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Activity Number:
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159
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 3, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #304870 |
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Title:
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Locally Weighted Censored Quantile Regression
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Author(s):
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Lan Wang*+ and Huixia (Judy) Wang
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Companies:
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The University of Minnesota and North Carolina State University
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Address:
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385 Ford Hall, School of Statistics, Minneapolis, MN, 55455,
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Keywords:
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Kaplan-Meier estimator ; Kernel ; Quantile regression ; Random censoring ; Semiparametric ; Survival analysis
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Abstract:
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Censored quantile regression offers a valuable supplement to Cox proportional hazards model. Existing work often requires stringent assumptions, such as unconditional independence of the survival time and the censoring variable or global linearity at all quantile levels. To overcome these drawbacks, we propose a novel locally weighted censored quantile regression approach. The new approach adopts the redistribution-of-mass idea and employs a local reweighting scheme. Its validity only requires conditional independence of the survival time and the censoring variable given the covariates, and linearity at the particular quantile level of interest. Our method leads to a simple algorithm. Applying recent theory of M-estimation with infinite dimensional parameters, we rigorously establish the consistency and asymptotic normality of the proposed estimator.
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