Results 201 to 210 of about 306,037 (248)
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Unbiasedness and biasedness of the Jonckheere–Terpstra and the Kruskal–Wallis tests
Journal of the Korean Statistical Society, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Murakami, Hidetoshi, Lee, Seong Keon
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Evolving benchmark functions using kruskal-wallis test
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018Evolutionary algorithms are cost-effective for solving real-world optimization problems, such as NP-hard and black-box problems. Before an evolutionary algorithm can be put into real-world applications, it is desirable that the algorithm was tested on a number of benchmark problems.
Yang Lou, Shiu Yin Yuen, Guanrong Chen
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A Multivariate Kruskal-Wallis Test With Post Hoc Procedures
Multivariate Behavioral Research, 1980An explicit statement of a statistic which is a nonparametric analogue to one-way MANOVA is presented. The statistic is a multivariate extension of the nonparametric Kruskal-Wallis test (1952). The large sample reference distribution of the test statistic is derived together with a set of computational formulas for the test statistic.
B M, Katz, M, McSweeney
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The Kruskal–Wallis tests are Cochran–Mantel–Haenszel mean score tests
METRON, 2020The Kruskal–Wallis tests are appropriate tests for the completely randomised design, both for when the data are untied ranks, and, with adjustment, for when there are ties and mid-ranks are used. Both these tests are shown to be Cochran–Mantel–Haenszel mean score tests.
J. C. W. Rayner, Glen Livingston
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A NOTE ON A MULTIVARIATE GENERALIZATION OF THE KRUSKAL‐WALLIS TEST
Decision Sciences, 1975Marketers are often interested in testing whether the mean vectors of multivariate distributions are equal. The test usually applied, one‐way MANOVA, assumes the distributions are multinormal. Unfortunately, this assumption is not supported in many studies.
James F. Horrell, V. Parker Lessig
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An Algorithm for Computing the Exact Distribution of the Kruskal–Wallis Test
Communications in Statistics - Simulation and Computation, 2003Abstract The Kruskal–Wallis test is a popular nonparametric test for comparing k independent samples. In this article we propose a new algorithm to compute the exact null distribution of the Kruskal–Wallis test. Generating the exact null distribution of the Kruskal–Wallis test is needed to compare several approximation methods. The 5% cut-off points of
Won Choi +3 more
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Communications in Statistics, 1974
In a contingency table one classification often corresponds to samples from different populations and the other classification to ordered categories. Methods for analyzing data of this type may be compared using recent extensions in the theory of rank tests by Conover (1973) and Vorlickova (1970) to non-continuous distributions.
Hobbs, G. R., Conover, W. J.
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In a contingency table one classification often corresponds to samples from different populations and the other classification to ordered categories. Methods for analyzing data of this type may be compared using recent extensions in the theory of rank tests by Conover (1973) and Vorlickova (1970) to non-continuous distributions.
Hobbs, G. R., Conover, W. J.
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Analysis of variance and the Kruskal–Wallis test
2008In this section, we consider comparisons among more than two groups parametrically, using analysis of variance, as well as nonparametrically, using the Kruskal–Wallis test. Furthermore, we look at two-way analysis of variance in the case of one observation per cell.
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Simplified Beta-Approximations to the Kruskal-WallisHTest
Journal of the American Statistical Association, 1959Abstract A Beta-approximation, commonly used to approximate permutation test distributions in the analysis of variance, is proposed for the null distribution of the Kruskal-Wallis H-statistic for one-way analysis of variance of ranks. The approximation seems slightly simpler and better than the Beta-approximation given by Kruskal and Wallis ...
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