Commonly used parametric models may lead to erroneous inference when analyzing count or continuous data with excess of zeroes. For non-clustered data, the most commonly used models to address the issue of excess zeroes are zero inflated Poisson (ZIP), zero inflated negative binomial (ZINB), hurdle Poisson (HP) and hurdle negative binomial (HNB). Our goal is to expand these for modeling longitud...

In recent years, there has been an abundance of research that has used Poisson models and its variants (negative binomial and zero-inflated models) to improve our understanding of the factors that affect accident frequencies on roadway segments. This study explores the application of an alternate method, tobit regression, by viewing vehicle accident rates directly (instead of frequencies) as a ...

Integer-valued time series are often modeled with Markov models or hidden Markov models (HMM). However, when the series represents count data it is often subject to excess zeros. In this case, usual distributions such as binomial or Poisson are unable to estimate the zero mass correctly. In order to overcome this issue, we introduce zero-inflated distributions in the hidden Markov model. The em...

An analysis of mortality is undertaken in two breeds of pigs: Danish Landrace and Yorkshire. Zero-inflated and standard versions of hierarchical Poisson, binomial, and negative binomial Bayesian models were fitted using Markov chain Monte Carlo (MCMC). The objectives of the study were to investigate whether there is support for genetic variation for mortality and to study the quality of fit and...