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Robust Bayesian bonus-malus premiums under the conditional specification model

机译:条件规格模型下的稳健贝叶斯红利-保费保费

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摘要

The conditional specification technique introduced by Arnold et al. (Conditional specification of statistical models. Springer series in statistics. Springer, New York, 1999) was used in Sarabia et al. (Astin Bull 34(1):85-98, 2004) to obtain bonus-malus premiums. The Poisson distribution for which the parameter is a function of the classical structure parameter was used and a new class of prior distributions appeared in a natural way. This model contains, as a particular case, the classical compound Poisson model. In the present paper, the Bayesian robustness of this new model is examined and found to be much more robust than in the classical model in GA(3)mez et al. (Insur Math Econ 31:105-113, 2002). For the present study, the moment conditions on the prior distribution are required. Examples, with real data, are given to illustrate our ideas under the net and exponential premium principles.
机译:Arnold等人介绍的条件规范技术。 (统计模型的条件规范。统计中的Springer系列。Springer,纽约,1999年)在Sarabia等人中使用。 (Astin Bull 34(1):85-98,2004),以获得红利奖金。使用参数是经典结构参数的函数的泊松分布,并且以自然的方式出现了一类新的先验分布。作为特殊情况,该模型包含经典的复合泊松模型。在本文中,对该新模型的贝叶斯鲁棒性进行了检验,发现它比GA(3)mez等人的经典模型更鲁棒。 (Insur Math Econ 31:105-113,2002)。对于本研究,需要先验分布上的弯矩条件。给出了带有真实数据的示例,以说明我们在净溢价和指数溢价原则下的想法。

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