Flexible maximum conditional likelihood estimation for single-index models to predict accident severity with telematics data
![Thumbnail](/dspace/bitstream/handle/2183/38113/Cao_Ricardo_2018_Flexible_maximum_conditional_likelihood_estimation_for_single-index_models.pdf.jpg?sequence=5&isAllowed=y)
Use este enlace para citar
http://hdl.handle.net/2183/38113
Excepto si se señala otra cosa, la licencia del ítem se describe como Atribución-NoComercial-SinDerivadas 3.0 España
Colecciones
Metadatos
Mostrar el registro completo del ítemTítulo
Flexible maximum conditional likelihood estimation for single-index models to predict accident severity with telematics dataFecha
2018Cita bibliográfica
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.
Resumen
[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.
Palabras clave
Insurance loss data
Heavy tailed distributions
Quantiles
Non-parametric conditional distribution
Heavy tailed distributions
Quantiles
Non-parametric conditional distribution
Descripción
IREA Working Papers often represent preliminary work and are circulated to encourage discussion.
Document included in IREA – Working Papers, 2018, IR18/29.
Versión del editor
Derechos
Atribución-NoComercial-SinDerivadas 3.0 España © 2018, los autores.