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November 2004 Multivariate Nonparametric Tests
Hannu Oja, Ronald H. Randles
Statist. Sci. 19(4): 598-605 (November 2004). DOI: 10.1214/088342304000000558

Abstract

Multivariate nonparametric statistical tests of hypotheses are described for the one-sample location problem, the several-sample location problem and the problem of testing independence between pairs of vectors. These methods are based on affine-invariant spatial sign and spatial rank vectors. They provide affine-invariant multivariate generalizations of the univariate sign test, signed-rank test, Wilcoxon rank sum test, Kruskal–Wallis test, and the Kendall and Spearman correlation tests. While the emphasis is on tests of hypotheses, certain references to associated affine-equivariant estimators are included. Pitman asymptotic efficiencies demonstrate the excellent performance of these methods, particularly in heavy-tailed population settings. Moreover, these methods are easy to compute for data in common dimensions.

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Hannu Oja. Ronald H. Randles. "Multivariate Nonparametric Tests." Statist. Sci. 19 (4) 598 - 605, November 2004. https://doi.org/10.1214/088342304000000558

Information

Published: November 2004
First available in Project Euclid: 18 April 2005

zbMATH: 1100.62567
MathSciNet: MR2185581
Digital Object Identifier: 10.1214/088342304000000558

Rights: Copyright © 2004 Institute of Mathematical Statistics

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Vol.19 • No. 4 • November 2004
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