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DOI: 10.1080/10485252.2024.2313137

Robust Sufficient Dimension Reduction via $\alpha$-Distance Covariance

Hsin-Hsiung HuangFeng YuTeng Zhang
Feb 2024
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摘要原文
We introduce a novel sufficient dimension-reduction (SDR) method which is robust against outliers using $\alpha$-distance covariance (dCov) in dimension-reduction problems. Under very mild conditions on the predictors, the central subspace is effectively estimated and model-free advantage without estimating link function based on the projection on the Stiefel manifold. We establish the convergence property of the proposed 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.
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