Global Bahadur Representation for Nonparametric Censored Regression Quantiles and its Applications
30 Pages Posted: 4 Nov 2011
Date Written: November 4, 2011
Abstract
This paper is concerned with the nonparametric estimation of regression quantiles where the response variable is randomly censored. Using results on the strong uniform convergence of U-processes, we derive a global Bahadur representation for the weighted local polynomial estimators, which is sufficiently accurate for many further theoretical analyses including inference. We consider two applications in detail: estimation of the average derivative, and estimation of the component functions in additive quantile regression models.
Keywords: Bahadur representation, Censored data, Kernel smoothing, Quantile regression, Semiparametric models
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