Abstract Under-five mortality is defined as the likelihood of a child born alive to die between birth and fifth birthday. Mortality under age five has been most targets public health policies may be common indicator levels. Thus, this study aimed assess under-five modeling Bayesian zero-inflated regression model determinants mortality. A community-based cross-sectional was conducted using 2016 ...

Many applications involve count data from a process that yields an excess number of zeros. Zero-inflated count models, in particular, zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models, along with Poisson hurdle models, are commonly used to address this problem. However, these models struggle to explain extreme incidence of zeros (say more than 80%), especially to fin...

Real-life count data are frequently characterized by overdispersion and excess zeros. Zero-inflated count models provide a parsimonious yet powerful way to model this type of situation. Such models assume that the data are a mixture of two separate data generation processes: one generates only zeros, and the other is either a Poisson or a negative binomial data-generating process. The result of...

This study describes four statistical models—Poisson; Negative Binomial; Zero-Inflated Poisson; and Zero-Inflated Negative Binomial—which were devised in order to examine traffic accidents and estimate the best probability estimating model in terms of future risk assessment at interurban road sections. The study was conducted on four sets of fixed-length sections of the road network: 500, 750, ...

In this paper we develop a Bayesian analysis for the zero-inflated regression models based on the COM-Poisson distribution. Our approach is based on Markov chain Monte Carlo methods. We discuss model selection, as well as, develop case deletion influence diagnostics for the joint posterior distribution based on the ψ-divergence, which has several divergence measures as particular cases, such as...