KMS Of Academy of mathematics and systems sciences, CAS
| Sure explained variability and independence screening | |
Chen, Min1,2 ; Lian, Yimin1; Chen, Zhao3; Zhang, Zhengjun4
| |
| 2017 | |
| Source Publication | JOURNAL OF NONPARAMETRIC STATISTICS
 |
| ISSN | 1048-5252 |
| Volume | 29Issue:4Pages:849-883 |
| Abstract | 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. |
| Keyword | Feature screening sure screening property generalised measures of correlation nonparametric inference model-free approach |
| DOI | 10.1080/10485252.2017.1375111 |
| Language | 英语 |
| Funding Project | 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 Research Area | Mathematics |
| WOS Subject | Statistics & Probability |
| WOS ID | WOS:000415659400008 |
| Publisher | TAYLOR & FRANCIS LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/26922 |
| Collection | 应用数学研究所 |
| Corresponding Author | Chen, Zhao |
| Affiliation | 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 |
| 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. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment