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GLOBAL BAHADUR REPRESENTATION FOR NONPARAMETRIC CENSORED REGRESSION QUANTILES AND ITS APPLICATIONS

Published online by Cambridge University Press:  25 February 2013

Efang Kong ,
Oliver Linton  and
Yingcun Xia
Efang Kong*
Affiliation:
University of Kent at Canterbury
Oliver Linton
Affiliation:
University of Cambridge
Yingcun Xia
Affiliation:
Nanjing University and National University of Singapore
*
*Address correspondence to Efang Kong, School of Mathematics, Statistics and Actuarial Science, University of Kent at Canterbury, UK; e-mail: [email protected].
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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.

Type
ARTICLES
Information
Econometric Theory , Volume 29 , Issue 5 , October 2013 , pp. 941 - 968
Copyright
Copyright © Cambridge University Press 2013 

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Footnotes

The authors thank Professor Arthur Lewbel (co-editor) and three referees for helpful comments. Xia’s work is partially supported by a grant of NUS R-155-000-121-112.

References

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