Robust estimation of dimension reduction space
AbstractMost 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 su±ciently 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.
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Bibliographic InfoPaper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2005-015.
Length: 18 pages
Date of creation: Mar 2005
Date of revision:
Dimension reduction; Nonparametric regression; M-estimation;
Other versions of this item:
- Cizek, P. & Hardle, W., 2006. "Robust estimation of dimension reduction space," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 51(2), pages 545-555, November.
- Cizek, P. & HÃ¤rdle, W.K., 2005. "Robust Estimation of Dimension Reduction Space," Discussion Paper, Tilburg University, Center for Economic Research 2005-31, Tilburg University, Center for Economic Research.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-12-01 (All new papers)
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