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Autor
Boratyńska Agata (Warsaw School of Economics, Poland)
Tytuł
Robust Bayesian Insurance Premium in a Collective Risk Model with Distorted Priors under the Generalised Bregman Loss
Źródło
Statistics in Transition, 2021, vol. 22, nr 3, s. 123-140, tab., wykr., bibliogr. 25 poz.
Słowa kluczowe
Składki ubezpieczeniowe, Ryzyko, Roszczenia
Insurance premium, Risk, Claims
Uwagi
summ.
Abstrakt
The article presents a collective risk model for the insurance claims. The objective is to estimate a premium, which is defined as a functional specified up to unknown parameters. For this purpose, the Bayesian methodology, which combines the prior knowledge about certain unknown parameters with the knowledge in the form of a random sample, has been adopted. The generalised Bregman loss function is considered. In effect, the results can be applied to numerous loss functions, including the square-error, LINEX, weighted square-error, Brown, entropy loss. Some uncertainty about a prior is assumed by a distorted band class of priors. The range of collective and Bayes premiums is calculated and posterior regret Γ-minimax premium as a robust procedure has been implemented. Two examples are provided to illustrate the issues considered - the first one with an unknown parameter of the Poisson distribution, and the second one with unknown parameters of distributions of the number and severity of claims.(original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka SGH im. Profesora Andrzeja Grodka
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Pełny tekst
Pokaż
Bibliografia
Pokaż
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Cytowane przez
Pokaż
ISSN
1234-7655
Język
eng
URI / DOI
http://dx.doi.org/10.21307/stattrans-2021-030
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