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dc.contributor.authorVitale, Raffaele
dc.contributor.authorWesterhuis, Johan A.
dc.contributor.authorNæs, Tormod
dc.contributor.authorSmilde, Age K.
dc.contributor.authorde Noord, Onno E.
dc.contributor.authorFerrer, Alberto
dc.date.accessioned2018-04-17T09:27:50Z
dc.date.available2018-04-17T09:27:50Z
dc.date.created2018-02-02T12:22:12Z
dc.date.issued2017
dc.identifier.issn0886-9383
dc.identifier.urihttp://hdl.handle.net/11250/2494418
dc.description.abstractSelecting the correct number of factors in principal component analysis (PCA) is a critical step to achieve a reasonable data modelling, where the optimal strategy strictly depends on the objective PCA is applied for. In the last decades, much work has been devoted to methods like Kaiser's eigenvalue greater than 1 rule, Velicer's minimum average partial rule, Cattell's scree test, Bartlett's chi-square test, Horn's parallel analysis, and cross-validation. However, limited attention has been paid to the possibility of assessing the significance of the calculated components via permutation testing. That may represent a feasible approach in case the focus of the study is discriminating relevant from nonsystematic sources of variation and/or the aforementioned methodologies cannot be resorted to (eg, when the analysed matrices do not fulfill specific properties or statistical assumptions). The main aim of this article is to provide practical insights for an improved understanding of permutation testing, highlighting its pros and cons, mathematically formalising the numerical procedure to be abided by when applying it for PCA factor selection by the description of a novel algorithm developed to this end, and proposing ad hoc solutions for optimising computational time and efficiency.
dc.language.isoengnb_NO
dc.titleSelecting the number of factors in principal component analysis by permutation testing—Numerical and practical aspectsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionsubmittedVersion
dc.source.volume31nb_NO
dc.source.journalJournal of Chemometricsnb_NO
dc.source.issue12nb_NO
dc.identifier.doi10.1002/cem.2937
dc.identifier.cristin1561275
dc.relation.projectNorges forskningsråd: 262308nb_NO
cristin.unitcode7543,3,2,0
cristin.unitnameRåvare og prosess
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode1


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