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Department seminar by Grace Hong, Michigan State UniversityExport this event to calendar

Thursday, May 5, 2016 — 4:00 PM EDT

Conditional Screening for Ultra-high Dimensional Covariates with Survival Outcomes

The emergence of large-scale biomedical survival studies, which typically involve excessive number of biomarkers, has brought high demand in designing ?efficient screening tools for selecting predictive biomarkers. The vast amount of biomarkers defi?es any existing variable selection methods via regularization. The recently developed variable screening methods, though powerful in many practical setting, fail to incorporate prior information on the importance of each biomarker and are less powerful in detecting marginally weak while jointly important signals. We propose a new conditional screening method for survival outcome data by computing the marginal contribution of each biomarker given priorly known biological information. This is based on the premise that some biomarkers are known to be associated with disease outcomes a priori. Our method possesses sure screening properties and a vanishing false selection rate.

This is joint work with Jian Kang and Yi Li. 

Location 
M3 - Mathematics 3
3127

,
Canada
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