Smoothed L-estimation of regression function

Julien Tamine, Pavel Cizek and Wolfgang Härdle

No 2002,88, SFB 373 Discussion Papers from Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes

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.

Keywords: nonparametric regression; L-estimation; smoothed cumulative distribution function (search for similar items in EconPapers)
Date: 2002
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/65287/1/727091808.pdf (application/pdf)

Related works:
Journal Article: Smoothed L-estimation of regression function (2008)
Working Paper: Smoothed L-estimation of Regression Function (2006)
Working Paper: Smoothed L-estimation of Regression Function (2006)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb373:200288

Access Statistics for this paper

More papers in SFB 373 Discussion Papers from Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().