A Flexible Bayesian Nonparametric Model for Predicting Future Insurance Claims
A Flexible Bayesian Nonparametric Model for Predicting Future Insurance Claims
复制标题
用于预测未来保险索赔的灵活贝叶斯非参数模型
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Ryan Martin
中科院分区：
文献类型：
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作者：
Liang Hong;Ryan Martin
准确预测未来索赔是保险业中一个非常重要的问题。贝叶斯方法在这种情况下是自然的，因为它为未来的索赔提供了一个完整的预测分布。经典的可信度理论提供了一个简单的近似的预测分布的平均值作为一个点预测，但这种方法忽略了预测分布的其他功能，如传播，这将是有用的决策。本文提出了一种对数正态混合Dirichlet过程模型，并讨论了相应的预测分布的理论性质和计算方法。数值例子表明，我们的模型相比，一些现有的保险损失模型的好处，并提出的方法的R代码实现。
ABSTRACT Accurate prediction of future claims is a fundamentally important problem in insurance. The Bayesian approach is natural in this context, as it provides a complete predictive distribution for future claims. The classical credibility theory provides a simple approximation to the mean of that predictive distribution as a point predictor, but this approach ignores other features of the predictive distribution, such as spread, that would be useful for decision making. In this article, we propose a Dirichlet process mixture of log-normals model and discuss the theoretical properties and computation of the corresponding predictive distribution. Numerical examples demonstrate the benefit of our model compared to some existing insurance loss models, and an R code implementation of the proposed method is also provided.