New test for the multivariate two-sample problem based on the concept of minimum energy

By G. Zech, B. Aslan. 2003

114 citations recorded by the source corpus.

We introduce a new statistical quantity, the energy, to test whether two samples originate from the same distributions. The energy is a simple logarithmic function of the distances of the observations in the variate space. The distribution of the test statistic is determined by a resampling method. The power of the energy test in one dimension was studied for a variety of different test samples and compared to several nonparametric tests. In two and four dimensions, a comparison was performed with the Friedman–Rafsky and nearest neighbor tests. The two-sample energy test was shown to be especially powerful in multidimensional applications.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5a0eeb51fc23dc3cf5f402100cab056e88e91edc

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In Computer Networks and Communications

In Statistical and Nonlinear Physics

In Statistics and Probability

Citations in the corpus

Testing equality of functions under monotonicity constraints (2012, background): “See also Baringhaus and Franz (2004), Aslan and Zech (2005), Jurečková and Kalina (2012) for more references on multivariate k-sample tests.”
A Two Sample Distribution-Free Test for Functional Data with Application to a Diffusion Tensor Imaging Study of Multiple Sclerosis (2016, methodology): “Some of the popular multivariate testing procedures (e.g. Friedman and Rafsky (1979), Read and Cressie (1988) and Aslan and Zech (2005)) could be employed in this situation.”