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