Open Access
2022 On sufficient variable screening using log odds ratio filter
Baoying Yang, Wenbo Wu, Xiangrong Yin
Author Affiliations +
Electron. J. Statist. 16(1): 498-526 (2022). DOI: 10.1214/21-EJS1951


For ultrahigh-dimensional data, variable screening is an important step to reduce the scale of the problem, hence, to improve the estimation accuracy and efficiency. In this paper, we propose a new dependence measure which is called the log odds ratio statistic to be used under the sufficient variable screening framework. The sufficient variable screening approach ensures the sufficiency of the selected input features in modeling the regression function and is an enhancement of existing marginal screening methods. In addition, we propose an ensemble variable screening approach to combine the proposed fused log odds ratio filter with the fused Kolmogorov filter to achieve supreme performance by taking advantages of both filters. We establish the sure screening properties of the fused log odds ratio filter for both marginal variable screening and sufficient variable screening. Extensive simulations and a real data analysis are provided to demonstrate the usefulness of the proposed log odds ratio filter and the sufficient variable screening procedure.


The authors thank the editor, associate editor, and referees for their thoughtful and insightful comments and suggestions.


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Baoying Yang. Wenbo Wu. Xiangrong Yin. "On sufficient variable screening using log odds ratio filter." Electron. J. Statist. 16 (1) 498 - 526, 2022.


Received: 1 September 2021; Published: 2022
First available in Project Euclid: 10 January 2022

MathSciNet: MR4361748
zbMATH: 1493.62181
Digital Object Identifier: 10.1214/21-EJS1951

Primary: 62G05 , 62J02
Secondary: 62B99

Keywords: Dependence measures , feature screening , sufficient variable screening

Vol.16 • No. 1 • 2022
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