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Global Bahadur representation for nonparametric censored regression quantiles and its applications

Authors: Efang Kong , Oliver Linton and Yingcun Xia
Date: 03 November 2011
Type: cemmap Working Paper, CWP33/11
DOI: 10.1920/wp.cem.2011.3311

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.

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Now published:
Efang Kong, Oliver Linton and Yingcun Xia October 2013, Global Bahadur representation for nonparametric censored regression quantiles and its applications, Journal Article, Cambridge University Press

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