变离散泊松-逆回归模型的电磁估计:在保险费率制定中的应用

G. Tzougas
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引用次数: 11

摘要

本文提出了具有不同色散的泊松-反伽玛回归模型,用于近似重尾和过分散的索赔计数。我们的主要贡献是我们开发了一种期望最大化(EM)类型的算法,用于具有不同色散的泊松-逆伽玛回归模型的最大似然(ML)估计。实证分析考察了一组汽车保险数据,以调查所提出算法的效率。最后,将泊松-逆伽玛模型确定的先验和后验溢价率,或奖励-损失溢价率,与经典负二项I型和泊松-逆高斯分布的结果进行比较,这些分布具有均值和分散参数的回归结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EM Estimation for the Poisson-Inverse Gamma Regression Model with Varying Dispersion: An Application to Insurance Ratemaking
This article presents the Poisson-Inverse Gamma regression model with varying dispersion for approximating heavy-tailed and overdispersed claim counts. Our main contribution is that we develop an Expectation-Maximization (EM) type algorithm for maximum likelihood (ML) estimation of the Poisson-Inverse Gamma regression model with varying dispersion. The empirical analysis examines a portfolio of motor insurance data in order to investigate the efficiency of the proposed algorithm. Finally, both the a priori and a posteriori, or Bonus-Malus, premium rates that are determined by the Poisson-Inverse Gamma model are compared to those that result from the classic Negative Binomial Type I and the Poisson-Inverse Gaussian distributions with regression structures for their mean and dispersion parameters.
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