Zero-inflated and overdispersed: what's one to do?
Zero-inflated and overdispersed: what's one to do?
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DOI：
10.1080/00949655.2012.668550
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发表时间：
2013-09-01
影响因子：
1.2
通讯作者：
Aban, Inmaculada
中科院分区：
文献类型：
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作者：
Perumean-Chaney, Suzanne E.;Morgan, Charity;Aban, Inmaculada
建议使用零膨胀泊松（ZIP）和零膨胀负二项式（ZINB）模型来处理计数数据中过多的零。由于各种原因，研究人员可能无法解决零通胀问题。本文有助于教育研究人员对零通货膨胀的重要性和错误指定统计模型的后果。通过模拟，我们发现当忽略数据中的零通货膨胀时，估计很差，并且遗漏了统计上显著的发现。当忽略零膨胀数据内的过分散时，就会产生较差的估计和膨胀的I型误差。提供了关于何时使用ZINB和ZIP模型的建议。在使用两步模型选择程序（似然比检验和Vuong检验）的示例中，只有当分布具有中等均值和样本量时才能正确识别ZIP模型，并且不能正确识别ZINB模型或ZIP和ZINB分布中的零通货膨胀。
Zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models are recommended for handling excessive zeros in count data. For various reasons, researchers may not address zero inflation. This paper helps educate researchers on the importance of accounting for zero inflation and the consequences of misspecifying the statistical model. Using simulations, we found that when the zero inflation in the data was ignored, estimation was poor and statistically significant findings were missed. When overdispersion within the zero-inflated data was ignored, poor estimation and inflated Type I errors resulted. Recommendations on when to use the ZINB and ZIP models are provided. In an illustration using a two-step model selection procedure (likelihood ratio test and the Vuong test), the ZIP model was correctly identified only when the distributions had moderate means and sample sizes and did not correctly identify the ZINB model or the zero inflation in the ZIP and ZINB distributions.