ReviewJournal of statistical distributions and applications2021
A comparison of zero-inflated and hurdle models for modeling zero-inflated count data.
Review in Journal of statistical distributions and applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 papers.
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Who cites it
104 citing papers in PubMed.
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44 more citing papers are in PubMed but not listed here.
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1 author.
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Abstract
Counts data with excessive zeros are frequently encountered in practice. For example, the number of health services visits often includes many zeros representing the patients with no utilization during a follow-up time. A common feature of this type of data is that the count measure tends to have excessive zero beyond a common count distribution can accommodate, such as Poisson or negative binomial. Zero-inflated or hurdle models are often used to fit such data. Despite the increasing popularity of ZI and hurdle models, there is still a lack of investigation of the fundamental differences between these two types of models. In this article, we reviewed the zero-inflated and hurdle models and highlighted their differences in terms of their data generating processes. We also conducted simulation studies to evaluate the performances of both types of models. The final choice of regression model should be made after a careful assessment of goodness of fit and should be tailored to a particular data in question.
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