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