Abstract
Multisample testing problems are among the most important topics in nonparametric statistics. Various nonparametric tests have been proposed for multisample testing problems involving location parameters, and the analysis of multivariate data is important in many scientific fields. One type of multivariate multisample testing problem based on Jureckova-Kalina-type rank of distance is discussed in this paper. A multivariate Kruskal-Wallis-type statistic is proposed for testing the location parameter with both equal and unequal sample sizes. Simulations are used to compare the power of proposed nonparametric statistics with the Wilks' lambda, the Pillai's trace and the Lawley-Hotelling trace for various population distributions.
Highlights
Testing hypotheses is one of the most important challenges in nonparametric statistics
To compare the power of the classical MANOVA test and tests based on the multivariate nonparametric statistics, we carried out a simulation study of different populations with various distributions
We considered multivariate multisample nonparametric statistics by applying Jureckova-Kalina-type rank of distance
Summary
Testing hypotheses is one of the most important challenges in nonparametric statistics. Because it is important to determine how to represent ranks for multivariate data in nonparametric statistics, various researchers have proposed the distances of observation for the rank tests. Murakami (2015b) considered the use of Jureckova-Kalina-type rank of distance with the Wilcoxon-type statistic. We extend this concept of rank of distance to a multisample setting.
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