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Čίžek, Pavel
Härdle, Wolfgang Karl
Year of Publication: 
Series/Report no.: 
SFB 649 discussion paper 2005,015
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.
Dimension reduction
Nonparametric regression
Document Type: 
Working Paper

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