首页|Sure Independence Screening via Semiparameteric Copula Learning
Sure Independence Screening via Semiparameteric Copula Learning
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
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.
Ultrahigh dimensionalityConditional mean dependenceCopula learningSemiparametric method
XIN Xin、XIE Bo-yi、LIU Ke-ke
展开 >
School of Mathematics and Statistics,Henan University,Kaifeng 475004,China
万方数据
