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Activity Number: 674
Type: Invited
Date/Time: Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract #318214
Title: Variable Selection for Quantile Regression Under General Censoring Scheme
Author(s): Lan Wang*
Companies: University of Minnesota
Keywords: quantile regression ; censoring ; variable selection

Censored quantile regression offers a valuable supplement to Cox proportional hazards model for survival analysis. Existing work in the literature on variable selection for censored quantile regression usually work with only a small number of covariates and requires stringent assumptions, such as unconditional independence of the survival time and or the independence of the survival time and the random errors. We provide a new penalized censored quantile regression framework under general censoring scheme that overcomes the aforementioned drawback. Theoretical and numerical results will be reported.

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

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