The most commonly used regression model in general insurance pricing is the compound Poisson model with gamma claim sizes. There are two different parametrizations for this model: the Poisson-gamma parametrization and Tweedie’s compound Poisson parametrization. Insurance industry typically prefers the Poisson-gamma parametrization. We review both parametrizations, provide new results that help to lower computational costs for Tweedie’s compound Poisson parameter estimation within generalized linear models, and we provide evidence supporting the industry preference for the Poisson-gamma parametrization.

中文翻译：

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使Tweedie的复合泊松模型更易于访问

一般保险定价中最常用的回归模型是具有伽玛索赔额的复合泊松模型。此模型有两种不同的参数设置：泊松-伽玛参数化和特威迪的复合泊松参数化。保险行业通常更喜欢泊松伽玛参数化。我们回顾了这两个参数设置，提供了有助于降低广义线性模型中Tweedie的复合Poisson参数估计的计算成本的新结果，并且我们提供了支持Poisson-gamma参数化行业偏好的证据。