|Authors:||Efang Kong, Oliver Linton and Yingcun Xia|
|Date:||03 November 2011|
|Type:||cemmap Working Papers, CWP33/11|
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.