wibowo, Comparing the powers of the Wald-Wolfowitz and Kolmogorov-Smirnov tests, Biometrical Journal, 1997, 39(6): 665–675. · Zbl 0884.62050 · doi:10.1002/bimj.4710390605 [9] J. M. Chambers, Software for Data Analysis: Programming with R (Statistics and Computing), Springer, 2008. · Zbl 1180.62002
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.