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dc.contributor.authorHubin, Aliaksandr
dc.contributor.authorHeinze, Georg
dc.contributor.authorDe Bin, Riccardo
dc.date.accessioned2023-10-31T07:35:14Z
dc.date.available2023-10-31T07:35:14Z
dc.date.created2023-10-10T11:52:52Z
dc.date.issued2023
dc.identifier.citationFractal and Fractional. 2023, 7 (9), Artikkel 641.en_US
dc.identifier.urihttps://hdl.handle.net/11250/3099566
dc.description.abstractWe propose a framework for fitting multivariable fractional polynomial models as special cases of Bayesian generalized nonlinear models, applying an adapted version of the genetically modified mode jumping Markov chain Monte Carlo algorithm. The universality of the Bayesian generalized nonlinear models allows us to employ a Bayesian version of fractional polynomials in any supervised learning task, including regression, classification, and time-to-event data analysis. We show through a simulation study that our novel approach performs similarly to the classical frequentist multivariable fractional polynomials approach in terms of variable selection, identification of the true functional forms, and prediction ability, while naturally providing, in contrast to its frequentist version, a coherent inference framework. Real-data examples provide further evidence in favor of our approach and show its flexibility.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectBayesian model selectionen_US
dc.subjectMCMCen_US
dc.subjectnonlinear effectsen_US
dc.titleFractional Polynomial Models as Special Cases of Bayesian Generalized Nonlinear Modelsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2023 by the authors.en_US
dc.subject.nsiVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410en_US
dc.source.volume7en_US
dc.source.journalFractal and Fractionalen_US
dc.source.issue9en_US
dc.identifier.doi10.3390/fractalfract7090641
dc.identifier.cristin2183292
dc.source.articlenumber641en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal