Comparing Some Negative Binomial Regression with Simulation

This paper presented a comprehensive comparative study of various negative binomial regression models using simulation techniques. Negative binomial regression models are widely employed in statistical analyses, particularly in situations involving count data with over dispersion. Zero amplified binomial distribution regressions are considered as important regressions because they have many applications for many experiments. The research aims to compare the two estimation methods (moments Estimation method, Maximum likelihood Estimation method and shrinkage estimation method) for zero inflation for a binomial distribution through a number of experiments with deference (random sample size, value of the distribution parameters and the estimation method). Our findings aim to provide practitioners and researchers with valuable insights into the strengths and limitations of different negative binomial regression models. By understanding the relative performance of these models through simulation studies