Multivariate Quantile-Based Permutation Tests with Application to Functional Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10506290" target="_blank" >RIV/00216208:11320/25:10506290 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=tdZuYgJH3B" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=tdZuYgJH3B</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/10618600.2024.2444302" target="_blank" >10.1080/10618600.2024.2444302</a>
Alternative languages
Result language
angličtina
Original language name
Multivariate Quantile-Based Permutation Tests with Application to Functional Data
Original language description
Permutation tests enable testing statistical hypotheses in situations when the distribution of the test statistic is complicated or not available. In some situations, the test statistic under investigation is multivariate, with the multiple testing problem being an important example. The corresponding multivariate permutation tests are then typically based on a suitable one-dimensional transformation of the vector of partial permutation p-values via so called combining functions. This article proposes a new approach that uses the discrete optimal measure transportation concept. The final single p-value is computed from the empirical center-outward distribution function of the permuted multivariate test statistics. This method avoids computation of the partial p-values and it is easy to be implemented. In addition, it allows to compute and interpret contributions of the components of the multivariate test statistic to the overall non-conformity score and to the rejection of the null hypothesis. Apart from this method, the measure transportation is applied also to the vector of partial p-values as an alternative to the classical combining functions. Both techniques are compared to the standard approaches using various practical examples in a Monte Carlo study. An application to a functional dataset is provided as well. Supplementary materials for this article are available online.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Computational and Graphical Statistics
ISSN
1061-8600
e-ISSN
1537-2715
Volume of the periodical
34
Issue of the periodical within the volume
4
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
1276-1290
UT code for WoS article
001417138200001
EID of the result in the Scopus database
2-s2.0-85218817131