Zobrazeno 1 - 6
of 6
pro vyhledávání: '"N. Miklós Arató"'
Autor:
N. Miklós Arató, László Martinek
Publikováno v:
Risks, Vol 10, Iss 11, p 204 (2022)
We analyse four stochastic claims reserving methods in terms of their capability to estimate reserve risk and how successful they are at predicting distributions and VaRs of claim developments in particular. Both actual data and hypothetical claim tr
Externí odkaz:
https://doaj.org/article/a30ffb2e4ee84d69afae5a1373744568
Autor:
László Martinek, N. Miklós Arató
Publikováno v:
Insurance: Mathematics and Economics. 85:205-217
A posteriori ratemaking is widely applied in the premium calculation of property and casualty products, particularly in third-party automobile insurance, which usually uses a bonus–malus system for premium adjustment. The present paper suggests an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c477113aa4a2a3eaec9e64a7c06ea9c6
https://doi.org/10.1016/j.insmatheco.2019.01.008
https://doi.org/10.1016/j.insmatheco.2019.01.008
Publikováno v:
Computational Statistics & Data Analysis. 51:1347-1363
Hierarchical Bayesian modelling is considered for the number of age-dependent deaths in different geographic regions. The model uses a conditional binomial distribution for the number of age-dependent deaths, a new family of zero mean Gaussian Markov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::84807bcaafcac4534683a4aef9304846
https://doi.org/10.1016/j.csda.2006.02.007
https://doi.org/10.1016/j.csda.2006.02.007
Publikováno v:
Advances in Data Analysis ISBN: 9780817647988
We analyse here the spatial dependence structure for the counts of certain type of claims occurring in household insurance in Hungary. We build up a Bayesian hierarchical model for the Gaussian Markov random field driven nonhomogeneous spatial Poisso
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::17b7d19537e9eb843f70cd2489c1db0b
https://doi.org/10.1007/978-0-8176-4799-5_20
https://doi.org/10.1007/978-0-8176-4799-5_20
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