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Article: Filtering a markov modulated random measure

TitleFiltering a markov modulated random measure
Authors
KeywordsInsurance risk models
Markov-modulated random measures
Martingales
Model uncertainty
Reference probability
Robust EM algorithms
Issue Date2010
PublisherIEEE.
Citation
Ieee Transactions On Automatic Control, 2010, v. 55 n. 1, p. 74-88 How to Cite?
AbstractWe develop a new exact filter when a hidden Markov chain influences both the sizes and times of a marked point process. An example would be an insurance claims process, where we assume that both the stochastic intensity of the claim arrivals and the distribution of the claim sizes depend on the states of an economy. We also develop the robust filter-based and smoother-based EM algorithms for the on-line recursive estimates of the unknown parameters in the Markov-modulated random measure. Our development is in the framework of modern theory of stochastic processes. © 2009 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/125411
ISSN
2015 Impact Factor: 2.777
2015 SCImago Journal Rankings: 4.238
ISI Accession Number ID
Funding AgencyGrant Number
SSHRC
Hong Kong Special Administrative Region, China754008H
Funding Information:

This work was supported by SSHRC, and by the Research Grants Council of the Hong Kong Special Administrative Region, China under Project HKU 754008H. Recommended by Associate Editor Z. Wang.

References

 

DC FieldValueLanguage
dc.contributor.authorElliott, RJen_HK
dc.contributor.authorSiu, TKen_HK
dc.contributor.authorYang, Hen_HK
dc.date.accessioned2010-10-31T11:29:52Z-
dc.date.available2010-10-31T11:29:52Z-
dc.date.issued2010en_HK
dc.identifier.citationIeee Transactions On Automatic Control, 2010, v. 55 n. 1, p. 74-88en_HK
dc.identifier.issn0018-9286en_HK
dc.identifier.urihttp://hdl.handle.net/10722/125411-
dc.description.abstractWe develop a new exact filter when a hidden Markov chain influences both the sizes and times of a marked point process. An example would be an insurance claims process, where we assume that both the stochastic intensity of the claim arrivals and the distribution of the claim sizes depend on the states of an economy. We also develop the robust filter-based and smoother-based EM algorithms for the on-line recursive estimates of the unknown parameters in the Markov-modulated random measure. Our development is in the framework of modern theory of stochastic processes. © 2009 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Transactions on Automatic Controlen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsIEEE Transactions on Automatic Control. Copyright © IEEE.-
dc.rights©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectInsurance risk modelsen_HK
dc.subjectMarkov-modulated random measuresen_HK
dc.subjectMartingalesen_HK
dc.subjectModel uncertaintyen_HK
dc.subjectReference probabilityen_HK
dc.subjectRobust EM algorithmsen_HK
dc.titleFiltering a markov modulated random measureen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0018-9286&volume=55&issue=1&spage=74&epage=88&date=2009&atitle=Filtering+a+markov+modulated+random+measureen_HK
dc.identifier.emailYang, H: hlyang@hku.hken_HK
dc.identifier.authorityYang, H=rp00826en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TAC.2009.2034227en_HK
dc.identifier.scopuseid_2-s2.0-75449097352en_HK
dc.identifier.hkuros173051en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-75449097352&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume55en_HK
dc.identifier.issue1en_HK
dc.identifier.spage74en_HK
dc.identifier.epage88en_HK
dc.identifier.isiWOS:000273609300007-
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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