Simplifying Complexity: Linearization Method for Partial Least Squares Regression

Abstract

This research investigates Romera’s local linearization approach as a variance prediction method in partial least squares (PLS) regression. By addressing limitations in the original PLS regression formula, the local linearization approach aims to improve accuracy and stability in variance predictions. Extensive simulations are conducted to assess the method's performance, demonstrating its superiority over traditional algebraic methods and showcasing its computational advantages, particularly with a large number of predictors. Additionally, the study introduces a novel computational technique utilizing bootstrap parameters, enhancing computational stability and robustness. Overall, the research provides valuable insights into the local linearization approach's effectiveness, guiding researchers and practitioners in selecting more reliable and efficient regression modeling techniques.