Variable selection in Inverse Gaussian regression model using modified crow search algorithm
Rafal Adeeb Othman · IRAQI JOURNAL OF STATISTICAL SCIENCES · 2023
The inverse Gaussian regression model is one of the important models, which is widely used in many applications. The inverse Gaussian model is placed in tables of families of generalized linear models as it is one of the basic models. Like other regression models, the model may contain many independent variables, which negatively affects the accuracy of the model and its simplicity in interpreting the results. This study aims to use the modified crow search algorithm and compare it with other methods in selecting the variables in the inverse Gaussian regression model using simulation and real data. The results showed that the proposed method contributes to reducing the average square error of the model and achieves better performance compared to other previously used methods.