ReviewJournal of applied statistics2023
Revisiting the analysis pipeline for overdispersed Poisson and binomial data.
Review in Journal of applied statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Association Between Sedentary Behavior and the Development of New-Onset Chronic Pain in Older American Adults: Insights From the Health and Retirement Study.American journal of health promotion : AJHP · 2026Article
- On the robustness of truncated negative binomial regression model: application to field epidemiology.Journal of applied statistics · 2026Article
- Transcription start sites experience a high influx of heritable variants fueled by early development.Nature communications · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Overdispersion is a common feature in categorical data analysis and several methods have been developed for detecting and handling it in generalized linear models. The first aim of this study is to clarify the relationships among various score statistics for testing overdispersion and to compare their performances. In addition, we investigate a principled way to correct finite sample bias in the score statistic caused by estimating regression parameters with restricted likelihood. The second aim is to reconsider the current practice for handling overdispersed categorical data. Although the conventional models are based on substantially different mechanisms for generating overdispersion, model selection in practice has not been well studied. We perform an intensive numerical study for determining which method is more robust to various overdispersion mechanisms. In addition, we provide some graphical tools for identifying the better model. The last aim is to reconsider the key assumption for deriving the score statistics. We study the meaning of testing overdispersion when this assumption is violated, and we analytically show the conditions for which it is not appropriate to employ the current statistical practices for analyzing overdispersed data.
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Registered trials
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