标题： 目标贝叶斯检验泊松与过度泊松模型 显示英文标题

标题： Objective Bayes testing of Poisson versus inflated Poisson models

摘要： 泊松分布常被用作计数数据的标准模型。 然而，这样的数据集往往不能很好地由泊松模型拟合，因为它们包含的零值比该模型兼容的要多。 对于这些情况，通常建议使用零膨胀泊松（ZIP）分布。 本文讨论了使用基于合适客观先验的贝叶斯方法来检验泊松模型与ZIP模型。 具体的选择了客观先验，并对其性质进行了研究。 该方法被扩展以在回归模型中包含协变量。 给出了几个应用实例。 显示英文摘要

摘要： The Poisson distribution is often used as a standard model for count data. Quite often, however, such data sets are not well fit by a Poisson model because they have more zeros than are compatible with this model. For these situations, a zero-inflated Poisson (ZIP) distribution is often proposed. This article addresses testing a Poisson versus a ZIP model, using Bayesian methodology based on suitable objective priors. Specific choices of objective priors are justified and their properties investigated. The methodology is extended to include covariates in regression models. Several applications are given.