Global Bahadur representation for nonparametric censored regression quantiles and its applications

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: cemmap working paper ; No. CWP33/11

Classification and Topics

• Classification: Wirtschaft
• Subject: Bahadur representation ; Censored data ; Kernel smoothing ; Quantile regression ; Semiparametric models ; Nichtparametrisches Verfahren ; Regression ; Schätztheorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Kong, Efang ; Linton, Oliver ; Xia, Yingcun
• Event: Veröffentlichung
• (who): Centre for Microdata Methods and Practice (cemmap)
• (where): London
• (when): 2011

Further information

• DOI: doi:10.1920/wp.cem.2011.3311
• Handle: http://hdl.handle.net/10419/64649
• Last update: 10.03.2025, 11:41 AM CET