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

Authors: Huskova, Marie¹; Meintanis, Simos²

Source: Journal of Nonparametric Statistics, Volume 20, Number 3, April 2008 , pp. 263-277(15)

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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.

Keywords: k-sample problem; empirical characteristic function; non-parametric test

Document Type: Research article

DOI: http://dx.doi.org/10.1080/10485250801948294

Affiliations: 1: Department of Statistics, Charles University of Prague, CZ, Czech Republic 2: Department of Economics, National and Kapodistrian University of Athens, Athens, Greece

Publication date: 2008-04-01