Count data with excess zeros are common in social science research and can be considered as a special case of mixture structured data. We exploit the flexibility of the Bayesian analytic approach to model the mixture data structure inherent in zero-inflated count data by using the zero-inflated Poisson (ZIP) model. We discuss the importance of modelling excess-zero count data in social sciences...

Recent studies show that soil animal count data are characterized by the presence of excess zeros and overdispersion, which violate the assumptions of standard statistical tests. Despite this, analyses have consisted of mainly non-parametric tests and log-normal least square regression (i.e. ANOVA). Failure to accommodate zero inflation in count data can result in biased estimation of ecologica...

Clinicians need to predict the number of involved nodes in breast cancer patients in order to ascertain severity, prognosis, and design subsequent treatment. The distribution of involved nodes often displays over-dispersion-a larger variability than expected. Until now, the negative binomial model has been used to describe this distribution assuming that over-dispersion is only due to unobserve...

Hall (2000) has described zero-inflated Poisson and binomial regression models that include random effects to account for excess zeros and additional sources of heterogeneity in the data. The authors of the present paper propose a general score test for the null hypothesis that variance components associated with these random effects are zero. For a zero-inflated Poisson model with random inter...

The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of these methods, a new implementation of hurdle and zero-inflated regression models in the functions ...