2024/01/09 by Ming-Chung Chang
Computer Science · Engineering · #Control Systems and Identification #Fault Detection and Control Systems #Gaussian Processes and Bayesian Inference
paper · doi:10.1080/10618600.2024.2304075
openalex publication_date 2024/01/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/17
Predictive analytics involves the use of statistical models to make predictions; however, the power of these techniques is hindered by ever-increasing quantities of data. The richness and sheer volume of big data can have a profound effect on computation time and/or numerical stability. In the current study, we develop a novel approach to subsampling with the aim of overcoming this issue when dealing with regression problems in a supervised learning framework. The proposed method integrates stratified sampling and is model-independent. We assess the theoretical underpinnings of the proposed subsampling scheme, and demonstrate its efficacy in yielding reliable predictions with desirable robustness when applied to different statistical models. Supplementary materials for this article are available online.