Show simple item record

dc.contributor.authorMiljkovic, Tatjana
dc.contributor.authorFernández, Daniel
dc.date.accessioned2019-02-04T17:11:02Z
dc.date.available2019-02-04T17:11:02Z
dc.identifier.otherRisks 2018, 6, 57; doi:10.3390/risks6020057en_US
dc.identifier.urihttp://hdl.handle.net/2374.MIA/6315
dc.description.abstractWe review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM) and mixture-based clustering for an ordered stereotype model (OSM). The latter is for modeling of ordinal variables, and the former is for modeling losses as a function of mixed-type of covariates. The article extends the idea of mixture modeling to a multivariate classification for the purpose of testing unobserved heterogeneity in an insurance portfolio. The application of both methods is illustrated on a well-known French automobile portfolio, in which the model fitting is performed using the expectation-maximization (EM) algorithm. Our findings show that these mixture-based clustering methods can be used to further test unobserved heterogeneity in an insurance portfolio and as such may be considered in insurance pricing, underwriting, and risk management.en_US
dc.relation.isversionofdoi:10.3390/risks6020057en_US
dc.titleOn Two Mixture-Based Clustering Approaches Used in Modeling an Insurance Portfolioen_US
dc.typeJournal Articleen_US
dc.date.published2018


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record