Covariate selection from telematics car driving data

DOI10.1007/s13385-017-0149-zzbMath1394.62151OpenAlexW2582016141MaRDI QIDQ1707549

Mario V. Wüthrich

Publication date: 3 April 2018

Published in: European Actuarial Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13385-017-0149-z

zbMATH Keywords

pattern recognitionclusteringunsupervised learningregression\(K\)-means clusteringcategorical classesdriving habitsdriving stylesmachine learning.telematics data

Mathematics Subject Classification ID

Related Items (8)

What can we learn from telematics car driving data: a survey ⋮ The added value of dynamically updating motor insurance prices with telematics collected driving behavior data ⋮ Modelling the aggregate loss for insurance claims with dependence ⋮ Actuarial intelligence in auto insurance: claim frequency modeling with driving behavior features and improved boosted trees ⋮ POISSON MODELS WITH DYNAMIC RANDOM EFFECTS AND NONNEGATIVE CREDIBILITIES PER PERIOD ⋮ Claims frequency modeling using telematics car driving data ⋮ Feature extraction from telematics car driving heatmaps ⋮ How Much Telematics Information Do Insurers Need for Claim Classification?

Uses Software

• ElemStatLearn
• ISLR

Cites Work

• Classification of scale-sensitive telematic observables for riskindividual pricing
• An Introduction to Statistical Learning
• Unnamed Item

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