A comparative study of different variable selection methods based on numerical simulation and empirical analysis

This study employs the principles of computer science and statistics to evaluate the efficacy of the linear random effect model, utilizing Lasso variable selection techniques (including Lasso, Elastic-Net, Adaptive-Lasso, and SCAD) through numerical simulation and empirical research. The analysis focuses on the model's consistency in variable selection, prediction accuracy, stability, and efficiency. This study employs a novel approach to assess the consistency of variable selection across models. Specifically, the angle between the actual coefficient vector & beta; and the estimated coefficient vector & beta; is computed to determine the degree of consistency. Additionally, the boxplot tool of statistical analysis is utilized to visually represent the distribution of model prediction accuracy data and variable selection consistency. The comparative stability of each model is assessed based on the frequency of outliers. This study conducts comparative experiments of numerical simulation to evaluate a proposed model evaluation method against commonly used analysis methods. The results demonstrate the effectiveness and correctness of the proposed method, highlighting its ability to conveniently analyze the stability and efficiency of each fitting model.

Automated summary

This study uses computer science and statistical principles to evaluate the effectiveness of the linear random effect model, employing Lasso variable selection techniques. It assesses the model's consistency, prediction accuracy, stability, and efficiency through numerical simulation and empirical research. A novel approach is used to assess variable selection consistency, employing the angle between the actual coefficient vector β and the estimated coefficient vector β. The study also utilizes statistical tools, such as the boxplot, to visually represent prediction accuracy and variable selection consistency. The proposed method is compared to commonly used analysis methods, demonstrating its effectiveness and convenience in analyzing model stability and efficiency.