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Conference Paper: Insurance claims modulated by a hidden marked point process

TitleInsurance claims modulated by a hidden marked point process
Authors
Issue Date2007
Citation
Proceedings Of The American Control Conference, 2007, p. 390-395 How to Cite?
AbstractRecently Markov-modulated compound Poisson models have gained its popularity in modelling insurance claims in the actuarial science literature. A Markov-modulated compound Poisson model can provide a realistic and flexibile way to model aggregate insurance claims by incorporating the impact of hidden states of an economy on claim frequencies and claim sizes. However, in practice, the Markov chain in the model is not observable. It is of practical interest to develop some methods to estimate the hidden state of the Markov chain and other unknown model parameters of the Markov-modulated compound Poisson model. This paper considers this important issue. We shall develop filters and smoothers for the hidden state of the economy underlying the Markov-modulated compound Poisson model. In general, we consider the case when both the stochastic intensity and the distribution of the claim sizes of the compound Poisson process depend on the hidden Markov chain. The filter and smoother provide an optimal way to estimate the insurance claims model in the "mean-squared- error" sense. We shall also develop estimators for the unknown model parameters of the Markov-modulated marked point process using the robust filter-based and smoother-based EM algorithms. © 2007 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/110207
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorElliott, RJen_HK
dc.contributor.authorSiu, TKen_HK
dc.contributor.authorYang, Hen_HK
dc.date.accessioned2010-09-26T01:55:50Z-
dc.date.available2010-09-26T01:55:50Z-
dc.date.issued2007en_HK
dc.identifier.citationProceedings Of The American Control Conference, 2007, p. 390-395en_HK
dc.identifier.issn0743-1619en_HK
dc.identifier.urihttp://hdl.handle.net/10722/110207-
dc.description.abstractRecently Markov-modulated compound Poisson models have gained its popularity in modelling insurance claims in the actuarial science literature. A Markov-modulated compound Poisson model can provide a realistic and flexibile way to model aggregate insurance claims by incorporating the impact of hidden states of an economy on claim frequencies and claim sizes. However, in practice, the Markov chain in the model is not observable. It is of practical interest to develop some methods to estimate the hidden state of the Markov chain and other unknown model parameters of the Markov-modulated compound Poisson model. This paper considers this important issue. We shall develop filters and smoothers for the hidden state of the economy underlying the Markov-modulated compound Poisson model. In general, we consider the case when both the stochastic intensity and the distribution of the claim sizes of the compound Poisson process depend on the hidden Markov chain. The filter and smoother provide an optimal way to estimate the insurance claims model in the "mean-squared- error" sense. We shall also develop estimators for the unknown model parameters of the Markov-modulated marked point process using the robust filter-based and smoother-based EM algorithms. © 2007 IEEE.en_HK
dc.languageengen_HK
dc.relation.ispartofProceedings of the American Control Conferenceen_HK
dc.titleInsurance claims modulated by a hidden marked point processen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailYang, H: hlyang@hku.hken_HK
dc.identifier.authorityYang, H=rp00826en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ACC.2007.4283152en_HK
dc.identifier.scopuseid_2-s2.0-46449103797en_HK
dc.identifier.hkuros142930en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-46449103797&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage390en_HK
dc.identifier.epage395en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridElliott, RJ=7402639776en_HK
dc.identifier.scopusauthoridSiu, TK=8655758200en_HK
dc.identifier.scopusauthoridYang, H=7406559537en_HK

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