A robust elastic net via bootstrap method under sampling uncertainty for significance analysis of high-dimensional design problems

作者：

Highlights：

• Sampling uncertainty of the elastic net is addressed.

• RENBOOT is proposed to reduce the sampling uncertainty.

• Statistical criterion and significance measure are used to analyze significance.

• Accuracies of RENBOOT are significantly improved compared with those of elastic net.

• Significance of input variables for the body-in-white of a vehicle is analyzed.

摘要

•Sampling uncertainty of the elastic net is addressed.•RENBOOT is proposed to reduce the sampling uncertainty.•Statistical criterion and significance measure are used to analyze significance.•Accuracies of RENBOOT are significantly improved compared with those of elastic net.•Significance of input variables for the body-in-white of a vehicle is analyzed.

论文关键词：Significance analysis,Elastic net,Sampling uncertainty,Bootstrap method,Statistical criterion,Significance measure

论文评审过程：Received 11 January 2021, Revised 19 March 2021, Accepted 30 April 2021, Available online 3 May 2021, Version of Record 6 May 2021.

论文官网地址：https://doi.org/10.1016/j.knosys.2021.107117