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Activity Number: 48 - New Frontiers in High-Dimensional and Complex Data analyses
Type: Invited
Date/Time: Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract #300178
Title: Dimension Reduction for High-Dimensional Censored Data
Author(s): Shanshan Ding and Wei Qian and Lan Wang*
Companies: University of Delaware and University of Delaware and University of Minnesota
Keywords: sufficient dimension reduction; variable selection; censored data; high dimension

We propose a unified framework and an efficient algorithm for analyzing high-dimensional survival data under weak modeling assumptions. In particular, it imposes neither parametric distributional assumption nor linear regression assumption. It only assumes that the survival time $T$ depends on a high-dimensional covariate vector $\xxx$ through low-dimensional linear combinations of covariates $\Gamma^T\xxx$. The censoring time is allowed to be conditionally independent of the survival time given the covariates.

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

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