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Ensembling Variable Selectors by Stability Selection for the Cox Model
Joint Authors
Zhang, Chun-Xia
Yin, Qing-Yan
Li, Jun-Li
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-11-15
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
As a pivotal tool to build interpretive models, variable selection plays an increasingly important role in high-dimensional data analysis.
In recent years, variable selection ensembles (VSEs) have gained much interest due to their many advantages.
Stability selection (Meinshausen and Bühlmann, 2010), a VSE technique based on subsampling in combination with a base algorithm like lasso, is an effective method to control false discovery rate (FDR) and to improve selection accuracy in linear regression models.
By adopting lasso as a base learner, we attempt to extend stability selection to handle variable selection problems in a Cox model.
According to our experience, it is crucial to set the regularization region Λ in lasso and the parameter λmin properly so that stability selection can work well.
To the best of our knowledge, however, there is no literature addressing this problem in an explicit way.
Therefore, we first provide a detailed procedure to specify Λ and λmin.
Then, some simulated and real-world data with various censoring rates are used to examine how well stability selection performs.
It is also compared with several other variable selection approaches.
Experimental results demonstrate that it achieves better or competitive performance in comparison with several other popular techniques.
American Psychological Association (APA)
Yin, Qing-Yan& Li, Jun-Li& Zhang, Chun-Xia. 2017. Ensembling Variable Selectors by Stability Selection for the Cox Model. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1140859
Modern Language Association (MLA)
Yin, Qing-Yan…[et al.]. Ensembling Variable Selectors by Stability Selection for the Cox Model. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1140859
American Medical Association (AMA)
Yin, Qing-Yan& Li, Jun-Li& Zhang, Chun-Xia. Ensembling Variable Selectors by Stability Selection for the Cox Model. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1140859
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1140859