Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

A Distance Based Two-Sample Test of Means Difference for Multivariate Datasets

Version 1 : Received: 27 November 2023 / Approved: 28 November 2023 / Online: 28 November 2023 (07:07:47 CET)
Version 2 : Received: 13 December 2023 / Approved: 14 December 2023 / Online: 14 December 2023 (09:02:23 CET)

How to cite: Novoselsky, A.; Kagan, E. A Distance Based Two-Sample Test of Means Difference for Multivariate Datasets. Preprints 2023, 2023111732. https://doi.org/10.20944/preprints202311.1732.v2 Novoselsky, A.; Kagan, E. A Distance Based Two-Sample Test of Means Difference for Multivariate Datasets. Preprints 2023, 2023111732. https://doi.org/10.20944/preprints202311.1732.v2

Abstract

In the paper we present a new test for comparison of the means of multivariate samples with unknown distributions. The test is based on the comparison of the distributions of the distances between the samples’ elements and their means using univariate two-sample Kolmogorov-Smirnov test. The activity of the suggested method is illustrated by numerical analysis of the real-world and simulated data.

Keywords

multivariate two-sample problem; multivariate means test; distance-based statistic; two-sample Kolmogorov-Smirnov test

Subject

Computer Science and Mathematics, Probability and Statistics

Comments (1)

Comment 1
Received: 14 December 2023
Commenter: Eugene Kagan
Commenter's Conflict of Interests: Author
Comment: Clarification about the spreading parameters of the samples was added (see section 2, the last sentence of the second paragraph and section 5, the furst paragraph after the algorithm).
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