Global Bahadur Representation for Nonparametric Censored Regression Quantiles and its Applications

30 Pages Posted: 4 Nov 2011

See all articles by Oliver B. Linton

Oliver B. Linton

University of Cambridge

Efang Kong

affiliation not provided to SSRN

Yingcun Xia

National University of Singapore (NUS)

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

Suggested Citation

Linton, Oliver B. and Kong, Efang and Xia, Yingcun, Global Bahadur Representation for Nonparametric Censored Regression Quantiles and its Applications (November 4, 2011). Available at SSRN: https://ssrn.com/abstract=1954617 or http://dx.doi.org/10.2139/ssrn.1954617

Oliver B. Linton (Contact Author)

University of Cambridge ( email )

Faculty of Economics
Cambridge, CB3 9DD
United Kingdom

Efang Kong

affiliation not provided to SSRN

No Address Available

Yingcun Xia

National University of Singapore (NUS) ( email )

1E Kent Ridge Road
NUHS Tower Block Level 7
Singapore, 119228
Singapore

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
102
Abstract Views
900
Rank
472,796
PlumX Metrics