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

JOURNAL OF MULTIVARIATE ANALYSIS

卷:167

Article

Alothman, Ahmad¹  Dong, Yuexiao¹  Artemiou, Andreas² 

[1] Temple Univ, Dept Stat Sci, Philadelphia, PA 19122 USA

[2] Cardiff Univ, Sch Math, Cardiff, S Glam, Wales

关键词: Canonical correlation analysis;    Dual marginal coordinate hypotheses;    Sliced inverse regression;    Trace test;   

DOI  :  10.1016/j.jmva.2018.06.003

来源: Elsevier

【 摘 要 】

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. (C) 2018 Elsevier Inc. All rights reserved.

【 授权许可】

Free