Global Bahadur Representation For Nonparametric Censored Regression Quantiles And Its Applications
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.
(This abstract was borrowed from another version of this item.)
Volume (Year): 29 (2013)
Issue (Month): 05 (October)
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