On two mixture-based clustering approaches used in modeling an insurance portfolio
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hdl:2117/330364
Tipus de documentArticle
Data publicació2018-06
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Reconeixement 4.0 Internacional
Abstract
We 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.
CitacióMiljkovic, T.; Fernandez, D. On two mixture-based clustering approaches used in modeling an insurance portfolio. "RISKS", Juny 2018, vol. 6, núm. 2, p. 57-1-57-18.
ISSN2227-9091
Versió de l'editorhttps://www.mdpi.com/2227-9091/6/2/57
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