0583578 20240527122908.820240305235959.9 85134794382 000830637300001 10.1080/02664763.2022.2102158 15 s. P cav_un_epca*02535720266-47635015 (2023)3142-3156Taylor & Francis multivariate distribution exchangeable distribution multivariate permutation test multiple testing non-parametric combination methodology multiple comparisons cav_un_auth*0345793 Kalina Jan UTIA-B Stochastická informatika Department of Stochastic Informatics SI SI Department of Stochastic Informatics Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0435779 Janáček P. CZ https://www.tandfonline.com/doi/full/10.1080/02664763.2022.2102158 http://library.utia.cas.cz/separaty/2024/SI/kalina-0583578.pdf GA21-05325S GA ČR cav_un_auth*0409039 GA22-02067S GA ČR CZ cav_un_auth*0435776 lthough there have been a number of available tests of bivariate exchangeability, i.e. bivariate symmetry for bivariate distributions, the literature is void of tests whether a multivariate distribution with more than two dimensions is exchangeable or not. In this paper, multivariate permutation tests of exchangeability of multivariate distributions are proposed, which are based on the non-parametric combination methodology, i.e. on combining non-parametric bivariate exchangeability tests. Numerical experiments on real as well as simulated multivariate data with more than two dimensions are presented here. The multivariate permutation test turns out to be typically more powerful than a bivariate exchangeability test performed only over a single pair of variables, and also more suitable compared to tests exploiting the approaches of Benjamini–Yekutieli or Bonferroni. WOS BB 10000 10100 10103 2024 RVO:67985556 https://hdl.handle.net/11104/0351584 S Article Statistics Probability A STATISTICS&PROBABILITY 1.4 0.4 8.7 0.00416 0.483 3925 72 0.545 34.62 1.41 895 Q2 1.000 86 Q2 56.3 Q4 Q3 0.58 Q2 56.3 2023 85134794382 SCOPUS PUBMED 000830637300001 WOS cav_un_epca*0253572 Journal of Applied Statistics Roč. 50 č. 15 2023 3142 3156 0266-4763 1360-0532 Taylor & Francis