Working Paper

Global Bahadur representation for nonparametric censored regression quantiles and its applications


Efang Kong, Oliver Linton, Yingcun Xia

Published Date

3 November 2011


Working Paper (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.