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Activity Number: 404 - Recent Research in High-Dimensional and Complex Data Analysis
Type: Topic-Contributed
Date/Time: Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
Sponsor: International Chinese Statistical Association
Abstract #317542
Title: Sufficient Variable Screening for Ultrahigh Dimensional Survival Data
Author(s): Chenlu Ke*
Companies: Virginia Commonwealth University
Keywords: Semi-competing Risks Data; Sufficient Variable Screening; Ultrahigh Dimensionality

We propose a novel framework of sufficient variable screening for ultrahigh dimensional semi-competing risks data based on a class of powerful independence measures proposed recently. Compared with existing sure independence screening methods for survival data, which only consider marginal relationship between the survival estimates and each predictor, our approach further incorporates joint information among predictors to refine the screening. Furthermore, our procedure takes advantage of the independence measures and avoid estimating the survival function. We establish the sure screening property of the proposed approach in the regime of sufficient variable selection. A partial sufficient variable screening procedure conditioning on a known set of important predictors is also introduced. Numerical studies across a variety of regression and classification settings demonstrate the superiority of our method.

Authors who are presenting talks have a * after their name.

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