Sure explained variability and independence screening
文献类型：期刊论文
| 作者 | Chen, Min1,2 ; Lian, Yimin1; Chen, Zhao3; Zhang, Zhengjun4
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| 刊名 | JOURNAL OF NONPARAMETRIC STATISTICS
 |
| 出版日期 | 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 |
| WOS记录号 | WOS:000415659400008 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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