Linear screening for high-dimensional computer experiments
Li, Chunya1,2; Chen, Daijun3; Xiong, Shifeng2
Source PublicationSTAT
AbstractIn 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 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 for some extreme cases, 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.
Keywordbest linear approximation best subset regression nonlinear model sure independence screening
Indexed BySCI
Funding ProjectNational Natural Science Foundation of China[11671386] ; National Natural Science Foundation of China[11871033]
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000735456200025
Citation statistics
Document Type期刊论文
Corresponding AuthorXiong, Shifeng
Affiliation1.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, KLSC, NCMIS, Zhongguancun East Rd 55, Beijing 100190, Peoples R China
3.Nuance Commun Inc, Chengdu 610094, Peoples R China
Recommended Citation
GB/T 7714
Li, Chunya,Chen, Daijun,Xiong, Shifeng. Linear screening for high-dimensional computer experiments[J]. STAT,2021,10(1):9.
APA Li, Chunya,Chen, Daijun,&Xiong, Shifeng.(2021).Linear screening for high-dimensional computer experiments.STAT,10(1),9.
MLA Li, Chunya,et al."Linear screening for high-dimensional computer experiments".STAT 10.1(2021):9.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Li, Chunya]'s Articles
[Chen, Daijun]'s Articles
[Xiong, Shifeng]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li, Chunya]'s Articles
[Chen, Daijun]'s Articles
[Xiong, Shifeng]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li, Chunya]'s Articles
[Chen, Daijun]'s Articles
[Xiong, Shifeng]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.