Publication:
A new minimal training sample scheme for intrinsic Bayes factors in censored data

dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorCabras, Stefano
dc.contributor.authorCastellanos, María Eugenia
dc.contributor.authorPerra, Silvia
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2022-06-22T15:09:53Z
dc.date.available2022-06-22T15:09:53Z
dc.date.issued2015-01-01
dc.description.abstractThe problem of covariate selection for regression models with right censored data is considered. It is approached from a default Bayesian point of view with Bayes factors (BFs) and in particular with Intrinsic BF (IBF) that depends on the minimal training samples (MTSs). In the presence of censored data, the number of possible MTSs increases, due to the fact that uncensored data, relevant for training the improper prior into a proper posterior, must be combined with censored data. For this purpose, the sequential minimal training sample scheme (SMTS) accounts for such requirements but generally leads to IBF correction factors that do not have an analytical form and thus require numerical approximation. In order to obtain an analytical expression of the correction terms, a different TS scheme is introduced based on the Kaplan-Meier (KM) estimator, termed the KM minimal training sample scheme. This new tool works extremely well in the analyzed simulation setting and also in the applications; it produces results which are similar, if not better, than the IBF calculated using MTSs. The resulting new IBF, being based on analytical expressions, is much faster to compute. Evidence of these results comes from a large simulation study, theoretical arguments, and an application to a real data set. (C) 2014 Elsevier B.V. All rights reserved.en
dc.description.sponsorshipMaria Eugenia Castellanos was partially supported by Ministerio de Ciencia e Innovación grant MTM2010-19528, MTM2013-42323 and the visiting professor program of the Regione Autonoma della Sardegna. Stefano Cabras has been partially supported by Ministerio de Ciencia e Innovación grant MTM2013-42323, ECO2012-38442, RYC-2012-11455, and Silvia Perra was partially supported by Ministero dell’Istruzione, dell’Univesità e della Ricerca of Italy grant CRP-59903.en
dc.identifier.bibliographicCitationCabras, S., Castellanos, M. E., & Perra, S. (2015). A new minimal training sample scheme for intrinsic Bayes factors in censored data. Computational Statistics & Data Analysis, 81, pp. 52-63.en
dc.identifier.doihttps://doi.org/10.1016/j.csda.2014.07.012
dc.identifier.issn0167-9473
dc.identifier.publicationfirstpage52es
dc.identifier.publicationlastpage63es
dc.identifier.publicationtitleCOMPUTATIONAL STATISTICS & DATA ANALYSISen
dc.identifier.publicationvolume81es
dc.identifier.urihttps://hdl.handle.net/10016/35238
dc.identifier.uxxiAR/0000015789
dc.language.isoenges
dc.publisherElsevieres
dc.relation.projectIDGobierno de España. ECO2012-38442es
dc.relation.projectIDGobierno de España. RYC-2012-11455es
dc.relation.projectIDGobierno de España. MTM2010-1952es
dc.relation.projectIDGobierno de España. MTM2013-42323es
dc.rights© Elsevier, 2014es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaEstadísticaes
dc.subject.otherImproper priorsen
dc.subject.otherIntrinsic Bayes factoren
dc.subject.otherKaplan-Meier estimatoren
dc.subject.otherModel selectionen
dc.subject.otherSurvival analysisen
dc.subject.otherModels selectionen
dc.subject.otherVariable-selectionen
dc.subject.otherRegressionen
dc.titleA new minimal training sample scheme for intrinsic Bayes factors in censored dataen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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