Distribution-free and model-free multivariate feature screening via multivariate rank distance correlation

Shaofei Zhao and Guifang Fu

Journal of Multivariate Analysis, 2022, vol. 192, issue C

Abstract: Feature screening approaches are effective in selecting active features from data with ultrahigh dimensionality and increasing complexity; however, many existing feature screening approaches are either restricted to a univariate response or rely on some distribution or model assumptions. In this article, we propose a sure independence screening approach based on the multivariate rank distance correlation (MrDc-SIS). The MrDc-SIS achieves multiple desirable properties such as being distribution-free, completely nonparametric, scale-free and robust for outliers or heavy tails. Moreover, the MrDc-SIS can be used to screen either univariate or multivariate responses and either one dimensional or multi-dimensional predictors. We establish the theoretical sure screening and rank consistency properties of the MrDc-SIS approach under a mild condition by lifting previous assumptions about the finite moments. Simulation studies demonstrate that MrDc-SIS outperforms eight other closely relevant approaches under certain settings. We also apply the MrDc-SIS approach to a multi-omics ovarian carcinoma data downloaded from The Cancer Genome Atlas (TCGA).

Keywords: Distance correlation; Feature screening; Multivariate rank; Sure screening property; Ultrahigh dimensional data analysis (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.jmva.2022.105081

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