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dc.contributor.authorSamruam Chongcharoenen_US
dc.contributor.authorManachai Rodchuenen_US
dc.date.accessioned2022-10-16T07:32:39Z-
dc.date.available2022-10-16T07:32:39Z-
dc.date.issued2021-11-01en_US
dc.identifier.issn01253395en_US
dc.identifier.other2-s2.0-85135347649en_US
dc.identifier.other10.14456/sjst-psu.2021.215en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85135347649&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/77506-
dc.description.abstractIn this paper, we considered two-sample multivariate testing for testing the equality of two population mean vectors of two normal populations in this situation in which one covariance is assumed to be known and the other unknown when both the sample sizes are larger than their dimensions. We adapted a test statistic from Yao (1965) and developed its distribution. The accuracy of the proposed test is investigated by simulation study. Under simulation study, the simulated results showed that the attained significance levels of proposed tests are close to nominal significance level setting in every situation considered. All proposed tests gave excellent performance and power in every situation considered except when the sample size from population with known covariance matrix is smaller than that from population with unknown covariance matrix. The two-sided proposed test and the one-sided proposed test as Ha: μ1 < μ2 work very well when the dimension is less than 30. Finally, we applied the proposed tests for analyzing the real data.en_US
dc.subjectMultidisciplinaryen_US
dc.titleA two-sample multivariate test with one covariance matrix unknownen_US
dc.typeJournalen_US
article.title.sourcetitleSongklanakarin Journal of Science and Technologyen_US
article.volume43en_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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