An empirical approach to determine a threshold for assessing overdispersion in Poisson and negative binomial models for count data

DOI10.1080/03610918.2017.1323223OpenAlexW2612063744WikidataQ90569297 ScholiaQ90569297MaRDI QIDQ5084950

Viswanathan Ramakrishnan, James W. Hardin, Leonard E. Egede, Mulugeta Gebregziabher, Elizabeth H. Payne

Publication date: 29 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://europepmc.org/articles/pmc6290908

zbMATH Keywords

outlierscount dataoverdispersionPearson chi-squarezero inflation

Mathematics Subject Classification ID

Generalized linear models (logistic models) (62J12) Parametric inference (62Fxx)

Related Items (1)

Application of a count data model to evaluate the anti-metastatic efficacy of QAP14 in 4T1 breast cancer allografts

Uses Software

• COUNT
• glm

Cites Work

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• A framework for modelling overdispersed count data, including the Poisson-shifted generalized inverse Gaussian distribution
• A score test for overdispersion in Poisson regression based on the generalized Poisson-2 model
• Overdispersion Diagnostics for Generalized Linear Models
• Testing Goodness of Fit for the Poisson Assumption When Observations Are Not Identically Distributed
• Regression Methods for Poisson Process Data
• Tests for detecting overdispersion in poisson models
• A comparison of tests for overdispersion in generalized linear models
• Some remarks on overdispersion
• Testing Approaches for Overdispersion in Poisson Regression versus the Generalized Poisson Model
• Analysis of count data with covariate dependence in both mean and variance
• Negative Binomial Regression

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