Variables Selection in Inverse Gaussian Regression Model using Modified Heuristic Search Algorithm

The inverse Gaussian regression model is one of the most widely recognized models, frequently used across various applications. It is part of the generalized linear model families and serves as a foundational model. Like other regression models, it may include numerous independent variables, which can negatively impact both the model’s accuracy and the simplicity of interpreting its results. This study aims to apply the modified invasive weed optimization algorithm and compare it with other methods for variable selection in the inverse Gaussian regression model, using both simulations and real-world data. The Monte Carlo simulation approach was employed, setting sample sizes n to four different values—30, 50, 100, and 150—to facilitate comparisons across sample sizes (small, medium, large). Results indicated that the proposed method reduces the mean square error of the model and outperforms previously used techniques. The AIC method emerged as the least effective in variable selection, as it yielded the highest prediction error (PE) and tended to select irrelevant explanatory variables.