Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Tsz Chai Fung"'
Publikováno v:
Journal of the Royal Statistical Society Series A: Statistics in Society. 186:61-83
The aim of this paper is to present a regression model for multivariate claim frequency data with dependence structures across the claim count responses, which may be of different sign and range, and overdispersion from the unobserved heterogeneity d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b398afa48bb02cf38f20c8c7eedf42f7
https://doi.org/10.1093/jrsssa/qnac010
https://doi.org/10.1093/jrsssa/qnac010
Autor:
Tsz Chai Fung
Publikováno v:
Insurance: Mathematics and Economics. 107:180-198
In this article, we present the maximum weighted likelihood estimator (MWLE) for robust estimations of heavy-tail finite mixture models (FMM). This is motivated by the complex distributional phenomena of insurance claim severity data, where flexible
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d86f32ffa3d08a2f525083bc0466e644
https://doi.org/10.1016/j.insmatheco.2022.08.008
https://doi.org/10.1016/j.insmatheco.2022.08.008
Publikováno v:
North American Actuarial Journal. 26:496-520
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dcb7d6d351cd7fc4d80764be522f426e
https://doi.org/10.1080/10920277.2021.2013896
https://doi.org/10.1080/10920277.2021.2013896
Publikováno v:
Actuary. Oct2021, p24-27. 4p.
Publikováno v:
Annals of Actuarial Science. :1-22
This paper introduces a new julia package, LRMoE, a statistical software tailor-made for actuarial applications, which allows actuarial researchers and practitioners to model and analyse insurance loss frequencies and severities using the Logit-weigh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a57ececef85ef35f34d2f641f7b1e8a1
https://doi.org/10.1017/s1748499521000087
https://doi.org/10.1017/s1748499521000087
Publikováno v:
North American Actuarial Journal. 25:206-231
Insurance loss severity data often exhibit heavy-tailed behavior, complex distributional characteristics such as multimodality, and peculiar links between policyholders’ risk profiles and claim amo...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::102849cc658ecd2e15df79c82e69d45a
https://doi.org/10.1080/10920277.2020.1729813
https://doi.org/10.1080/10920277.2020.1729813
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::917a6d004c8445da1665b5181e216dc0
https://doi.org/10.2139/ssrn.4135432
https://doi.org/10.2139/ssrn.4135432
Publikováno v:
Insurance: Mathematics and Economics. 89:111-127
In the Property and Casualty (PC and Expert functions, which govern the distributional properties of the claims. Also, upon the development of denseness theory in regression setting, we can heuristically interpret the LRMoE as a “fully flexible”
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fd73140e2ca7a9717b565c69daaa93f6
https://doi.org/10.1016/j.insmatheco.2019.09.007
https://doi.org/10.1016/j.insmatheco.2019.09.007
Publikováno v:
ASTIN Bulletin. 49:647-688
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 identif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::95d150086321900de981a5b0df135572
https://doi.org/10.1017/asb.2019.25
https://doi.org/10.1017/asb.2019.25
Publikováno v:
Scandinavian Actuarial Journal. 2019:686-710
Modeling multivariate time-series aggregate losses is an important actuarial topic that is very challenging due to the fact that losses can be serially dependent with heterogeneous dependen...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b2411ad03cf3152de69808c6765ffe4c
https://doi.org/10.1080/03461238.2019.1598482
https://doi.org/10.1080/03461238.2019.1598482