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Nonparametric Estimation in Mixture Cure Models with Covariates
dc.contributor.author | López-Cheda, Ana | |
dc.contributor.author | Peng, Yingwei | |
dc.contributor.author | Jácome, M. A. | |
dc.date.accessioned | 2023-12-21T14:50:43Z | |
dc.date.issued | 2023-05-17 | |
dc.identifier.citation | López-Cheda, A., Peng, Y. & Jácome, M.A. Nonparametric estimation in mixture cure models with covariates. TEST 32, 467–495 (2023). https://doi.org/10.1007/s11749-022-00840-z | es_ES |
dc.identifier.issn | 1133-0686 | |
dc.identifier.issn | 1863-8260 | |
dc.identifier.uri | http://hdl.handle.net/2183/34597 | |
dc.description | This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11749-022-00840-z | es_ES |
dc.description.abstract | [Abstract] Nonparametric estimation methods for the cure rate and the distribution of the failure time of uncured subjects with covariates for censored survival data have attracted much attention in the last few years. To model the effects of covariates on the distribution of the failure time of uncured subjects, existing works assume that the cure rate is a constant or depends on the same covariate as the distribution of uncured subjects. In this paper, we review the nonparametric estimation methods in the context of the mixture cure model and propose a new nonparametric estimator for the distribution of uncured subjects that relaxes the assumption used in the existing works. The estimation is based on the EM algorithm, which is readily available for mixture cure models, and is strongly consistent. The finite sample performance of the proposed estimator is assessed and compared with existing methods in a simulation study. Finally, the nonparametric estimation methods are employed to model the effects of some covariates on the time to bankruptcy among commercial banks insured by the Federal Deposit Insurance Corporation during the first quarter of 2006. | es_ES |
dc.description.sponsorship | ALC was sponsored by the BEATRIZ GALINDO JUNIOR Spanish grant from Ministerio de Ciencia, Innovación y Universidades with reference BGP18/00154. ALC and MAJ acknowledge partial support by the MINECO Grant MTM2017-82724-R (EU ERDF support included), the MICINN Grant PID2020-113578RB-I00, and partial support of Xunta de Galicia (Centro Singular de Investigación de Galicia accreditation ED431G 2019/01 and Grupos de Referencia Competitiva ED431C-2020-14 and ED431C2016-015) and the European Union (European Regional Development Fund - ERDF). YP’s work was partially supported by a Discovery grant from the Natural Sciences and Engineering Research Council of Canada. The authors thank Alessandro Beretta and Cédric Heuchenne for their assistance in obtaining the bank data analyzed in Sect. 7 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C-2020-14 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C2016-015 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer Nature | es_ES |
dc.relation | info:eu-repo/grantAgreement/MECD/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/BEAGAL18%2F00143/ES/ | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/MTM2017-82724-R/ES/INFERENCIA ESTADISTICA FLEXIBLE PARA DATOS COMPLEJOS DE GRAN VOLUMEN Y DE ALTA DIMENSION | 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/MÉTODOS ESTADÍSTICOS FLEXIBLES EN CIENCIA DE DATOS PARA DATOS COMPLEJOS Y DE GRAN VOLUMEN: TEORÍA Y APLICACIONES | es_ES |
dc.relation.uri | https://doi.org/10.1007/s11749-022-00840-z | es_ES |
dc.subject | Bootstrap | es_ES |
dc.subject | Censored data | es_ES |
dc.subject | EM algorithm | es_ES |
dc.subject | Survival analysis | es_ES |
dc.title | Nonparametric Estimation in Mixture Cure Models with Covariates | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/embargoedAccess | es_ES |
dc.date.embargoEndDate | 2024-05-18 | es_ES |
dc.date.embargoLift | 2024-05-18 | |
UDC.journalTitle | TEST | es_ES |
UDC.volume | 32 | es_ES |
UDC.startPage | 467 | es_ES |
UDC.endPage | 495 | es_ES |
dc.identifier.doi | 10.1007/s11749-022-00840-z |
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