報告人: Xingqiu Zhao趙興球（The Hong Kong Polytechnic University）

報告題目: A New Nonparametric Screening Method for Ultrahigh-dimensional Survival Data

報告摘要: This article focuses on the development of variable screening methods for ultrahigh-dimensional survival data which frequently occur in many scientific fields. Most existing screening procedures are developed for ultrahigh-dimensional complete data and cannot be applicable to censored survival data. To address the new challenges from censoring, we propose a novel model-free screening method through the Kolmogorov-Smirnov

test statistic that is specially tailored to the ultrahigh-dimensional survival data. This new method enjoys the sure screening property under some mild regularity conditions, and its superior performance over existing screening methods is demonstrated by our extensive simulation studies. A real data example of gene expression is used to illustrate the application of the proposed fully nonparametric screening procedure.

報告時間：2016年11月10日（星期四）下午3:30-4:30

報告地點：科技南樓702