论文标题
过度分散零充气计数数据的指数分散模型
Exponential Dispersion Models for Overdispersed Zero-Inflated Count Data
论文作者
论文摘要
我们考虑了离散概率分布的三个新类的指数分散模型，这些模型是通过在其平均值参数化中指定其方差函数来定义的。在以前的论文（Bar-Lev and Ridder，2020a）中，我们开发了这些类别的框架，并证明它们具有一些理想的特性。这些类别中的每一个都被证明过度分散，零膨胀顺序使它们成为统计模型中使用者的竞争统计模型。在本文中，我们详细介绍了其概率质量函数的计算方面。此外，我们将这些类应用于拟合具有过度分散和零膨胀统计的真实数据集。基于泊松或负二项式分布的经典模型表现不佳，因此近年来已经提出了许多替代品。我们与这些其他建议进行了广泛的比较，我们可以从中得出结论，我们的框架是一种灵活的工具，可在所有情况下都能提供出色的结果。此外，在大多数情况下，我们的模型可以最合适。
We consider three new classes of exponential dispersion models of discrete probability distributions which are defined by specifying their variance functions in their mean value parameterization. In a previous paper (Bar-Lev and Ridder, 2020a), we have developed the framework of these classes and proved that they have some desirable properties. Each of these classes was shown to be overdispersed and zero inflated in ascending order, making them as competitive statistical models for those in use in statistical modeling. In this paper we elaborate on the computational aspects of their probability mass functions. Furthermore, we apply these classes for fitting real data sets having overdispersed and zero-inflated statistics. Classic models based on Poisson or negative binomial distributions show poor fits, and therefore many alternatives have already proposed in recent years. We execute an extensive comparison with these other proposals, from which we may conclude that our framework is a flexible tool that gives excellent results in all cases. Moreover, in most cases our model gives the best fit.
