Analyzing clustered count data with a cluster-specific random effect zero-inflated Conway–Maxwell–Poisson distribution

DOI10.1080/02664763.2017.1312299OpenAlexW2605042271WikidataQ64990258 ScholiaQ64990258MaRDI QIDQ5035761

Hyoyoung Choo-Wosoba, Somnath Datta

Publication date: 22 February 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2017.1312299

zbMATH Keywords

Poisson distributionmixed effects modelGaussian-Hermite (G-H) quadraturenext-generation sequencing (NGS) dataunder- and over-dispersions

Mathematics Subject Classification ID

Applications of statistics (62Pxx)

Related Items

On some aspects of a zero-inflated overdispersed model and its applications, A multilevel zero-inflated Conway–Maxwell type negative binomial model for analysing clustered count data, Analyzing longitudinal clustered count data with zero inflation: Marginal modeling using the Conway–Maxwell–Poisson distribution, GEE-based zero-inflated generalized Poisson model for clustered over or under-dispersed count data, Underdispersion models: Models that are “under the radar”, Zero-inflated count time series models using Gaussian copula, Three-level zero-inflated Conway-Maxwell-Poisson regression model for analyzing dispersed clustered count data with extra zeros

Uses Software

• fastGHQuad

Cites Work

• A flexible regression model for count data
• A flexible zero-inflated model to address data dispersion
• Score tests for zero-inflated Poisson models
• The zero-inflated Conway-Maxwell-Poisson distribution: Bayesian inference, regression modeling and influence diagnostic
• Mixed model and estimating equation approaches for zero inflation in clustered binary response data with application to a dating violence study
• On the Effect of the Number of Quadrature Points in a Logistic Random Effects Model: An Example
• Zero‐Inflated Poisson and Binomial Regression with Random Effects: A Case Study
• Zero-Inflated Negative Binomial Mixed Regression Modeling of Over-Dispersed Count Data with Extra Zeros
• Pattern‐Mixture Zero‐Inflated Mixed Models for Longitudinal Unbalanced Count Data with Excessive Zeros
• Random effect models for repeated measures of zero-inflated count data
• Marginal regression models for clustered count data based on zero‐inflated Conway–Maxwell–Poisson distribution with applications
• A Useful Distribution for Fitting Discrete Data: Revival of the Conway–Maxwell–Poisson Distribution