报告人：刘妍岩（武汉大学）

报告题目：Censored Cumulative Residual Independent Screening for Ultrahigh-Dimensional Survival Data

报告摘要：For complete ultrahigh-dimensional data, sure independent screening methods can effectively reduce the dimensionality while ensuring that all the active variables can be retained with high probability. However, limited screening methods have been developed for censored data, which often arise in clinical trials and genetic studies. We propose a censored cumulative residual independent screening method that is specially tailored to the ultrahigh-dimensional survival data. The proposed screening method is model-free, and it tends to rank the active variables over the inactive ones in terms of their association with the survival times and also enjoys the sure independent screening property. Compared with several existing methods, our model-free screening method works well with general survival models, is invariant to the monotone transformation of the responses, and requires substantially weaker moment conditions. Numerical studies demonstrate the usefulness of the censored cumulative residual independent screening method, and the new approach is illustrated with a gene expression data set.

报告人简介：刘妍岩，武汉大学教授，研究方向是半参数统计推断、生物统计、生存分析、缺失数据统计方法、有偏抽样统计方法研究。

报告时间：2018年6月11日（星期一）下午16:00-17:00

报告地点：科技楼南702