Probability of default estimation in credit risk using mixture cure models

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UDC.coleccionInvestigaciónes_ES
UDC.departamentoMatemáticases_ES
UDC.grupoInvModelización, Optimización e Inferencia Estatística (MODES)es_ES
UDC.issue107853es_ES
UDC.journalTitleComputational Statistics & Data Analysises_ES
UDC.volume189es_ES
dc.contributor.authorPeláez, Rebeca
dc.contributor.authorKeilegom, Ingrid Van
dc.contributor.authorCao, Ricardo
dc.contributor.authorVilar, Juan M.
dc.date.accessioned2024-04-19T11:22:02Z
dc.date.available2024-04-19T11:22:02Z
dc.date.issued2024-01
dc.description.abstract[Abstract]: An estimator of the probability of default (PD) in credit risk is proposed. It is derived from a nonparametric conditional survival function estimator based on cure models. Asymptotic expressions for the bias and the variance, as well as the asymptotic normality of the proposed estimator are presented. A simulation study shows the performance of the nonparametric estimator compared with Beran's PD estimator and other semiparametric methods. Finally, an empirical study based on modified real data illustrates the practical behaviour.es_ES
dc.description.sponsorshipThis research has been supported by MICINN Grant PID2020-113578RB-100 , by the Xunta de Galicia (Grupo de Referencia Competitiva ED431C-2020-14 and Centro Singular de Investigación de Galicia ED431G 2019/01), all of them through the ERDF and by the European Research Council (2016-2022, Horizon 2020 / ERC grant agreement No. 694409 ). Peláez, R. was sponsored by inMOTION Programme of grants for pre-doctoral stays Inditex-UDC 2021.es_ES
dc.description.sponsorshipXunta de Galicia; ED431C-2020-14es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01es_ES
dc.identifier.citationR. Peláez, I. Van Keilegom, R. Cao, and J. M. Vilar, "Probability of default estimation in credit risk using mixture cure models", Computational Statistics and Data Analysis, Vol. 189, 107853, Jan. 2024, doi: 10.1016/j.csda.2023.107853es_ES
dc.identifier.doi10.1016/j.csda.2023.107853
dc.identifier.urihttp://hdl.handle.net/2183/36259
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113578RB-I00/ES/METODOS ESTADISTICOS FLEXIBLES EN CIENCIA DE DATOS PARA DATOS COMPLEJOS Y DE GRAN VOLUMEN: TEORIA Y APLICACIONESes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/694409es_ES
dc.relation.urihttps://doi.org/10.1016/j.csda.2023.107853es_ES
dc.rightsAtribución-NoComercial 3.0 Españaes_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/es/*
dc.subjectCensored dataes_ES
dc.subjectKernel methodes_ES
dc.subjectNonparametric estimationes_ES
dc.subjectSurvival analysises_ES
dc.titleProbability of default estimation in credit risk using mixture cure modelses_ES
dc.typejournal articlees_ES
dspace.entity.typePublication
relation.isAuthorOfPublication3360aaca-39be-43b4-a458-974e79cdbc6b
relation.isAuthorOfPublication8266f7ba-97e2-451f-9c0a-5501266378e0
relation.isAuthorOfPublication.latestForDiscovery3360aaca-39be-43b4-a458-974e79cdbc6b

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