On the Use of Zero-Inflated and Hurdle Models for Modeling Vaccine Adverse Event Count Data

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A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use (2010, background): “The choice of model should be informed by one’s conception of the underlying data-generating mechanism as well as by model fit (Rose et al., 2006).”
Models for count data with endogenous participation (2009, background): “6 See, e.g., Mullahy (1986), Rose et al. (2006 ) a ndYen and Adamowicz (1994) on separately modeling”
The consequences of checking for zero‐inflation and overdispersion in the analysis of count data (2019, background): “(Future work should consider whether hurdle models (Rose et al., 2006) are similarly problematic.)”
What Drives the Swing Voter in Africa (2013, background): “…measures are overdispersed (the variance is greater than the mean), and the negative binomial count model can accommodate nonnormal, heteroskedastic distributions by including a dispersion parameter (Cameron and Trivedi 1998; Elhai, Calhoun, and Ford 2008; Long 1997, 236; Rose et al. 2006, 464).”