Constructing Regression Model Using Penalty Methods to Process High-Dimensional Data for a Factorial Experiment - Mahmood M Taher (Journal of Al-Qadisiyah for Computer Science and Mathematics, 2026)
Research addressed high-dimensional data in a 2³ factorial experiment model using a completely randomized design. The columns of matrix (X) represented the effects of factor levels and their interactions, along with the overall arithmetic mean, while the rows represented the number of experimental observations. The high-dimensional data problem has been addressed by using several methods, comparing them according to various criteria. Penalty methods (Bridge, LASSO, ALASSO, SCAD) were employed to select significant factors in the model. Through simulation and application of statistical criteria, the performance of these methods was compared, with results Lasso and Adaptive Lasso show poor performance in most cases, with high MSE and MAD values. CAD generally falls in between, offering better performance compared to Lasso and Adaptive Lasso, but not as good as Bridge.