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Conditional likelihood based inference on single-index models for motor insurance claim severity
dc.contributor.author | Bolancé Losilla, Catalina | |
dc.contributor.author | Cao, Ricardo | |
dc.contributor.author | Guillén, Montserrat | |
dc.date.accessioned | 2024-09-16T14:58:02Z | |
dc.date.available | 2024-09-16T14:58:02Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Bolancé, C., Cao, R., & Guillén, M. (2024). Conditional likelihood based inference on single index-models for motor insurance claim severity. SORT-Statistics and Operations Research Transactions, 48(2). DOI: 10.57645/20.8080.02.20 | es_ES |
dc.identifier.issn | 1696-2281 | |
dc.identifier.issn | 2013-8830 | |
dc.identifier.uri | http://hdl.handle.net/2183/39065 | |
dc.description.abstract | [Abstract]: Prediction of a traffc accident cost is one of the major problems in motor insurance. To identify the factors that infuence costs is one of the main challenges of actuarial modelling. Telematics data about individual driving patterns could help calculating the expected claim severity in motor insurance. We propose using single-index models to assess the marginal effects of covariates on the claim severity conditional distribution. Thus, drivers with a claim cost distribution that has a long tail can be identifed. These are risky drivers, who should pay a higher insurance premium and for whom preventative actions can be designed. A new kernel approach to estimate the covariance matrix of coeffcients’ estimator is outlined. Its statistical properties are described and an application to an innovative data set containing information on driving styles is presented. The method provides good results when the response variable is skewed. | es_ES |
dc.description.sponsorship | This article is part of the I+D+i projects PID2019-105986GB-C21 and grant TED2021- 130187B-I00, financed by MCIN/ AEI/10.13039/501100011033. MG thanks ICREA Academia. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Institut d'Estadistica de Catalunya | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-105986GB-C21/ES/MODELOS PREDICTIVOS PARA EL RIESGO EN SEGUROS Y FINANZAS | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-130187B-I00/ES/ANALÍTICA DE DATOS EN SEGUROS: METODOS E IMPLICACIONES PARA PRODUCTOS BASADOS EN EL USO | es_ES |
dc.relation.uri | http://dx.doi.org/10.57645/20.8080.02.20 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Covariance matrix of estimator | es_ES |
dc.subject | Kernel estimator | es_ES |
dc.subject | Marginal effects | es_ES |
dc.subject | Right-skewed cost variable | es_ES |
dc.subject | Telematics covariates | es_ES |
dc.title | Conditional likelihood based inference on single-index models for motor insurance claim severity | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | SORT - Statistics and Operations Research Transactions | es_ES |
UDC.volume | 48 | es_ES |
UDC.issue | 2 | es_ES |
UDC.startPage | 1 | es_ES |
UDC.endPage | 24 | es_ES |
dc.identifier.doi | 10.57645/20.8080.02.20 |
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