‎Zero-inflated negative binomial model is an appropriate choice to count response variables with excessive zeros and over-dispersion simultaneously. ‎This paper addressed parameter estimation in the zero-inflated when there are many parameters, ‎so that some of them have not influence on variable. ‎We proposed based linear shrinkage, ‎pretest, ‎shrinkage pretest, ‎Stein-type, ‎and positive Stei...

background: the number of failed courses and semesters in students are indicatorsof their performance. these amounts have zero inflated (zi) distributions. using zi poisson and negative binomial distributions we can model these count data to find the associated factors and estimate the parameters. this study aims at to investigate the important factors related to the educational performance of ...

A Poisson model typically is assumed for count data, but when there are so many zeroes in the response variable, because of overdispersion, a negative binomial regression is suggested as a count regression instead of Poisson regression. In this paper, a zero-inflated negative binomial regression model with right truncation count data was developed. In this model, we considered a response variab...

INTRODUCTION Count data are often collected in chronic disease research, and sometimes these data have a skewed distribution. The number of unhealthy days reported in the Behavioral Risk Factor Surveillance System (BRFSS) is an example of such data: most respondents report zero days. Studies have either categorized the Healthy Days measure or used linear regression models. We used alternative r...

Three nonlinear count models, Poisson R.egression (PR), Negative Binomial Regression (NBR), and Generalized Poisson Regression (GPR) are used for assessing the effects of risk factors on agricultural injuries from farm injury data. A sample of 1,322 respondents who participated in the farm safety/injury baseline survey in nine rural counties in Alabama and Mississippi, aged 18 years and older a...