Sure explained variability and independence screening

doi:10.1080/10485252.2017.1375111

CORC > 数学与系统科学研究院 > 中国科学院数学与系统科学研究院 > 应用数学研究所

	Sure explained variability and independence screening
	Chen, Min1,2 ; Lian, Yimin 1; Chen, Zhao 3; Zhang, Zhengjun 4
刊名	JOURNAL OF NONPARAMETRIC STATISTICS
	2017
卷号	29 期号:4 页码:849-883
关键词	Feature screening sure screening property generalised measures of correlation nonparametric inference model-free approach
ISSN号	1048-5252
DOI	10.1080/10485252.2017.1375111
英文摘要	In 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.
资助项目	National 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研究方向	Mathematics
语种	英语
出版者	TAYLOR & FRANCIS LTD
WOS记录号	WOS:000415659400008
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/26921]
专题	应用数学研究所
通讯作者	Chen, Zhao
作者单位	1.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
推荐引用方式 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.