ArticleStatistical methods in medical research2019
A test of inflated zeros for Poisson regression models.
Article in Statistical methods in medical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Trial
- Analysing dengue fever spread in Kenya using the Zero-Inflated Poisson model.Journal of public health in Africa · 2025Article
- A Lindley-binomial model for analyzing the proportions with sparseness and excessive zeros.Journal of applied statistics · 2024Article
- Fewer Children in Families Associated with Lower Odds of Early Childhood Caries: A Sample from Three Countries.International journal of environmental research and public health · 2023Article
- Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data.Methods in molecular biology (Clifton, N.J.) · 2023Review
- A GEE-type approach to untangle structural and random zeros in predictors.Statistical methods in medical research · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Excessive zeros are common in practice and may cause overdispersion and invalidate inference when fitting Poisson regression models. There is a large body of literature on zero-inflated Poisson models. However, methods for testing whether there are excessive zeros are less well developed. The Vuong test comparing a Poisson and a zero-inflated Poisson model is commonly applied in practice. However, the type I error of the test often deviates seriously from the nominal level, rendering serious doubts on the validity of the test in such applications. In this paper, we develop a new approach for testing inflated zeros under the Poisson model. Unlike the Vuong test for inflated zeros, our method does not require a zero-inflated Poisson model to perform the test. Simulation studies show that when compared with the Vuong test our approach not only better at controlling type I error rate, but also yield more power.
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Registered trials
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