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dc.contributor.authorRimal, Raju
dc.contributor.authorAlmøy, Trygve
dc.contributor.authorSæbø, Solve
dc.date.accessioned2019-06-25T09:39:22Z
dc.date.available2019-06-25T09:39:22Z
dc.date.created2019-06-12T15:54:04Z
dc.date.issued2019
dc.identifier.citationChemometrics and Intelligent Laboratory Systems. 2019, 190 10-21.nb_NO
dc.identifier.issn0169-7439
dc.identifier.urihttp://hdl.handle.net/11250/2602036
dc.description.abstractWhile data science is battling to extract information from the enormous explosion of data, many estimators and algorithms are being developed for better prediction. Researchers and data scientists often introduce new methods and evaluate them based on various aspects of data. However, studies on the impact of/on a model with multiple response variables are limited. This study compares some newly-developed (envelope) and well-established (PLS, PCR) prediction methods based on real data and simulated data specifically designed by varying properties such as multicollinearity, the correlation between multiple responses and position of relevant principal components of predictors. This study aims to give some insight into these methods and help the researcher to understand and use them in further studies.nb_NO
dc.language.isoengnb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleComparison of multi-response prediction methodsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber10-21nb_NO
dc.source.volume190nb_NO
dc.source.journalChemometrics and Intelligent Laboratory Systemsnb_NO
dc.identifier.doihttps://doi.org/10.1016/j.chemolab.2019.05.004
dc.identifier.cristin1704434
cristin.unitcode192,12,0,0
cristin.unitcode192,50,0,0
cristin.unitnameKjemi, bioteknologi og matvitenskap
cristin.unitnameSentraladministrasjonen
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Med mindre annet er angitt, så er denne innførselen lisensiert som Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal