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Title: Robust sufficient dimension reduction via α -distance covariance
We introduce a novel sufficient dimension-reduction (SDR) method which is robust against outliers using α-distance covariance (dCov)in dimension-reduction problems. Under very mild conditions on the predictors, the central subspace is effectively estimated and model-free without estimating link function based on the projection on the Stiefel manifold. We establish the convergence property of the pro-posed estimation under some regularity conditions. We compare the performance of our method with existing SDR methods by simulation and real data analysis and show that our algorithm improves the computational efficiency and effectiveness.  more » « less
Award ID(s):
2318925 2318926
PAR ID:
10534142
Author(s) / Creator(s):
; ;
Publisher / Repository:
Taylor & Francis
Date Published:
Journal Name:
Journal of Nonparametric Statistics
Edition / Version:
1
Volume:
0
Issue:
0
ISSN:
1048-5252
Page Range / eLocation ID:
1-16
Subject(s) / Keyword(s):
α-distance covariance central subspace sufficient dimension reduction (SDR) manifold learning robust statistics
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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