The gamma regression model is one of the regression models that has found a lot of uses, particularly in a variety of sciences such as engineering, medicine, insurance, and the humanities, among other areas. When the response variable can only take positive restricted to positive real numbers, this model is utilized. The maximum likelihood approach is normally the one that is used whenever the covariates do not have any link with each other. This is because the maximum likelihood method is the most accurate estimation method. However, just like in linear regression models, it is possible that we come across situations in which there is a correlation or linear relationship between the covariates. In such a scenario, the inference that is drawn using this method will be incorrect due to the large estimate that is produced. In this thesis, we investigate how to estimate the parameters of a gamma regression model when there is multicollinearity between the covariates. We begin by presenting the ridge estimator for the gamma regression model. After that, we use the various techniques that have been suggested to estimate the ridge parameter in other regression models in the gamma regression model. Finally, we use Monte Carlo simulation to determine which ridge estimator provides the most accurate results. After that, we discuss the gamma regression model, and then apply the selected estimator to a practical scenario.
