Mixture model framework facilitates understanding of zero-inflated and hurdle models for count data
Mixture model framework facilitates understanding of zero-inflated and hurdle models for count data
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DOI：
10.1080/10543400701514098
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发表时间：
2007-01-01
影响因子：
1.1
通讯作者：
Baughman, A. L.
中科院分区：
文献类型：
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作者：
Baughman, A. L.
在本文中，我们对Rose等人提出的计数数据的零膨胀模型和障碍模型进行了评论，2006年，J. Biobarma. Stat. 16：463-481。通过将这些模型视为有限混合模型，可以更好地理解模型的组成部分，包括对有限混合模型中潜在变量的假设。决定零膨胀模型或障碍模型是否适合给定的数据集需要与主题专家密切合作。例如，在对疫苗不良事件计数数据建模时，不良事件发生的药代动力学原理以及检测或报告不良事件的可能性是混合模型开发的重要考虑因素。
In this note, we comment on the Zero-inflated and hurdle models for count data presented by Rose et al., 2006, J. Biopharma. Stat. 16:463-481. By viewing these models as finite mixture models, one gains a better understanding of the components of the models, including assumptions about the latent variable(s) in the finite mixture models. Deciding whether a zero-inflated or hurdle model is appropriate for a given data set requires close collaboration with subject matter experts. For instance, in modeling vaccine adverse event count data, the pharmacokinetic rationale for the occurrence of an adverse event and the likelihood of detecting or reporting the adverse event are important considerations for mixture model development.