Nonparametric incidence estimation and bootstrap bandwidth selection in mixture cure models

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UDC.coleccionInvestigaciónes_ES
UDC.departamentoMatemáticases_ES
UDC.endPage165es_ES
UDC.grupoInvModelización, Optimización e Inferencia Estatística (MODES)es_ES
UDC.journalTitleComputational Statistics & Data Analysises_ES
UDC.startPage144es_ES
UDC.volume105es_ES
dc.contributor.authorLópez-Cheda, Ana
dc.contributor.authorCao, Ricardo
dc.contributor.authorJácome, M. A.
dc.contributor.authorKeilegom, Ingrid Van
dc.date.accessioned2023-12-21T10:15:14Z
dc.date.available2023-12-21T10:15:14Z
dc.date.issued2017-01
dc.description© 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article [López-Cheda, A., Cao, R., Jácome, M.A., Van Keilegom, I., 2017. Nonparametric incidence estimation and bootstrap bandwidth selection in mixture cure models. Computational Statistics & Data Analysis 105, 144–165] has been accepted for publication in Computational Statistics & Data Analysis. The Version of Record is available online at https://doi.org/10.1016/j.csda.2016.08.002.es_ES
dc.description.abstract[Abstract]: A completely nonparametric method for the estimation of mixture cure models is proposed. A nonparametric estimator of the incidence is extensively studied and a nonparametric estimator of the latency is presented. These estimators, which are based on the Beran estimator of the conditional survival function, are proved to be the local maximum likelihood estimators. An i.i.d. representation is obtained for the nonparametric incidence estimator. As a consequence, an asymptotically optimal bandwidth is found. Moreover, a bootstrap bandwidth selection method for the nonparametric incidence estimator is proposed. The introduced nonparametric estimators are compared with existing semiparametric approaches in a simulation study, in which the performance of the bootstrap bandwidth selector is also assessed. Finally, the method is applied to a database of colorectal cancer from the University Hospital of A Coruña (CHUAC).es_ES
dc.description.sponsorshipThe first author’s research was sponsored by the Spanish FPU grant from MECD with reference FPU13/01371. The work of the first author has been partially carried out during a visit at the Université catholique de Louvain, financed by INDITEX, with reference INDITEX-UDC 2014. All the authors acknowledge partial support by the MINECO grant MTM2014-52876-R (EU ERDF support included). The first three authors’ research has been partially supported by MICINN Grant MTM2011-22392 (EU ERDF support included) and Xunta de Galicia GRC Grant CN2012/130. The research of the fourth author was supported by IAP Research Network P7/06 of the Belgian State (Belgian Science Policy), and by the contract “Projet d’Actions de Recherche Concertées” (ARC) 11/16-039 of the “Communauté française de Belgique” (granted by the “ Académie universitaire Louvain”). The authors would like to thank the Associate Editor and the three anonymous referees for their constructive and helpful comments, which have greatly improved the paper. The authors are grateful to Dr. Sonia Pértega and Dr. Salvador Pita, at the University Hospital of A Coruña, for providing the colorectal cancer data set.es_ES
dc.description.sponsorshipXunta de Galicia; CN2012/130es_ES
dc.identifier.citationA. López-Cheda, R. Cao, M. A. Jácome, y I. Van Keilegom, «Nonparametric incidence estimation and bootstrap bandwidth selection in mixture cure models», Computational Statistics & Data Analysis, vol. 105, pp. 144-165, ene. 2017, doi: 10.1016/j.csda.2016.08.002.es_ES
dc.identifier.issn1872-7352
dc.identifier.urihttp://hdl.handle.net/2183/34587
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/MECD/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/FPU13%2F01371/ES/es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2014-52876-R/ES/INFERENCIA ESTADISTICA COMPLEJA Y DE ALTA DIMENSION: EN GENOMICA, NEUROCIENCIA, ONCOLOGIA, MATERIALES COMPLEJOS, MALHERBOLOGIA, MEDIO AMBIENTE, ENERGIA Y APLICACIONES INDUSTRIALESes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/MICINN/Plan Nacional de I+D+i 2008-2011/MTM2011-22392/ES/INFERENCIA ESTADISTICA PARA DATOS COMPLEJOS Y DE ALTA DIMENSION: APLICACIONES EN ANALISIS TERMICO, FIABILIDAD NAVAL, GENOMICA, MALHERBOLOGIA, NEUROCIENCIA Y ONCOLOGIAes_ES
dc.relation.urihttps://doi.org/10.1016/j.csda.2016.08.002es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectSurvival analysises_ES
dc.subjectCensored dataes_ES
dc.subjectLocal maximum likelihoodes_ES
dc.subjectKernel estimationes_ES
dc.titleNonparametric incidence estimation and bootstrap bandwidth selection in mixture cure modelses_ES
dc.typejournal articlees_ES
dspace.entity.typePublication
relation.isAuthorOfPublication811e9787-a857-4c18-8295-268b4014b4bc
relation.isAuthorOfPublication3360aaca-39be-43b4-a458-974e79cdbc6b
relation.isAuthorOfPublicatione629ebcc-3475-4638-b4e7-bf3e786f997c
relation.isAuthorOfPublication.latestForDiscovery811e9787-a857-4c18-8295-268b4014b4bc

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