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Flexible maximum conditional likelihood estimation for single-index models to predict accident severity with telematics data
| dc.contributor.author | Bolancé Losilla, Catalina | |
| dc.contributor.author | Cao, Ricardo | |
| dc.contributor.author | Guillén, Montserrat | |
| dc.date.accessioned | 2024-07-17T14:04:52Z | |
| dc.date.available | 2024-07-17T14:04:52Z | |
| dc.date.issued | 2018 | |
| dc.identifier.citation | Bolancé 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.uri | http://hdl.handle.net/2183/38113 | |
| dc.description | IREA 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.sponsorship | The 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.sponsorship | Xunta de Galicia; ED431C-2016-015 | es_ES |
| dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
| dc.language.iso | eng | es_ES |
| dc.publisher | Universitat de Barcelona.Institut de Recerca en Economia Aplicada Regional i Pública (IREA) | es_ES |
| dc.relation | info: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 INDUSTRI | 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 2013-2016/ECO2016-76203-C2-2-P/ES/ | es_ES |
| dc.relation.ispartofseries | IREA – Working Papers, 2018, IR18/29 | es_ES |
| dc.relation.uri | http://hdl.handle.net/2445/126954 | es_ES |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | es_ES |
| dc.rights | © 2018, los autores. | es_ES |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
| dc.subject | Insurance loss data | es_ES |
| dc.subject | Heavy tailed distributions | es_ES |
| dc.subject | Quantiles | es_ES |
| dc.subject | Non-parametric conditional distribution | es_ES |
| dc.title | Flexible maximum conditional likelihood estimation for single-index models to predict accident severity with telematics data | es_ES |
| dc.type | info:eu-repo/semantics/workingPaper | es_ES |
| dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
| UDC.journalTitle | Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA)) | es_ES |
| UDC.volume | 2018 | es_ES |
| UDC.issue | 29 | es_ES |
| UDC.startPage | 1 | es_ES |
| UDC.endPage | 46 | es_ES |
