Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/65287 
Year of Publication: 
2002
Series/Report no.: 
SFB 373 Discussion Paper No. 2002,88
Publisher: 
Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes, Berlin
Abstract: 
The Nadaraya-Watson estimator of regression is known to be highly sensitive to the presence of outliers in the sample. A possible way of robustication consists in using local L-estimates of regression. Whereas the local L-estimation is traditionally done using an empirical conditional distribution function, we propose to use instead a smoothed conditional distribution function. We show that this smoothed L-estimation approach provides computational as well as statistical finite sample improvements. The asymptotic distribution of the estimator is derived under mild Ø-mixing conditions.
Subjects: 
nonparametric regression
L-estimation
smoothed cumulative distribution function
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
135.04 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.