 A data depth based nonparametric test of independence between two random vectorsSakineh Dehghan and Mohammad Reza Faridrohani Journal of Multivariate Analysis, 2024, vol. 202, issue C Abstract: A new family of depth-based test statistics is proposed for testing the hypothesis of independence between two random vectors. In the procedure to derive the asymptotic distribution of the tests under the null hypothesis, we do not require any symmetric assumption of the distribution functions. Furthermore, a conditional distribution-free property of the tests is shown. The asymptotic relative efficiency of the tests is discussed under the class of elliptically symmetric distribution. Asymptotic relative efficiencies along with Monte Carlo results suggest that the performance of the proposed class is comparable to the existing ones, and under some circumstances, it has higher power. Finally, we apply the tests to two real data sets and also discuss the robustness of our tests. Keywords: Depth function; Multivariate test of Independence; Pitman efficiency; Scale invariant (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:202:y:2024:i:c:s0047259x24000046 Ordering information: This journal article can be ordered from DOI: 10.1016/j.jmva.2024.105297 Access Statistics for this article Journal of Multivariate Analysis is currently edited by de Leeuw, J. More articles in Journal of Multivariate Analysis from Elsevier |
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