ArticleComputational intelligence and neuroscience2017
Ensembling Variable Selectors by Stability Selection for the Cox Model.
Article in Computational intelligence and neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Proteomic risk score for early prediction of kidney disease progression in individuals with APOL1 high-risk genotypes.Nature medicine · 2026Article
- Prognostic biomarker discovery in pancreatic cancer through hybrid ensemble feature selection and multi-omics data.BioData mining · 2026Article
- Integrated path stability selection.Journal of the American Statistical Association · 2025Article
- Machine learning-based disulfidptosis-related lncRNA signature predicts prognosis, immune infiltration and drug sensitivity in hepatocellular carcinoma.Scientific reports · 2024Article
- Stability and volatility shape the gut bacteriome and Kazachstania slooffiae dynamics in preweaning, nursery and adult pigs.Scientific reports · 2022Article
- Data-driven machine learning models for decoding speech categorization from evoked brain responses.Journal of neural engineering · 2021Article
- Speech categorization is better described by induced rather than evoked neural activity.The Journal of the Acoustical Society of America · 2021Article
- Decoding Hearing-Related Changes in Older Adults' Spatiotemporal Neural Processing of Speech Using Machine Learning.Frontiers in neuroscience · 2020Article
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Authors and funding
3 authors.
Funding
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Abstract
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
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