Kernel estimation of single-index models for motor insurance claim severity
Data and R programming
Catalina Bolancé, Ricardo Cao & Montserrat Guillén
This page presents data and R programmes from the paper
Flexible maximum conditional likelihood estimation for single-index models to predict accident severity with telematics data .
These data are inspired by a subsample of a real data set from an insurance company collected in 2011.
Data description
| Name | Definition |
| Cost | cost per claim in thousands of euros |
| Age | age in years |
| Agelic | number of years holding a driving licence |
| Agecar | age of car in years |
| Parking | a binary indicator equal to 1 if car is parked in a garage overnight and 0 otherwise |
| Tkm | annual distance driven in thousands of kilometres |
| Nightkm | percentage of kilometres driven at night |
| Urbankm | percentage of kilometres driven on urban roads |
| Speedkm | percentage of kilometres driven above the speed limit |
Files
Supplementary Materials PDF files
Other related papers:
- Alemany, R.; Bolancé, C.; Guillen, M.(2013) A nonparametric approach to calculating value-at-risk, Insurance: Mathematics and Economics, 52(2), 255-262.
- Guillen, M.; Nielsen, J.P.; Ayuso, M.; Pérez-Marín, A.M. (2019)The use of telematics devices to improve automobile insurance rates, Risk Analysis, 39(3), 662-672
- Universitat de Barcelona - Last Updated: 05-23-2014