Analysis of zero-inflated clustered count data: a marginalized model approach

DOI10.1016/J.CSDA.2010.07.005zbMath1247.62108OpenAlexW2049680061MaRDI QIDQ452693

Keunbaik Lee, Joon Jin Song, Dee Wood Harper, Youngsung Joo

Publication date: 15 September 2012

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2010.07.005

zbMATH Keywords

random effectshurdle modelsquasi-NewtonZIP models

Mathematics Subject Classification ID

Nonparametric estimation (62G05)

Related Items (11)

Marginalized zero-inflated generalized Poisson regression ⋮ Clustering for time-varying relational count data ⋮ Analysis of long series of longitudinal ordinal data using marginalized models ⋮ Marginalized models for longitudinal count data ⋮ Addressing overdispersion and zero-inflation for clustered count data via new multilevel heterogenous hurdle models ⋮ Modeling the random effects covariance matrix for generalized linear mixed models ⋮ Regression analysis of zero-inflated time-series counts: application to air pollution related emergency room visit data ⋮ First-order marginalised transition random effects models with probit link function ⋮ Multilevel zero-inflated generalized Poisson regression modeling for dispersed correlated count data ⋮ Marginalized transition shared random effects models for longitudinal binary data with nonignorable dropout ⋮ Joint frailty models for zero-inflated recurrent events in the presence of a terminal event

Cites Work

• Trend in correlated proportions
• Misspecified maximum likelihood estimates and generalised linear mixed models
• Likelihood-based approach for analysis of longitudinal nominal data using marginalized random effects models
• A Score Test for Testing a Zero‐Inflated Poisson Regression Model Against Zero‐Inflated Negative Binomial Alternatives
• Marginal models for zero inflated clustered data
• Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing
• Theory & Methods: Modelling Correlated Zero‐inflated Count Data
• Marginally Specified Logistic‐Normal Models for Longitudinal Binary Data
• Zero‐Inflated Poisson and Binomial Regression with Random Effects: A Case Study
• A Random-Effects Ordinal Regression Model for Multilevel Analysis
• Random effect models for repeated measures of zero-inflated count data

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