2020/06/13 by Chunya Li, Li, Chunya, Daijun Chen +3
Computer Science · Decision Sciences · #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Methodology (stat.ME) #Optimal Experimental Design Methods #Probabilistic and Robust Engineering Design
paper · pdf · doi:10.48550/arxiv.2006.07640
openalex publication_date 2020/06/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper we propose a linear variable screening method for computer experiments when the number of input variables is larger than the number of runs. This method uses a linear model to model the nonlinear data, and screens the important variables by existing screening methods for linear models. When the underlying simulator is nearly sparse, we prove that the linear screening method is asymptotically valid under mild conditions. To improve the screening accuracy, we also provide a two-stage procedure that uses different basis functions in the linear model. The proposed methods are very simple and easy to implement. Numerical results indicate that our methods outperform existing model-free screening methods.