Spatial Statistical Modeling of Insurance Risk An epidemiologist approach to car insurance
Tufvesson, Oscar ; Lindström, Johan LU
and Lindström, Erik
LU
(2017)
SPAS2017 International Conference on Stochastic Processes and Algebraic Structures – From Theory Towards Applications
- Abstract
- Spatial models, such as the Besag, York and Mollie (BYM)model, have long been used in epidemiology and disease mapping. A commonproblem in these subjects is the modelling of number of disease eventsper region; here the BYM models provides a holistic framework for bothcovariates and dependencies between regions.We use these tools to assess the relative insurance risk associated withthe policyholders geographical location. The models are placed in a Bayesianframework, and inference is made using Integrated Nested Laplace Approximation(INLA).The model is applied to car insurance data from If P&C Insurance togetherwith spatial referenced covariate data of high resolution, provided byInsightone. Including spatially dependence in the... (More)
- Spatial models, such as the Besag, York and Mollie (BYM)model, have long been used in epidemiology and disease mapping. A commonproblem in these subjects is the modelling of number of disease eventsper region; here the BYM models provides a holistic framework for bothcovariates and dependencies between regions.We use these tools to assess the relative insurance risk associated withthe policyholders geographical location. The models are placed in a Bayesianframework, and inference is made using Integrated Nested Laplace Approximation(INLA).The model is applied to car insurance data from If P&C Insurance togetherwith spatial referenced covariate data of high resolution, provided byInsightone. Including spatially dependence in the modelling of number ofclaims signicantly improves on the result obtained using ordinary generalisedlinear models. However, the support for adding a spatial componentto the model for claims cost is weaker. (Less)
- Abstract (Swedish)
- Spatial models, such as the Besag, York and Mollie (BYM)model, have long been used in epidemiology and disease mapping. A commonproblem in these subjects is the modelling of number of disease eventsper region; here the BYM models provides a holistic framework for bothcovariates and dependencies between regions.We use these tools to assess the relative insurance risk associated withthe policyholders geographical location. The models are placed in a Bayesianframework, and inference is made using Integrated Nested Laplace Approximation(INLA).The model is applied to car insurance data from If P&C Insurance togetherwith spatial referenced covariate data of high resolution, provided byInsightone. Including spatially dependence in the... (More)
- Spatial models, such as the Besag, York and Mollie (BYM)model, have long been used in epidemiology and disease mapping. A commonproblem in these subjects is the modelling of number of disease eventsper region; here the BYM models provides a holistic framework for bothcovariates and dependencies between regions.We use these tools to assess the relative insurance risk associated withthe policyholders geographical location. The models are placed in a Bayesianframework, and inference is made using Integrated Nested Laplace Approximation(INLA).The model is applied to car insurance data from If P&C Insurance togetherwith spatial referenced covariate data of high resolution, provided byInsightone. Including spatially dependence in the modelling of number ofclaims signicantly improves on the result obtained using ordinary generalisedlinear models. However, the support for adding a spatial componentto the model for claims cost is weaker. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/ce52eaf5-d3c8-4639-bd5d-9d138a0fe796
- author
- Tufvesson, Oscar
; Lindström, Johan
LU
and Lindström, Erik
LU
- organization
- publishing date
- 2017-10-04
- type
- Contribution to conference
- publication status
- published
- subject
- conference name
- SPAS2017 International Conference on Stochastic Processes and Algebraic Structures – From Theory Towards Applications
- conference location
- Sweden
- conference dates
- 2017-10-04 - 2017-10-06
- language
- Swedish
- LU publication?
- yes
- id
- ce52eaf5-d3c8-4639-bd5d-9d138a0fe796
- date added to LUP
- 2017-10-26 16:16:35
- date last changed
- 2019-03-08 03:24:00
@misc{ce52eaf5-d3c8-4639-bd5d-9d138a0fe796,
abstract = {{Spatial models, such as the Besag, York and Mollie (BYM)model, have long been used in epidemiology and disease mapping. A commonproblem in these subjects is the modelling of number of disease eventsper region; here the BYM models provides a holistic framework for bothcovariates and dependencies between regions.We use these tools to assess the relative insurance risk associated withthe policyholders geographical location. The models are placed in a Bayesianframework, and inference is made using Integrated Nested Laplace Approximation(INLA).The model is applied to car insurance data from If P&C Insurance togetherwith spatial referenced covariate data of high resolution, provided byInsightone. Including spatially dependence in the modelling of number ofclaims signicantly improves on the result obtained using ordinary generalisedlinear models. However, the support for adding a spatial componentto the model for claims cost is weaker.}},
author = {{Tufvesson, Oscar and Lindström, Johan and Lindström, Erik}},
language = {{swe}},
month = {{10}},
title = {{Spatial Statistical Modeling of Insurance Risk An epidemiologist approach to car insurance}},
year = {{2017}},
}