The Continuum Regression Analysis with Preprocessed Variable Selection LASSO and SIR-LASSO - Suruddin, Adzkar Adlu Hasyr; Erfiani, Erfiani; Sumertajaya, I Made (Department of Statistics ITS, 2025)
Analyzing high-dimensional data is a considerable challenge in statistics and data science. Issues like multicollinearity and outliers often arise, leading to unstable coefficients and diminished model effectiveness. Continuum regression is a useful method for calibration models because it effectively handles multicollinearity and reduces the number of dimensions in the data. This method condenses data into autonomous latent variables, resulting in a more stable, precise, and reliable model. It is possible to use the dimensionality reduction method without losing any important information from the original data. This makes it a useful tool for making calibration models work better. In the initial phase, minimizing dimensions via variable selection is crucial. The study aims to build and test the Continuum Regression calibration model using LASSO and SIR-LASSO variable selection preprocessing methods. SIR-LASSO is a method that integrates SIR with the variable selection capabilities of LASSO. This technique aims to handle high-dimensional data by identifying relevant low-dimensional structures. LASSO improves variable selection by applying a penalty to regression coefficients, reducing the impact of less significant or redundant variables. The integration improves SIR's efficacy in assessing high-dimensional data while also enhancing model stability and interpretability. This approach seeks to address the issues of multicollinearity and model instability. We conducted simulations using both low-dimensional and high-dimensional datasets to assess the efficacy of CR LASSO and CR SIR-LASSO. RStudio version 4.1.3 was used for the analysis. The