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dc.contributor.authorHansen, Bjørn Gunnar
dc.contributor.authorOlsson, Ulf H.
dc.date.accessioned2023-01-18T10:20:34Z
dc.date.available2023-01-18T10:20:34Z
dc.date.created2021-06-25T13:03:17Z
dc.date.issued2021
dc.identifier.citationStructural Equation Modeling. 2021, .en_US
dc.identifier.issn1070-5511
dc.identifier.urihttps://hdl.handle.net/11250/3044230
dc.description.abstractAlthough structural equation model (SEM) is a powerful and widely applied tool particularly in social sciences, few studies have explored how SEM and statistical learning methods can be combined. The purpose of this paper is to explore how gradient component-wise boosting (GCB) can contribute to item selection. We ran 200 regressions with different farmer psychological variables collected to explain variation in an animal welfare indicator (AWI). The most frequently selected variables from the regressions were selected to build a SEM to explain variation in the AWI. The results show that boosting selects relevant items for a SEM.en_US
dc.language.isoengen_US
dc.publisherTaylor & Francisen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.subjectSpecification searchen_US
dc.subjectSEMen_US
dc.subjectboostingen_US
dc.titleSpecification Search in Structural Equation Modeling (SEM): How Gradient Component-wise Boosting can Contributeen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber12en_US
dc.source.volume29en_US
dc.source.journalStructural Equation Modelingen_US
dc.source.issue1en_US
dc.identifier.doi10.1080/10705511.2021.1935263
dc.identifier.cristin1918491
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal