论文标题
计数数据的新指数分散模型-ABM和LM类
New exponential dispersion models for count data -- the ABM and LM classes
论文作者
论文摘要
在其关于立方差异函数的基本论文中,Letac和Mora(《统计纪事》,1990年)提出了对真实线上自然指数家族的系统,严格和全面的研究,通过其差异函数和平均值参数参数化的表征。他们提出了一个部分,出于某种原因未被注意到。本节介绍了与非负整数集合分布的自然指数家族相关的方差函数的构建,并允许找到相应的生成措施。由于指数分散模型基于自然指数式家族,因此我们在本文中介绍了基于结果的两种新的指数分散模型。对于与简单方差函数相关联的这些类,我们得出它们的平均值参数化及其相关的生成度量。我们还证明它们具有一些理想的属性。两种类均显示出过度分散并按升顺序膨胀,这使得它们成为统计和精算模型中使用的人的竞争统计模型。据我们所知,我们在本文中提出的计数分布类别尚未在文献中引入或讨论。为了证明我们的课程可以用作使用的人的竞争统计模型(例如泊松,负二项式),我们包括一个真实数据的数值示例。在此示例中,我们将课程的表现与相关竞争模型进行了比较。
In their fundamental paper on cubic variance functions, Letac and Mora (The Annals of Statistics,1990) presented a systematic, rigorous and comprehensive study of natural exponential families on the real line, their characterization through their variance functions and mean value parameterization. They presented a section that for some reason has been left unnoticed. This section deals with the construction of variance functions associated with natural exponential families of counting distributions on the set of nonnegative integers and allows to find the corresponding generating measures. As exponential dispersion models are based on natural exponential families, we introduce in this paper two new classes of exponential dispersion models based on their results. For these classes, which are associated with simple variance functions, we derive their mean value parameterization and their associated generating measures. We also prove that they have some desirable properties. Both classes are shown to be overdispersed and zero-inflated in ascending order, making them as competitive statistical models for those in use in both, statistical and actuarial modeling. To our best knowledge, the classes of counting distributions we present in this paper, have not been introduced or discussed before in the literature. To show that our classes can serve as competitive statistical models for those in use (e.g., Poisson, Negative binomial), we include a numerical example of real data. In this example, we compare the performance of our classes with relevant competitive models.