ArticleStatistics in medicine2020
Optimized variable selection via repeated data splitting.
Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Automated Cerebral Edema Detection using Electroencephalography in Post-cardiac Arrest Patients.Neurocritical care · 2026Article
- A Nomogram for Predicting Tenofovir-Associated Osteoporosis in Chronic Hepatitis B.Journal of clinical medicine · 2026Article
- Two-stage subsampling variable selection for sparse high-dimensional generalized linear models.Statistical methods in medical research · 2025Article
- Development and validation of the Self-Awareness of Ego-Threatening Biases Questionnaire (SAETBQ).PloS one · 2025Article
- Subsampling based variable selection for generalized linear models.Computational statistics & data analysis · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Model selection in high-dimensional settings has received substantial attention in recent years, however, similar advancements in the low-dimensional setting have been lacking. In this article, we introduce a new variable selection procedure for low to moderate scale regressions (n>p). This method repeatedly splits the data into two sets, one for estimation and one for validation, to obtain an empirically optimized threshold which is then used to screen for variables to include in the final model. In an extensive simulation study, we show that the proposed variable selection technique enjoys superior performance compared with candidate methods (backward elimination via repeated data splitting, univariate screening at 0.05 level, adaptive LASSO, SCAD), being amongst those with the lowest inclusion of noisy predictors while having the highest power to detect the correct model and being unaffected by correlations among the predictors. We illustrate the methods by applying them to a cohort of patients undergoing hepatectomy at our institution.
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Registered trials
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