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Bayesian quantile regression model for claim count data

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dc.FundingDetailsUniversiti Kebangsaan Malaysia Ministry of Higher Education, Malaysia,�MOHE
dc.FundingDetailsWe would like to personally thank Duncan Lee from the University of Glasgow for providing us the R algorithms which is used in this research. We gratefully acknowledge the financial support received in the form of research university grants ( GUP-2015-002 and DPP-2015-010 ) from Universiti Kebangsaan Malaysia (UKM) and Ministry of Higher Education (MOHE), Malaysia . We are also grateful to the anonymous referees for their valuable comments and suggestions.
dc.citedby4
dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.affiliationsUniversiti Kebangsaan Malaysia (UKM)
dc.contributor.authorFuzi M.F.M.en_US
dc.contributor.authorJemain A.A.en_US
dc.contributor.authorIsmail N.en_US
dc.date.accessioned2024-05-28T08:25:45Z
dc.date.available2024-05-28T08:25:45Z
dc.date.issued2016
dc.description.abstractQuantile regression model estimates the relationship between the quantile of a response distribution and the regression parameters, and has been developed for linear models with continuous responses. In this paper, we apply Bayesian quantile regression model for the Malaysian motor insurance claim count data to study the effects of change in the estimates of regression parameters (or the rating factors) on the magnitude of the response variable (or the claim count). We also compare the results of quantile regression models from the Bayesian and frequentist approaches and the results of mean regression models from the Poisson and negative binomial. Comparison from Poisson and Bayesian quantile regression models shows that the effects of vehicle year decrease as the quantile increases, suggesting that the rating factor has lower risk for higher claim counts. On the other hand, the effects of vehicle type increase as the quantile increases, indicating that the rating factor has higher risk for higher claim counts. � 2015 Elsevier B.V.
dc.description.natureFinalen_US
dc.identifier.CODENIMECD
dc.identifier.doi10.1016/j.insmatheco.2015.11.004
dc.identifier.epage137
dc.identifier.issn1676687
dc.identifier.scopus2-s2.0-84949482075
dc.identifier.spage124
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84949482075&doi=10.1016%2fj.insmatheco.2015.11.004&partnerID=40&md5=a86c3e067740b0cceb2d328064bbc20b
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/8676
dc.identifier.volume66
dc.languageEnglish
dc.language.isoen_US
dc.publisherElsevieren_US
dc.relation.ispartofInsurance: Mathematics and Economics
dc.sourceScopus
dc.titleBayesian quantile regression model for claim count data
dc.typeArticleen_US
dspace.entity.typePublication

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