Addressing overdispersion and zero-inflation for clustered count data via new multilevel heterogenous hurdle models

DOI10.1080/02664763.2022.2096875OpenAlexW4288037890MaRDI QIDQ6157175

Yasin Altinisik

Publication date: 19 June 2023

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

Full work available at URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9870003

zbMATH Keywords

count dataoverdispersionPoisson-Lindley distributionzero-inflationmultilevel modelingPoisson-ailamujia distribution

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Semi-Nonparametric Maximum Likelihood Estimation
• A combined overdispersed and marginalized multilevel model
• Analysis of zero-inflated clustered count data: a marginalized model approach
• Examples in which misspecification of a random effects distribution reduces efficiency, and possible remedies
• Assessing normality in random effects models
• Estimating the dimension of a model
• A random-effects hurdle model for predicting bycatch of endangered marine species
• A comparison of zero-inflated and hurdle models for modeling zero-inflated count data
• Misspecified maximum likelihood estimates and generalised linear mixed models
• Some Covariance Models for Longitudinal Count Data with Overdispersion
• Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros
• Estimation methods for the discrete Poisson–Lindley distribution
• Linear Inversion of Band-Limited Reflection Seismograms
• A Biometrics Invited Paper. The Analysis and Selection of Variables in Linear Regression
• Zero‐Inflated Poisson and Binomial Regression with Random Effects: A Case Study
• Zero‐Altered and other Regression Models for Count Data with Added Zeros
• A zero-inflated overdispersed hierarchical Poisson model
• Type I and Type II Error Under Random‐Effects Misspecification in Generalized Linear Mixed Models
• Random effect models for repeated measures of zero-inflated count data
• Cholesky Residuals for Assessing Normal Errors in a Linear Model With Correlated Outcomes
• Unit-Lindley mixed-effect model for proportion data
• Linear mixed models for longitudinal data
• A new look at the statistical model identification

This page was built for publication: Addressing overdispersion and zero-inflation for clustered count data via new multilevel heterogenous hurdle models