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IR18-029_Bolance+Cao+Guillen.pdf (1.98 MB)

Document type

Working paper

Publication date

2018

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cc-by-nc-nd, (c) Bolancé Losilla et al., 2018
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/126954

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

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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.

Subject

Avaluació del risc, Estadística no paramètrica, Assegurances d'accidents

Subject (English)

Risk assessment, Nonparametric statistics, Accident insurance

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Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA))

Citation

BOLANCÉ LOSILLA, Catalina, CAO, Ricardo and GUILLÉN, Montserrat. Flexible maximum conditional likelihood estimation for single-index models to predict accident severity with telematics data. IREA – Working Papers. 2018. Vol.  IR18/29. [consulted: 12 of August of 2026]. Available at: https://hdl.handle.net/2445/126954

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