Sure Independence Screening via Semiparameteric Copula Learning

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  • School of Mathematics and Statistics, Henan University, Kaifeng 475004, China
XIN Xin (1982-), female, native of Kaifeng, Henan, associate professor of Henan University, engages in statistics; XIE Bo-yi (1998-), female, native of Nanyang, Henan, Ph.D. student of Nanjing University, engages in management and engineering; LIU Ke-Ke (2000-), female, native of Zhumadian, Henan, graduate student of Henan University, engages in statistics.

Received date: 2023-03-06

  Online published: 2024-06-30

Supported by

 Supported by Natural Science Foundation of Henan (Grant No. 202300410066); Program for Science and Technology Development of Henan Province (Grant No. 242102310350).

Abstract

 This paper is concerned with ultrahigh dimensional data analysis, which has become increasingly important in diverse scientific fields. We develop a sure independence screening procedure via the measure of conditional mean dependence based on Copula (CC-SIS, for short). The CC-SIS can be implemented as easily as the sure independence screening procedures which respectively based on the Pearson correlation, conditional mean and distance correlation (SIS, SIRS and DC-SIS, for short) and can significantly improve the performance of feature screening. We establish the sure screening property for the CC-SIS, and conduct simulations to examine its finite sample performance. Numerical comparison indicates that the CC-SIS performs better than the other two methods in various models. At last, we also illustrate the CC-SIS through a real data example.

Cite this article

XIN Xin, XIE Bo-yi, LIU Ke-ke . Sure Independence Screening via Semiparameteric Copula Learning[J]. Chinese Quarterly Journal of Mathematics, 2024 , 39(2) : 144 -160 . DOI: 10.13371/j.cnki.chin.q.j.m.2024.02.003

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