A CLASS OF MIXTURE OF EXPERTS MODELS FOR GENERAL INSURANCE: APPLICATION TO CORRELATED CLAIM FREQUENCIES

This paper focuses on the estimation and application aspects of the Erlang count logit-weighted reduced mixture of experts model (EC-LRMoE), which is a fully flexible multivariate insurance claim frequency regression model. We first prove the identifiability property of the proposed model to ensure...

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Vydáno v:ASTIN Bulletin : The Journal of the IAA Ročník 49; číslo 3; s. 647 - 688
Hlavní autoři: Chai Fung, Tsz, Badescu, Andrei L., Sheldon Lin, X.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, USA Cambridge University Press 01.09.2019
Témata:
Actuarial science
Algorithms
Calibration
Datasets
Insurance claims
Insurance premiums
Parameter estimation
Policyholders
Statistical inference
expectation conditional maximization algorithm
multivariate count regression
logit-weighted gating functions
Erlang count models
mixture of experts models
ISSN:0515-0361, 1783-1350
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Shrnutí:This paper focuses on the estimation and application aspects of the Erlang count logit-weighted reduced mixture of experts model (EC-LRMoE), which is a fully flexible multivariate insurance claim frequency regression model. We first prove the identifiability property of the proposed model to ensure that it is a suitable candidate for statistical inference. An expectation conditional maximization (ECM) algorithm is developed for efficient model calibrations. Three simulation studies are performed to examine the effectiveness of the proposed ECM algorithm and the versatility of the proposed model. The applicability of the EC-LRMoE is shown through fitting an European automobile insurance data set. Since the data set contains several complex features, we find it necessary to adopt such a flexible model. Apart from showing excellent fitting results, we are able to interpret the fitted model in an insurance perspective and to visualize the relationship between policyholders’ information and their risk level. Finally, we demonstrate how the fitted model may be useful for insurance ratemaking.
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ISSN:0515-0361
1783-1350
DOI:10.1017/asb.2019.25