Counting data is one of the most widely used data types because it is found in various sciences such as medicine, pharmacy, management, industry, and economics. Generally, a count regression model such as Poisson regression is used to analyze this type of data, but when the count data contains extra zeros, the results will not be efficient. Since extra zeros in the data cause over-dispersion, zero-inflated Poisson (ZIP) regression models are used for the Poisson distribution. Hurdle regression (HR) models are another type of modified count regression, HR is an effective model for dealing with zero-inflated data, which in combination with the generalized Poisson distribution can deal with over-dispersion or under-dispersion in addition to the problem of extra zeros. In this dissertation, we will study and analyze the generalized Hurdle Poisson regression model (GPHR) which has two properties of extra zero values and non-equality variance and mean. We will use different approaches to estimate the parameters of the discussed model. We will simulate the proposed methods by coding in R and compare the results using the evaluation criteria. Finally, we will apply the selected method to real data.
