Global Bahadur Representation For NonParametric Censored Regression Quantiles and its Applications

Kong, Efang and Linton, Oliver and 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:https://doi.org/10.1017/S0266466612000813) (Full text available)

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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
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: 18 Jan 2017 07:06 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/37196 (The current URI for this page, for reference purposes)
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