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Small sample sizes : A big data problem in high-dimensional data analysis

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dc.contributor.authorKonietschke, Frank
dc.contributor.authorSchwab, Karima
dc.contributor.authorPauly, Markus
dc.contributor.institutionUniversity of Aberdeen.Applied Medicineen
dc.date.accessioned2021-06-30T13:41:01Z
dc.date.available2021-06-30T13:41:01Z
dc.date.issued2021-03-01
dc.descriptionAcknowledgements The authors are grateful to the Editor, Associate Editor and three anonymous referees for their helpful suggestions, which greatly improved the manuscript. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research is supported by the German Science Foundation awards number DFG KO 4680/3-2 and PA 2409/3-2.en
dc.description.statusPeer revieweden
dc.format.extent15
dc.format.extent417524
dc.identifier196149748
dc.identifierf6983a36-ac07-4790-bb74-ebdad5a3aa40
dc.identifier85096540596
dc.identifier.citationKonietschke, F, Schwab, K & Pauly, M 2021, 'Small sample sizes : A big data problem in high-dimensional data analysis', Statistical Methods in Medical Research, vol. 30, no. 3, pp. 687–701. https://doi.org/10.1177/0962280220970228en
dc.identifier.doi10.1177/0962280220970228
dc.identifier.iss3en
dc.identifier.issn0962-2802
dc.identifier.otherORCID: /0000-0001-7063-7136/work/96308213
dc.identifier.urihttps://hdl.handle.net/2164/16756
dc.identifier.urlhttp://dx.doi.org/10.1177/0962280220970228en
dc.identifier.vol30en
dc.language.isoeng
dc.relation.ispartofStatistical Methods in Medical Researchen
dc.subjectMultiple contrast testsen
dc.subjectmax t-testen
dc.subjectrepeated measuresen
dc.subjectresamplingen
dc.subjectsimultaneous confidence intervalsen
dc.subjectR Medicineen
dc.subjectOtheren
dc.subjectDFG KO 4680/3-2en
dc.subjectPA 2409/3-2en
dc.subject.lccRen
dc.titleSmall sample sizes : A big data problem in high-dimensional data analysisen
dc.typeJournal articleen

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