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A general framework for prediction in penalized regression

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorCarballo González, Alba
dc.contributor.authorDurbán Reguera, María Luz
dc.contributor.authorKauermann, Goran
dc.contributor.authorLee, Dae-Jin
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2021-03-25T12:51:07Z
dc.date.available2021-03-25T12:51:07Z
dc.date.issued2020-02-27
dc.description.abstractThere are two main approaches to carrying out prediction in the context of penalized regression: with low-rank basis and penalties or through the smooth mixed models. In this article, we give further insight in the case of P-splines showing the influence of the penalty on the prediction. In the context of mixed models, we can connect the new predicted values to the observed values through a joint normal distribution, which allows us to compute prediction intervals. In this work, we propose an alternative approach, called the extended mixed model approach, that allows us to fit and predict data simultaneously. The methodology is illustrated with two real datasets, one of them on aboveground biomass and the other on monthly sulphur dioxide (SO2) levels in a selection of monitoring sites in Europe.es
dc.description.sponsorshipThe first and second authors acknowledge financial support from the Spanish Ministry of Economy and Competitiveness MTM2014-52184-P. The fourth author acknowledges the financial support by the Basque Government through the BERC 2018-2021 programme and by Spanish Ministry of Economy and Competitiveness MINECO through BCAM Severo Ochoa excellence accreditation SEV-2013-0323 and through project MTM2017-82379-R funded by (AEI/FEDER, UE) and acronym AFTERAM.es
dc.format.extent21es
dc.identifier.bibliographicCitationStatistical Modelling, February 2020es
dc.identifier.doihttps://doi.org/10.1177/1471082X19896867
dc.identifier.issn1471-082X
dc.identifier.publicationfirstpage1es
dc.identifier.publicationlastpage20es
dc.identifier.publicationtitleSTATISTICAL MODELLINGes
dc.identifier.urihttps://hdl.handle.net/10016/32234
dc.identifier.uxxiAR/0000027513
dc.language.isoenges
dc.publisherSAGE Publishinges
dc.relation.projectIDGobierno de España. MTM2014-52184-Pes
dc.rights© 2020 SAGE Publicationses
dc.rights.accessRightsopen accesses
dc.subject.ecienciaEstadísticaes
dc.subject.otherMixed Modelses
dc.subject.otherPenalized Regressiones
dc.subject.otherPredictiones
dc.subject.otherP-Splineses
dc.subject.otherSmooth Modelses
dc.titleA general framework for prediction in penalized regressiones
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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