Robust estimation of dimension reduction space

Pavel Čίžek and Wolfgang Härdle

No 2005-015, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

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.

Keywords: Dimension reduction; Nonparametric regression; M-estimation (search for similar items in EconPapers)
Date: 2005
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Related works:
Working Paper: Robust Estimation of Dimension Reduction Space (2005)
Working Paper: Robust Estimation of Dimension Reduction Space (2005)
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