Geoadditive regression for analyzing small-scale geographical variability in car insurance View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2007-04

AUTHORS

Ludwig Fahrmeir, Frank Sagerer, Gerald Sussmann

ABSTRACT

The empirical part of this article is based on a study on car insurance data to explore how global and local geographical effects on frequency and size of claims can be assessed with appropriate statistical methodology. Because these spatial effects have to be modeled and estimated simultaneously with linear and possibly nonlinear effects of further covariates such as age of policy holder, age of car or bonus-malus score, generalized linear models cannot be applied. Also, compared to previous analyses, the geographical information is given by the exact location of the residence of policy holders. Therefore, we employ a new class of geoadditive models, where the spatial component is modeled based on stationary Gaussian random fields, common in geostatistics (Kriging). Statistical inference is carried out by an empirical Bayes or penalized likelihood approach using mixed model technology. The results confirm that the methodological concept provides useful tools for exploratory analyses of the data at hand and in similar situations. More... »

PAGES

47-65

References to SciGraph publications

Journal

TITLE

Blätter der DGVFM

ISSUE

1

VOLUME

28

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11857-007-0014-2

DOI

http://dx.doi.org/10.1007/s11857-007-0014-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1016166776


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