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Sequential and Simultaneous Distance-based Dimension Reduction (1903.00037v3)

Published 28 Feb 2019 in stat.ME

Abstract: This paper introduces a method called Sequential and Simultaneous Distance-based Dimension Reduction ($S2D2R$) that performs simultaneous dimension reduction for a pair of random vectors based on Distance Covariance (dCov). Compared with Sufficient Dimension Reduction (SDR) and Canonical Correlation Analysis (CCA)-based approaches, $S2D2R$ is a model-free approach that does not impose dimensional or distributional restrictions on variables and is more sensitive to nonlinear relationships. Theoretically, we establish a non-asymptotic error bound to guarantee the performance of $S2D2R$. Numerically, $S2D2R$ performs comparable to or better than other state-of-the-art algorithms and is computationally faster. All codes of our $S2D2R$ method can be found on Github, including an R package named S2D2R.

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