Article
Version 1
Preserved in Portico This version is not peer-reviewed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)
Version 2 : Received: 13 December 2023 / Approved: 14 December 2023 / Online: 14 December 2023 (09:02:23 CET)
A peer-reviewed article of this Preprint also exists.
Novoselsky, A.; Kagan, E. A Distance Based Two-Sample Test of Means Difference for Multivariate Datasets. Statistical Papers 2024, doi:10.1007/s00362-024-01576-8. Novoselsky, A.; Kagan, E. A Distance Based Two-Sample Test of Means Difference for Multivariate Datasets. Statistical Papers 2024, doi:10.1007/s00362-024-01576-8.
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
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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