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Linear screening for high-dimensional computer experiments
Li, Chunya1,2; Chen, Daijun3; Xiong, Shifeng2
2021-12-01
Source PublicationSTAT
ISSN2049-1573
Volume10Issue:1Pages:9
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
DOI10.1002/sta4.320
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[11671386] ; National Natural Science Foundation of China[11871033]
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000735456200025
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/59758
Collection中国科学院数学与系统科学研究院
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.
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