A Model Selection Approach for Variable Selection with Censored Data

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dc.contributor.author Castellanos, María Eugenia
dc.contributor.author García-Donato, Gonzalo
dc.contributor.author Cabras, Stefano
dc.date.accessioned 2021-06-08T10:33:12Z
dc.date.available 2021-06-08T10:33:12Z
dc.date.issued 2021-03
dc.identifier.bibliographicCitation Castellanos, M. E., García-Donato, G. & Cabras, S. (2021). A Model Selection Approach for Variable Selection with Censored Data. Bayesian Analysis, 16(1), pp. 271-300.
dc.identifier.issn 1931-6690
dc.identifier.uri http://hdl.handle.net/10016/32850
dc.description.abstract We consider the variable selection problem when the response is subject to censoring. A main particularity of this context is that information content of sampled units varies depending on the censoring times. Our approach is based on model selection where all 2 k possible models are entertained and we adopt an objective Bayesian perspective where the choice of prior distributions is a delicate issue given the well-known sensitivity of Bayes factors to these prior inputs. We show that borrowing priors from the ‘uncensored’ literature may lead to unsatisfactory results as this default procedure implicitly assumes a uniform contribution of all units independently on their censoring times. In this paper, we develop specific methodology based on a generalization of the g-priors, explicitly addressing the particularities of survival problems arguing that it behaves comparatively better than standard approaches on the basis of arguments specific to variable selection problems (like e.g. predictive matching) in the particular case of the accelerated failure time model with lognormal errors. We apply the methodology to a recent large epidemiological study about breast cancer survival rates in Castellón, a province of Spain.
dc.description.sponsorship Partially supported by the Ministerio de Ciencia e Innovación grants MTM2016-77501-P. Partially supported by Junta de Comunidades de Castilla-La Mancha grant SBPLY/17/180501/000491/2.
dc.format.extent 30
dc.language.iso eng
dc.publisher International Society for Bayesian Analysis (ISBA)
dc.rights © 2021 International Society for Bayesian Analysis
dc.rights Atribución 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.subject.other Bayes factors
dc.subject.other Bayesian model averaging
dc.subject.other Conventional priors
dc.subject.other Model selection
dc.subject.other Objective priors
dc.subject.other Predictive matching
dc.title A Model Selection Approach for Variable Selection with Censored Data
dc.type article
dc.subject.eciencia Estadística
dc.identifier.doi https://doi.org/10.1214/20-BA1207
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. MTM2016-77501-P
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 271
dc.identifier.publicationissue 1
dc.identifier.publicationlastpage 300
dc.identifier.publicationtitle Bayesian Analysis
dc.identifier.publicationvolume 16
dc.identifier.uxxi AR/0000027615
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.affiliation.dpto UC3M. Departamento de Estadística
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