RICR: ridge-guided independent component regression for handling high-dimensional multicollinear data

The digital age has brought an explosion of data through technology adoption and smart devices. Our research tackles the specific challenge of multicollinearity in analysis of massive datasets. This issue can make statistical models unstable, where small data changes lead to dramatic shifts in results. To address this, we have developed a new statistical method called the Ridge-Guided Independent Component Regression (RICR) and propose an estimator based upon RICR, i.e. Independent Component Ridge Regression Estimator (ICRRE). This method combines two existing approaches – Ordinary Ridge Regression (ORR) and Independent Component Regression (ICR) – to better handle both multicollinearity and high dimensions of data. We mathematically prove that RIC-based estimator outperforms both the ICR- and ORR-based estimators by establishing the conditions under which it produces superior results. To validate our approach, we conducted extensive simulations comparing these methods. We also tested ICRRE's real-world performance using cotton yield data.