In applications involving count data, it is common to encounter an excess number of zeros. For example, in the study of outpatient service utilization, the number of utilization days will take on integer values, with many subjects having no utilization (zero values). Mixed distribution models, such as the zero-inflated Poisson and zero-inflated negative binomial, are often used to fit such data. A more general class of mixture models, called hurdle models, can be used to model zero deflation as well as zero inflation. Several authors have proposed frequentist approaches to fitting zero-inflated models for repeated measures. We describe a practical Bayesian approach which incorporates prior information, has optimal small-sample properties and allows for tractable inference. The approach can be easily implemented using standard Bayesian software. A study of psychiatric outpatient service use illustrates the methods.

中文翻译：

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用于重复测量零膨胀计数数据的贝叶斯模型，适用于门诊精神科服务使用

在涉及计数数据的应用程序中，经常会遇到过多的零。例如，在门诊服务利用的研究中，利用天数将取整数值，许多受试者没有利用（零值）。混合分布模型，例如零膨胀泊松和零膨胀负二项式，通常用于拟合此类数据。一类更通用的混合模型称为障碍模型，可用于模拟零通货紧缩和零通货膨胀。几位作者提出了频率论方法来拟合零膨胀模型以进行重复测量。我们描述了一种实用的贝叶斯方法，它结合了先验信息，具有最佳的小样本特性并允许进行易处理的推理。该方法可以使用标准贝叶斯软件轻松实现。