A Bayesian Approach for Zero-Inflated Count Regression Models by Using the Reversible Jump Markov Chain Monte Carlo Method and an Application

DOI10.1080/03610920902985436zbMath1318.62088OpenAlexW2038272609MaRDI QIDQ3585304

İlknur Özmen, Haydar Demirhan

Publication date: 19 August 2010

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610920902985436

zbMATH Keywords

hierarchical modelingMetropolis-Hastings algorithmGibbs samplingzero-inflationlog-gamma distributionBayesian posterior estimatespotential scale reduction factorzoological data

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Generalized linear models (logistic models) (62J12)

Cites Work

• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• A Bayesian analysis of zero-inflated generalized Poisson model
• Prior induction in log-linear models for general contingency table analysis.
• Bayesian analysis of zero-inflated regression models
• The Analysis of Rates Using Poisson Regression Models
• Negative binomial and mixed poisson regression
• Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing
• Zero-Inflated Poisson Models and C.A.MAN: A Tutorial Collection of Evidence
• Restricted generalized poisson regression model
• Bayesian Analysis of Proportional Hazards Models Built from Monotone Functions
• Zero‐Altered and other Regression Models for Count Data with Added Zeros
• Markov chain Monte Carlo model determination for hierarchical and graphical log-linear models
• Full Bayesian Significance Test for Zero-Inflated Distributions

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