Please use this identifier to cite or link to this item: https://doi.org/10.1080/10485252.2021.2015768
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dc.titleA Revisit to Bai--Saranadasa's Two-Sample Test
dc.contributor.authorJin-Ting Zhang
dc.contributor.authorTianming Zhu
dc.date.accessioned2022-03-14T03:23:04Z
dc.date.available2022-03-14T03:23:04Z
dc.date.issued2021-12-24
dc.identifier.citationJin-Ting Zhang, Tianming Zhu (2021-12-24). A Revisit to Bai--Saranadasa's Two-Sample Test. Journal of Nonparametric Statistics 34 (01) : 58-76. ScholarBank@NUS Repository. https://doi.org/10.1080/10485252.2021.2015768
dc.identifier.issn1048-5252
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/217048
dc.description.abstractBai–Saranadasa's two-sample test for high-dimensional data, namely BS-test, has been widely cited in the literature. However, it may not control the size well when the required conditions are not satisfied. In this paper, a revisit to the BS-test is conducted. It is shown that under some regularity conditions and the null hypothesis, the BS-test statistic and a chi-square-type mixture have the same limiting distribution. It is then natural to approximate the null distribution of the BS-test using that of the chi-square-type mixture, which is actually obtained from the BS-test statistic when the two high-dimensional samples are normally distributed. The resulting test is then referred to as a normal-reference test. Two simulation studies and a real data example demonstrate that in terms of size control, the proposed normal-reference test performs very well and it performs substantially better than the BS-test and three other existing competitors proposed in the literature.
dc.publisherTaylor & Francis
dc.sourceTaylor & Francis
dc.subjectBai–Saranadasa's two-sample test
dc.subjecthigh-dimensional data
dc.subjectχ2-type mixtures
dc.subjectthree-cumulant matched χ2-approximation
dc.typeArticle
dc.contributor.departmentDEPT OF STATISTICS & APPLIED PROBABILITY
dc.description.doi10.1080/10485252.2021.2015768
dc.description.sourcetitleJournal of Nonparametric Statistics
dc.description.volume34
dc.description.issue01
dc.description.page58-76
dc.published.statePublished
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