Flexible maximum conditional likelihood estimation for single-index models to predict accident severity with telematics data
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Flexible maximum conditional likelihood estimation for single-index models to predict accident severity with telematics dataDate
2018Citation
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.
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.
Keywords
Insurance loss data
Heavy tailed distributions
Quantiles
Non-parametric conditional distribution
Heavy tailed distributions
Quantiles
Non-parametric conditional distribution
Description
IREA Working Papers often represent preliminary work and are circulated to encourage discussion.
Document included in IREA – Working Papers, 2018, IR18/29.
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Atribución-NoComercial-SinDerivadas 3.0 España © 2018, los autores.