In this note, we examine the performance of 25 new composite models that are derived from 5 underlying folded distributions for modeling insurance loss data. These models are assessed using standard selection criteria involving the Akaike Information Criteria and the Bayesian Information Criteria as well as proximity to empirical risk estimates. Three models are found significant in improving the goodness-of-fit than the latest development in the literature with two models reliable for risk estimation.

中文翻译：

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具有基础折叠分布的复合模型

在本说明中，我们检查了从5个基础折叠分布中得出的25个新复合模型的性能，这些模型用于对保险损失数据进行建模。这些模型使用涉及Akaike信息标准和贝叶斯信息标准的标准选择标准进行评估，并且接近经验风险估计。与文献中的最新发展相比，发现三个模型在改善拟合优度方面具有重要意义，其中两个模型可用于风险估计。