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Sufficient dimension reduction via distance covariance with multivariate responses

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  • Xianyan Chen
  • Qingcong Yuan
  • Xiangrong Yin

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.

Suggested Citation

  • Xianyan Chen & Qingcong Yuan & Xiangrong Yin, 2019. "Sufficient dimension reduction via distance covariance with multivariate responses," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 31(2), pages 268-288, April.
    • Handle: RePEc:taf:gnstxx:v:31:y:2019:i:2:p:268-288
    DOI: 10.1080/10485252.2018.1562065
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    Cited by:

    1. Zhang, Hong-Fan, 2021. "Minimum Average Variance Estimation with group Lasso for the multivariate response Central Mean Subspace," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    2. Zhang, Wei & Gao, Wei & Ng, Hon Keung Tony, 2023. "Multivariate tests of independence based on a new class of measures of independence in Reproducing Kernel Hilbert Space," Journal of Multivariate Analysis, Elsevier, vol. 195(C).

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