Robust Continuum Regression Study of LASSO Selection and WLAD LASSO on High-Dimensional Data Containing Outliers - Daulay, Nurmai Syaroh; Erfiani, Erfiani; Soleh, Agus M (Universitas Muhammadiyah Mataram, 2024)
In research, we often encounter problems of multicollinearity and outliers, which can cause coefficients to become unstable and reduce model performance. Robust Continuum Regression (RCR) overcomes the problem of multicollinearity by reducing the number of independent variables, namely compressing the data into new variables (latent variables) that are independent of each other and whose dimensions are much smaller and applying robust regression techniques so that the complexity of the regression model can be reduced without losing essential information from data and provide more stable parameter estimates. However, it is hampered in the computational aspect if the data has very high dimensions (p>>n). In the initial stage, it is necessary to reduce dimensions by selecting variables. The Least Absolute Shrinkage and Selection Operator (LASSO) can overcome this but is sensitive to the presence of outliers, which can result in errors in selecting significant variables. Therefore, we need a method that is robust to outliers in selecting explanatory variables such as Weighted Least Absolute Deviations with LASSO penalty (WLAD LASSO) in selecting variables by considering the absolute deviation of the residuals. This method aims to overcome the problem of multicollinearity and model instability in high-dimensional data by paying attention to resistance to outliers. Leverages the outlier resistant RCR and variable selection capabilities of LASSO and WLAD LASSO to provide a more reliable and efficient solution for complex data analysis. Measure the performance of RKR-LASSO and RKR-WLAD LASSO; simulations were carried out using low-dimensional data and high-dimensional data with two scenarios, namely without outliers (δ= 0%) and with outliers (δ= 10%, 20%, 30%) with a level of correlation (ρ = 0.1,0.5,0.9). The analysis stage uses RStudio version 4.1.3 software using the