Abstract Using numerous transaction data on the number of stock trades, we conduct a forecasting exercise with INGARCH models, governed by various conditional distributions; Poisson, linear and quadratic negative binomial, double Poisson generalized Poisson. The model parameters are estimated efficient Markov Chain Monte Carlo methods, while forecast evaluation is done calculating point density...

Statistical regression models are widely used in highway safety for modeling motor vehicle crash data. They include the Poisson model, the Negative Binomial (NB) (Poisson-gamma) model, as well as their modified forms or extensions. The Poisson model works well when the data do not exhibit over-dispersion, while the NB model is often recommended when they do. Recently, a new type of statistical ...

In a variety of research domains, data are generated as a consequence of the count process and may possess an ‘excess’ of zeros. There have been many attempts to analyse such data using different statistical methods, including the zero-inflated Poisson (ZiP) and zero-inflated binomial (ZiB) models. The interpretation of these models is however problematic if the covariates considered for the no...

Recently, count regression models have been used to model over dispersed and zero-inflated response variable that is affected by one or more covariates. Generalized Poisson (GP) negative binomial (NB) suggested deal with over-dispersion. Zero inflated such as the (ZIP), (ZINB) generalized Pois son (ZIGP) handle data many zeros. The aim of this study number C. caretta hatchlings dying from expos...

As wafer sizes increase, the clustering phenomenon of defects increases. Clustered defects cause the conventional Poisson yield model underestimate actual wafer yield, as defects are no longer uniformly distributed over a wafer. Although some yield models, such as negative binomial or compound Poisson models, consider the effects of defect clustering on yield prediction, these models have some ...