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Kong, Efang
Linton, Oliver
Xia, Yingcun
Year of Publication: 
Series/Report no.: 
cemmap working paper CWP33/11
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.
Bahadur representation
Censored data
Kernel smoothing
Quantile regression
Semiparametric models
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Document Type: 
Working Paper

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