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2005 | 14 | 11-25

Article title

Robust Bayesian estimation of insurance premium in collective risk model

Authors

Selected contents from this journal

Title variants

Languages of publication

PL

Abstracts

EN
The collective risk model for the insurance claims is considered. The objective is to estimate a premium which is defined as a functional H specified up to an unknown parameter 'theta' (the expected number of claims). Four principles of calculating a premium are applied: net, variance principle, Esscher and exponential. The Bayesian methodology, which combines the prior knowledge about a parameter 'theta' with the knowledge in the form of a random sample, is adopted. Two loss functions (the square loss function and the asymmetric loss function LINEX) are considered. The obtained Bayes premium depends on a choice of a prior. Some uncertainty about a prior is assumed by introducing four classes of priors. The oscillation of the Bayes estimator is calculated. Considering one of the concepts of robust procedures the posterior regret 'Gamma' -minimax premiums are calculated as optimal robust premiums. A numerical example is presented.

Year

Volume

14

Pages

11-25

Physical description

Document type

ARTICLE

Contributors

author
  • A. Boratynska, Szkola Gl√≥wna Handlowa w Warszawie, Instytut Ekonometrii, al Niepodleglosci 164, 02-554 Warszawa, Poland,

References

Document Type

Publication order reference

Identifiers

CEJSH db identifier
06PLAAAA00731800

YADDA identifier

bwmeta1.element.6c470cc1-84c9-3bb7-96eb-20b8351e342f
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