On a new multivariate two-sample test
Journal of Multivariate Analysis
Two-sample tests of the equality of two cumulative incidence functions
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
k-Sample tests based on the likelihood ratio
Computational Statistics & Data Analysis
A studentized permutation test for the non-parametric Behrens-Fisher problem
Computational Statistics & Data Analysis
Non-parametric k-sample tests: Density functions vs distribution functions
Computational Statistics & Data Analysis
Goodness-of-fit tests based on empirical characteristic functions
Computational Statistics & Data Analysis
The 3D Moore-Rayleigh Test for the Quantitative Groupwise Comparison of MR Brain Images
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Computational Statistics & Data Analysis
Dependent wild bootstrap for degenerate U- and V-statistics
Journal of Multivariate Analysis
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A class of tests for the two sample problem that is based on the empirical characteristic function is investigated. They can be applied to continuous as well as to discrete data of any arbitrary fixed dimension. The tests are consistent against any fixed alternatives for adequate choices of the weight function involved in the definition of the test statistic. Both the bootstrap and the permutation procedures can be employed to estimate consistently the null distribution. The goodness of these approximations and the power of some tests in this class for finite sample sizes are investigated by simulation.