Some permutation tests for high dimensional mean vectors

Caizhu Huang (), Euloge Clovis Kenne Pagui () and Fortunato Pesarin ()
Additional contact information
Caizhu Huang: Guandong University of Finance and Economics
Euloge Clovis Kenne Pagui: University of Oslo
Fortunato Pesarin: University of Padova

Statistical Methods & Applications, 2025, vol. 34, issue 4, No 8, 753-765

Abstract: Abstract For high dimensional data where the dimension p of the observation vector is larger than the group sample sizes $$n_i, i=1,2$$ n i , i = 1 , 2 , classical approaches based on asymptotic theory are no longer valid. A recent parametric projection test shows a comparable type I error but with a substantial power improvement. However, the asymptotic theory of all such statistics may not hold with small $$n_i$$ n i , especially, in the presence of strong correlation structures. We here propose the use of a permutation test based on the same parametric projection test statistic. Extensive simulation results show that the permutation versions are more accurate under the null hypothesis than the parametric projection test when sample sizes are small and, especially when there is a strong correlation in the data. A significant test of “Sell in May and Go Away” for the manufacturing sector in China’s A-share market from May 2001 to October 2017 illustrates its application.

Keywords: Nonparametric inference; Projection test; Two-sample mean vector; Sell in May and Go Away (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10260-025-00804-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stmapp:v:34:y:2025:i:4:d:10.1007_s10260-025-00804-1

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2

DOI: 10.1007/s10260-025-00804-1

Access Statistics for this article

Statistical Methods & Applications is currently edited by Tommaso Proietti

More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().