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NONPARAMETRIC TESTS FOR THE MULTIVARIATE MULTI-SAMPLE LOCATION PROBLEM
Yonghwan Um and Ronald H. Randles
Vol. 8, No. 3 (July 1998), pp. 801-812
Published by: Institute of Statistical Science, Academia Sinica
Stable URL: http://www.jstor.org/stable/24306464
Page Count: 12
You can always find the topics here!Topics: Nonparametric tests, Degrees of freedom, Statism, Null hypothesis, Matrices, Hyperplanes, Statistics, Rank tests, Covariance, Statistical results
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Nonparametric tests for the multi-sample multivariate location problem are proposed which extend the two-sample multivariate rank tests by Randles and Peters (1990) to the multi-sample setting. The asymptotic distributions of the proposed statistics under the null hypothesis and under certain contiguous alternatives are obtained for a class of elliptically symmetric distributions. Comparisons are made between the proposed statistics and several competitors via Pitman asymptotic relative efficiencies and Monte Carlo results. The tests proposed perform better than the Lawley-Hotelling generalized T2 for heavy-tailed distributions. For normal to light-tailed distributions, the proposed statistics also perform better than other nonparametric competitors and the proposed analog of the signed-rank test performs better than the Lawley-Hotelling generalized T2 for light-tailed distributions.
Statistica Sinica © 1998 Institute of Statistical Science, Academia Sinica