偏态Student- t分布下保险风险的客观贝叶斯模型
Objective Bayesian modelling of insurance risks with the skewed Student-t distribution
摘要：
保险风险数据通常表现出扭曲的行为。在本文中，我们提出了一种贝叶斯方法来捕捉这些数据集的主要特征。这项工作扩展了Villa和Walker（2014 a）中介绍的方法，考虑了一个额外的参数，该参数捕获了数据的偏斜度。特别是，一个偏态的学生- t分布被认为是。分析了两个数据集：丹麦火灾损失和美国赔偿损失。进行了分析与客观贝叶斯方法。对于表示自由度数量的离散参数，我们采用了最近在Villa和Walker（2014 b）中引入的新先验。
英文摘要：
Insurance risks data typically exhibit skewed behaviour. In this paper, we propose a Bayesian approach to capture the main features of these datasets. This work extends the methodology introduced in Villa and Walker (2014a) by considering an extra parameter which captures the skewness of the data. In particular, a skewed Student-t distribution is considered. Two datasets are analysed: the Danish fire losses and the US indemnity loss. The analysis is carried with an objective Bayesian approach. For the discrete parameter representing the number of the degrees of freedom, we adopt a novel prior recently introduced in Villa and Walker (2014b).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
