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dc.contributor.authorHellebuyck, Rafael Adriel
dc.date.accessioned2019-01-08T17:06:02Z
dc.date.available2019-01-08T17:06:02Z
dc.date.issued2019-01-08
dc.identifier.urihttp://hdl.handle.net/10675.2/622025
dc.descriptionThe file you are attempting to access is currently restricted to Augusta University. Please log in with your NetID if off campus.en
dc.description.abstractWithin the medical field, the demand to store and analyze small sample, large variable data has become ever-abundant. Several two-sample tests for equality of means, including the revered Hotelling’s T2 test, have already been established when the combined sample size of both populations exceeds the dimension of the variables. However, tests such as Hotelling’s T2 become either unusable or output small power when the number of variables is greater than the combined sample size. We propose a test using both prepivoting and Edgeworth expansion that maintains high power in this higher dimensional scenario, known as the “large p small n ” problem. Our test’s finite sample performance is compared with other recently proposed tests designed to also handle the “large p small n ” situation. We apply our test to a microarray gene expression data set and report competitive rates for both power and Type-I error.
dc.subjectPivoten
dc.subjectPrepivoten
dc.subjectEdgeworth Expansionen
dc.subjectRandom Projectionen
dc.titleTWO-SAMPLE TESTS FOR HIGH DIMEMSIONAL MEANS WITH PREPIVOTING and DATA TRANSFORMATIONen
dc.typeThesisen
dc.contributor.departmentDepartment of Biostatistics and Epidemiologyen
dc.language.rfc3066en
dc.date.updated2019-01-08T17:06:03Z
dc.description.advisorGhosh, Santuen
dc.description.degreeMaster of Science with a Major in Biostatisticsen
dc.description.committeeChen, Jie; Ayyala, Deepak Nag; Shi, Yangen
html.description.abstractWithin the medical field, the demand to store and analyze small sample, large variable data has become ever-abundant. Several two-sample tests for equality of means, including the revered Hotelling’s T2 test, have already been established when the combined sample size of both populations exceeds the dimension of the variables. However, tests such as Hotelling’s T2 become either unusable or output small power when the number of variables is greater than the combined sample size. We propose a test using both prepivoting and Edgeworth expansion that maintains high power in this higher dimensional scenario, known as the “large p small n ” problem. Our test’s finite sample performance is compared with other recently proposed tests designed to also handle the “large p small n ” situation. We apply our test to a microarray gene expression data set and report competitive rates for both power and Type-I error.


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