Tests for the multivariate k-sample problem based on the empirical characteristic function

Author: Huskova Marie  

Publisher: Taylor & Francis Ltd

ISSN: 1048-5252

Source: Journal of Nonparametric Statistics, Vol.20, Iss.3, 2008-04, pp. : 263-277

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Abstract

Tests for the multivariate k-sample problem are considered. The tests are based on the weighted L2 distance between empirical characteristic functions, and afford an interesting interpretation in terms of a corresponding test statistic based on the L2 distance of pairs of non-parametric density estimators. Depending on the choice of weighting, a corresponding Dirac-type weight function reduces the test to a normalised version of the L2 distance between the sample means of the k populations. Theoretical and computational issues are considered, while the finite-sample implementation based on the permutation distribution of the test statistic shows that the new test performs well in comparison with alternative procedures of the change-point type.