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 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 51 (2006)
Issue (Month): 2 (November)
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Web page: http://www.elsevier.com/locate/csda
Other versions of this item:
- Cizek, P. & Härdle, W.K., 2005. "Robust Estimation of Dimension Reduction Space," Discussion Paper 2005-31, Tilburg University, Center for Economic Research.
- Pavel Cizek & Wolfgang Härdle, 2005. "Robust estimation of dimension reduction space," SFB 649 Discussion Papers SFB649DP2005-015, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- 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
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"Smoothed L-estimation of Regression Function,"
2006-20, Tilburg University, Center for Economic Research.
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