Marginal regression models for clustered count data based on zero‐inflated Conway–Maxwell–Poisson distribution with applications

DOI10.1111/biom.12436zbMath1419.62326OpenAlexW2172763334WikidataQ31024485 ScholiaQ31024485MaRDI QIDQ5739291

Hyoyoung Choo-Wosoba, Somnath Datta, Steven M. Levy

Publication date: 15 July 2016

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc4948193

zbMATH Keywords

bootstrapgeneralized estimating equationgeneralized linear modelgenomicsIowa Fluoride Studyexpectation-solution algorithmcaries data

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Related Items (10)

Analyzing clustered count data with a cluster-specific random effect zero-inflated Conway–Maxwell–Poisson distribution ⋮ A multilevel zero-inflated Conway–Maxwell type negative binomial model for analysing clustered count data ⋮ A flexible regression model for zero- and k-inflated count data ⋮ Analyzing longitudinal clustered count data with zero inflation: Marginal modeling using the Conway–Maxwell–Poisson distribution ⋮ Likelihood-based tests in zero-inflated power series models ⋮ GEE-based zero-inflated generalized Poisson model for clustered over or under-dispersed count data ⋮ Underdispersion models: Models that are “under the radar” ⋮ A flexible zero-inflated model to address data dispersion ⋮ A new long-term survival model with dispersion induced by discrete frailty ⋮ A simple and useful regression model for fitting count data

Cites Work

• A flexible regression model for count data
• Longitudinal data analysis using generalized linear models
• GEE type inference for clustered zero-inflated negative binomial regression with application to dental caries
• Marginal models for zero inflated clustered data
• Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing
• Zero-Inflated Poisson Models and C.A.MAN: A Tutorial Collection of Evidence
• Mixtures of marginal models
• A Useful Distribution for Fitting Discrete Data: Revival of the Conway–Maxwell–Poisson Distribution
• The S-U algorithm for missing data problems

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