- Tytuł:
- Robust Bayesian insurance premium in a collective risk model with distorted priors under the generalised Bregman loss
- Autorzy:
- Boratyńska, Agata
- Powiązania:
- https://bibliotekanauki.pl/articles/1827546.pdf
- Data publikacji:
- 2021-09-06
- Wydawca:
- Główny Urząd Statystyczny
- Tematy:
-
classes of priors
posterior regret
distortion function
Bregman loss
insurance premium - Opis:
- 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 squareerror, 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.
- Źródło:
-
Statistics in Transition new series; 2021, 22, 3; 123-140
1234-7655 - Pojawia się w:
- Statistics in Transition new series
- Dostawca treści:
- Biblioteka Nauki
Artykuł