In this article, we propose a class of zero-modified Poisson mixture models as an alternative to model overdispersed count data exhibiting inflation or deflation of zeros. A relevant feature of this class is that the zero modification can be incorporated using a zero truncation process and consequently, the proposed models can be expressed in the hurdle version. This procedure leads to the fact that the proposed models can be fitted without any previous information about the zero modification present in agiven dataset. A fully Bayesian approach has been considered for estimation and inference concerns. Three different simulation studies have been conducted to illustrate the performance of the developed methodology. The usefulness of the proposed class of models has been assessed by using three real datasets provided by the literature. A general model comparison with some well-known discrete distributions has been presented.

中文翻译：

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一些零修正泊松混合模型的贝叶斯方法

在本文中，我们提出了一类零修正泊松混合模型，作为模型过度分散的计数数据的替代方案，这些数据表现出零的膨胀或紧缩。此类的一个相关特征是可以使用零截断过程合并零修改，因此，建议的模型可以在障碍版本中表示。这个过程导致这样一个事实，即可以在没有关于给定数据集中存在的零修改的任何先前信息的情况下拟合所提出的模型。完全贝叶斯方法已被考虑用于估计和推理问题。已经进行了三种不同的模拟研究来说明所开发方法的性能。已通过使用文献提供的三个真实数据集评估了所提出的模型类别的有用性。