On dual model-free variable selection with two groups of variables

Ahmad Alothman, Yuexiao Dong and Andreas Artemiou

Journal of Multivariate Analysis, 2018, vol. 167, issue C, 366-377

Abstract: In the presence of two groups of variables, existing model-free variable selection methods only reduce the dimensionality of the predictors. We extend the popular marginal coordinate hypotheses Cook (2004) in the sufficient dimension reduction literature and consider the dual marginal coordinate hypotheses, where the role of the predictor and the response is not important. Motivated by canonical correlation analysis (CCA), we propose a CCA-based test for the dual marginal coordinate hypotheses, and devise a joint backward selection algorithm for dual model-free variable selection. The performances of the proposed test and the variable selection procedure are evaluated through synthetic examples and a real data analysis.

Keywords: Canonical correlation analysis; Dual marginal coordinate hypotheses; Sliced inverse regression; Trace test (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:167:y:2018:i:c:p:366-377

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DOI: 10.1016/j.jmva.2018.06.003

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