Global Bahadur Representation For NonParametric Censored Regression Quantiles and its Applications

Kong, Efang, Linton, Oliver, Xia, Yingcun (2013) Global Bahadur Representation For NonParametric Censored Regression Quantiles and its Applications. Econometric Theory, 29 (5). pp. 941-968. ISSN 0266-4666. (doi:10.1017/S0266466612000813)

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http://dx.doi.org/10.1017/S0266466612000813

Abstract

This paper is concerned with the nonparametric estimation of regression quantiles of a response variable that is randomly censored. Using results on the strong uniform convergence rate of U-processes, we derive a global Bahadur representation for a class of locally weighted polynomial estimators, which is sufficiently accurate for many further theoretical analyses including inference. Implications of our results are demonstrated through the study of the asymptotic properties of the average derivative estimator of the average gradient vector and the estimator of the component functions in censored additive quantile regression models.

Item Type: Article
DOI/Identification number: 10.1017/S0266466612000813
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Efang Kong
Date Deposited: 04 Dec 2013 16:42 UTC
Last Modified: 29 May 2019 11:34 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/37196 (The current URI for this page, for reference purposes)
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