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dc.contributor.authorBorrajo García, María Isabel
dc.contributor.authorGonzález Manteiga, Wenceslao
dc.contributor.authorMartínez Miranda, María Dolores
dc.date.accessioned2019-11-28T11:15:03Z
dc.date.available2019-11-28T11:15:03Z
dc.date.issued2017
dc.identifier.citationBorrajo, M. I., González-Manteiga, W., & Martínez-Miranda, M. D. (2017). Bandwidth selection for kernel density estimation with length-biased data. Journal of Nonparametric Statistics, 29(3), 636-668.
dc.identifier.issn1048-5252
dc.identifier.urihttp://hdl.handle.net/10347/20313
dc.descriptionThis is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Nonparametric Statistics on 23 Jun 2017, available online: https://doi.org/10.1080/10485252.2017.1339309.
dc.description.abstractLength-biased data are a particular case of weighted data, which arise in many situations: biomedicine, quality control or epidemiology among others. In this paper we study the theoretical properties of kernel density estimation in the context of length-biased data, proposing two consistent bootstrap methods that we use for bandwidth selection. Apart from the bootstrap bandwidth selectors we suggest a rule-of-thumb. These bandwidth selection proposals are compared with a least-squares cross-validation method. A simulation study is accomplished to understand the behaviour of the procedures in finite samples
dc.description.sponsorshipThe authors acknowledge the support fromthe SpanishMinistry of Economy and Competitiveness, through grant number MTM2013-41383P, which includes support from the European Regional Development Fund (ERDF). Support from the IAP network StUDyS (P7/06) from Belgian Science Policy, is also acknowledged. M.I. Borrajo has been supported by FPU (FPU2013/00473) from the Spanish Ministry of Education
dc.language.isoeng
dc.publisherTaylor & Francis
dc.subjectBootstrap
dc.subjectRule-of-thumb
dc.subjectCross-validation
dc.subjectNonparametric
dc.subjectWeighted data
dc.titleBandwidth selection for kernel density estimation with length-biased data
dc.typeinfo:eu-repo/semantics/article
dc.identifier.DOI10.1080/10485252.2017.1339309
dc.relation.publisherversionhttps://doi.org/10.1080/10485252.2017.1339309
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.e-issn1029-0311
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Estatística, Análise Matemática e Optimización
dc.description.peerreviewedSI


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