Robust estimation of dimension reduction space

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Robust estimation of dimension reduction space

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 a...

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ژورنال

عنوان ژورنال: Computational Statistics & Data Analysis

سال: 2006

ISSN: 0167-9473

DOI: 10.1016/j.csda.2005.11.001