@techreport{Czek2005Robust,
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.},
address = {Berlin},
author = {Pavel \v{C}ί\v{z}ek and Wolfgang Karl H\"{a}rdle},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
keywords = {330; Dimension reduction; Nonparametric regression; M-estimation; Nichtparametrisches Verfahren; Robustes Verfahren; Sch\"{a}tztheorie; Theorie},
language = {eng},
number = {2005,015},
publisher = {SFB 649, Economic Risk},
title = {Robust estimation of dimension reduction space},
type = {SFB 649 discussion paper},
url = {http://hdl.handle.net/10419/25034},
year = {2005}
}
