Sufficient dimension reduction via distance covariance with multivariate responses

Xianyan Chen, Qingcong Yuan and Xiangrong Yin

Journal of Nonparametric Statistics, 2019, vol. 31, issue 2, 268-288

Abstract: In this article, we propose a new method for sufficient dimension reduction when both response and predictor are vectors. The new method, using distance covariance, keeps the model-free advantage, and can fully recover the central subspace even when many predictors are discrete. We then extend this method to the dual central subspace, including a special case of canonical correlation analysis. We illustrated estimators through extensive simulations and real datasets, and compared to some existing methods, showing that our estimators are competitive and robust.

Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1080/10485252.2018.1562065

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