EconStor >
Humboldt-Universität zu Berlin >
Sonderforschungsbereich 649: Ökonomisches Risiko, Humboldt-Universität Berlin >
SFB 649 Discussion Papers, HU Berlin >

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10419/25034
  
Title:Robust estimation of dimension reduction space PDF Logo
Authors:Čίžek, Pavel
Härdle, Wolfgang Karl
Issue Date:2005
Series/Report no.:SFB 649 discussion paper 2005,015
Abstract:Most dimension reduction methods based on nonparametric smoothing are highly sensitive to outliers and to data coming from heavy-tailed distributions. We show that the recently proposed methods by Xia et al. (2002) can be made robust in such a way that preserves all advantages of the original approach. Their extension based on the local one-step M-estimators is sufficiently robust to outliers and data from heavy tailed distributions, it is relatively easy to implement, and surprisingly, it performs as well as the original methods when applied to normally distributed data.
Subjects:Dimension reduction
Nonparametric regression
M-estimation
Document Type:Working Paper
Appears in Collections:SFB 649 Discussion Papers, HU Berlin

Files in This Item:
File Description SizeFormat
496003585.PDF329.58 kBAdobe PDF
No. of Downloads: Counter Stats
Download bibliographical data as: BibTeX
Share on:http://hdl.handle.net/10419/25034

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