CSpace  > 应用数学研究所
Sure explained variability and independence screening
Chen, Min1,2; Lian, Yimin1; Chen, Zhao3; Zhang, Zhengjun4
AbstractIn the era of Big Data, extracting the most important exploratory variables available in ultrahigh-dimensional data plays a key role in scientific researches. Existing researches have been mainly focusing on applying the extracted exploratory variables to describe the central tendency of their related response variables. For a response variable, its variability characteristic is as much important as the central tendency in statistical inference. This paper focuses on the variability and proposes a new model-free feature screening approach: sure explained variability and independence screening (SEVIS). The core of SEVIS is to take the advantage of recently proposed asymmetric and nonlinear generalised measures of correlation in the screening. Under some mild conditions, the paper shows that SEVIS not only possesses desired sure screening property and ranking consistency property, but also is a computational convenient variable selection method to deal with ultrahigh-dimensional data sets with more features than observations. The superior performance of SEVIS, compared with existing model-free methods, is illustrated in extensive simulations. A real example in ultrahigh-dimensional variable selection demonstrates that the variables selected by SEVIS better explain not only the response variables, but also the variables selected by other methods.
KeywordFeature screening sure screening property generalised measures of correlation nonparametric inference model-free approach
Funding ProjectNational Natural Science Foundation of China[11371345] ; National Natural Science Foundation of China[11690014] ; National Natural Science Foundation of China[11690015] ; [NSF-DMS-1505367] ; [NSF-CMMI-1536978]
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000415659400008
Citation statistics
Document Type期刊论文
Corresponding AuthorChen, Zhao
Affiliation1.Univ Sci & Technol China, Sch Management, Hefei, Anhui, Peoples R China
2.Chinese Acad Sci, AMSS, Beijing, Peoples R China
3.Penn State Univ, Dept Stat, State Coll, PA 16802 USA
4.Univ Wisconsin, Dept Stat, Madison, WI 53706 USA
Recommended Citation
GB/T 7714
Chen, Min,Lian, Yimin,Chen, Zhao,et al. Sure explained variability and independence screening[J]. JOURNAL OF NONPARAMETRIC STATISTICS,2017,29(4):849-883.
APA Chen, Min,Lian, Yimin,Chen, Zhao,&Zhang, Zhengjun.(2017).Sure explained variability and independence screening.JOURNAL OF NONPARAMETRIC STATISTICS,29(4),849-883.
MLA Chen, Min,et al."Sure explained variability and independence screening".JOURNAL OF NONPARAMETRIC STATISTICS 29.4(2017):849-883.
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
[Chen, Min]'s Articles
[Lian, Yimin]'s Articles
[Chen, Zhao]'s Articles
Baidu academic
Similar articles in Baidu academic
[Chen, Min]'s Articles
[Lian, Yimin]'s Articles
[Chen, Zhao]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Chen, Min]'s Articles
[Lian, Yimin]'s Articles
[Chen, Zhao]'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.