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Probability of default estimation in credit risk using mixture cure models
dc.contributor.author | Peláez, Rebeca | |
dc.contributor.author | Keilegom, Ingrid Van | |
dc.contributor.author | Cao, Ricardo | |
dc.contributor.author | Vilar, Juan M. | |
dc.date.accessioned | 2024-04-19T11:22:02Z | |
dc.date.available | 2024-04-19T11:22:02Z | |
dc.date.issued | 2024-01 | |
dc.identifier.citation | R. 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.2023.107853 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/36259 | |
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.sponsorship | This 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.sponsorship | Xunta de Galicia; ED431C-2020-14 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info: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 APLICACIONES | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/694409 | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.csda.2023.107853 | es_ES |
dc.rights | Atribución-NoComercial 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/3.0/es/ | * |
dc.subject | Censored data | es_ES |
dc.subject | Kernel method | es_ES |
dc.subject | Nonparametric estimation | es_ES |
dc.subject | Survival analysis | es_ES |
dc.title | Probability of default estimation in credit risk using mixture cure models | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Computational Statistics & Data Analysis | es_ES |
UDC.volume | 189 | es_ES |
UDC.issue | 107853 | es_ES |
dc.identifier.doi | 10.1016/j.csda.2023.107853 |
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