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A Class of mixture of experts models for general insurance : application to correlated claim frequencies

Recurso electrónico / Electronic resource
MARC record
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001  MAP20190032070
003  MAP
005  20191106165004.0
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040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
1001 ‎$0‎MAPA20190015158‎$a‎Chai Fung , Tsz
24512‎$a‎A Class of mixture of experts models for general insurance‎$b‎: application to correlated claim frequencies‎$c‎Tsz Chai Fung, Andrei L. Badescu , X. Sheldon Lin
520  ‎$a‎This 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.
650 4‎$0‎MAPA20080592011‎$a‎Modelos actuariales
650 4‎$0‎MAPA20080602437‎$a‎Matemática del seguro
650 4‎$0‎MAPA20080579258‎$a‎Cálculo actuarial
7730 ‎$w‎MAP20077000420‎$t‎Astin bulletin‎$d‎Belgium : ASTIN and AFIR Sections of the International Actuarial Association‎$x‎0515-0361‎$g‎02/09/2019 Volumen 49 Número 3 - septiembre 2019 , p. 647-688