Flexible maximum conditional likelihood estimation for single-index models to predict accident severity with telematics data

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UDC.coleccionInvestigaciónes_ES
UDC.departamentoMatemáticases_ES
UDC.endPage46es_ES
UDC.grupoInvModelización, Optimización e Inferencia Estatística (MODES)es_ES
UDC.issue29es_ES
UDC.journalTitleDocuments de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA))es_ES
UDC.startPage1es_ES
UDC.volume2018es_ES
dc.contributor.authorBolancé Losilla, Catalina
dc.contributor.authorCao, Ricardo
dc.contributor.authorGuillén, Montserrat
dc.date.accessioned2024-07-17T14:04:52Z
dc.date.available2024-07-17T14:04:52Z
dc.date.issued2018
dc.descriptionIREA Working Papers often represent preliminary work and are circulated to encourage discussion. Document included in IREA – Working Papers, 2018, IR18/29.es_ES
dc.description.abstract[Abstract]: Estimation in single-index models for risk assessment is developed. Statistical properties are given and an application to estimate the cost of traffic accidents in an innovative insurance data set that has information on driving style is presented. A new kernel approach for the estimator covariance matrix is provided. Both, the simulation study and the real case show that the method provides the best results when data are highly skewed and when the conditional distribution is of interest. Supplementary materials containing appendices are available online.es_ES
dc.description.sponsorshipThe support received by the Ministry of Economy and Competitiveness in Grant ECO2016- 76203-C2-2-P for the first and third authors is gratefully acknowledged. The research of the second author has been supported by MINECO Grants MTM2014-52876-R and MTM2017- 82724-R, and by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2016-015 and Centro Singular de Investigación de Galicia ED431G/01), all of them through the ERDF. All authors declare no conflict of interest as no sponsor has been involved in the implementation and conclusions of the research.es_ES
dc.description.sponsorshipXunta de Galicia; ED431C-2016-015es_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01es_ES
dc.identifier.citationBolancé Losilla, C., Cao, R., & Guillén, M. (2018). Flexible maximum conditional likelihood estimation for single-index models to predict accident severity with telematics data. IREA–Working Papers, 2018, IR18/29.es_ES
dc.identifier.urihttp://hdl.handle.net/2183/38113
dc.language.isoenges_ES
dc.publisherUniversitat de Barcelona.Institut de Recerca en Economia Aplicada Regional i Pública (IREA)es_ES
dc.relation.ispartofseriesIREA – Working Papers, 2018, IR18/29es_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 INDUSTRIes_ES
dc.relation.projectIDinfo: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 DIMENSIONes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/ECO2016-76203-C2-2-P/ES/es_ES
dc.relation.urihttp://hdl.handle.net/2445/126954es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes_ES
dc.rights© 2018, los autores.es_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectInsurance loss dataes_ES
dc.subjectHeavy tailed distributionses_ES
dc.subjectQuantileses_ES
dc.subjectNon-parametric conditional distributiones_ES
dc.titleFlexible maximum conditional likelihood estimation for single-index models to predict accident severity with telematics dataes_ES
dc.typeworking paperes_ES
dspace.entity.typePublication
relation.isAuthorOfPublication3360aaca-39be-43b4-a458-974e79cdbc6b
relation.isAuthorOfPublication.latestForDiscovery3360aaca-39be-43b4-a458-974e79cdbc6b

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