Zero‐Inflated Poisson and Binomial Regression with Random Effects: A Case Study

By Dan Hall. 2000

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Subfields: Plant Science, Statistics and Probability

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Citations in the corpus

A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use (2010, background): “Hall (2000), Yau and Lee (2001) and Min and Agresti (2005) have proposed various random intercept zero-inflated models.”
Trajectories of Pure and Co-Occurring Internalizing and Externalizing Problems from Age 2 to Age 12: Findings from the NICHD Study of Early Child Care (2007, positive): “Furthermore, because all the variables were highly skewed with a large number of zeros at each time point, a zero inflated Poison (ZIP) model was used (Hall, 2000; Kreuter & Muthén, 2006; Lambert, 1992; Nagin & Land, 1993).”
Multilevel zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros (2008, background): “Hall [6] has considered clustered data and shown how mixed effect versions of zero-inflated Poisson (ZIP) and zero-inflated binomial (ZIB) models are appropriate.”
Modeling Nonnegative Data with Clumping at Zero: A Survey (2002, methodology): “Hall (2000) extended the zeroinflated Poisson and zero-inflated binomial models to handle longitudinal data, adding random effects to account for the within-subject dependence.”
A spatial zero-inflated poisson regression model for oak regeneration (2006, neutral): “Hall (2000) introduced zero-inflated Poisson models with random effects for applications in longitudinal data analysis.”
Modeling annualized occurrence, frequency, and composition of ingrowth using mixed-effects zero-inflated models and permanent plots in the Acadian Forest Region of North America (2011, background): “Although the theoretical framework of zero-inflated models was well established more than a decade ago (Lambert 1992; Hall 2000), their applications in forestry are limited due to the complexity and difficulty in obtaining convergence.”
Growth Curve Models for Zero-Inflated Count Data: An Application to Smoking Behavior (2007, methodology): “Other models such as the zero-inflated negative binomial (ZINB) model provide additional correction for overdispersion (see Greene, 1994 ; Hall, 2000 ; see also Chin & Quddus, 2003 , for a discussion comparing count models).”
Robust Estimation for Zero‐Inflated Poisson Regression (2009, methodology): “In addition, we studied the existing approach, MHD estimation, in this context, and compared these two approaches with ML. Our simulation results suggest that the RES approach provides substantial protection against outliers relative to ML and easily outperforms MHD estimation. In addition, the MHD ”
On the Use of Zero-Inflated and Hurdle Models for Modeling Vaccine Adverse Event Count Data (2006, background): “Hall ( 2000 ), Yau and Lee ( 2001 ), and Berk and Lachenbruch ( 2002 ) included cluster-specific random effects into their zero-inflated models.”
Zero-Inflated Generalized Poisson Regression Model with an Application to Domestic Violence Data (2021, methodology): “…zeros than expected: Lambert (1992) described the zero-inflated Poisson (ZIP) regression models with an application to defects in manufacturing; Hall (2000) described the zero-inflated binomial (ZIB) regression model and incorporated random effects into ZIP and ZIB models; and Lee et al.…”
Marginal models for zero inflated clustered data (2004, methodology): “These data set were used by Hall (2000) to illustrate the ZIP and ZIB models with random effects to account for clustering in the data (van Iersel et al.”
Random effect models for repeated measures of zero-inflated count data (2005, background): “Hall (2000) extended the Lambert (1992) zero-inflated Poisson (ZIP) model to handle longitudinal data, adding a random effect to account for the within-subject dependence in the Poisson state.”