ArticleJournal of the American Statistical Association2025
Integrated path stability selection.
Article in Journal of the American Statistical Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Local graph estimation with pathwise false discovery control.Nature communications · 2026Article
- Nonparametric IPSS: fast, flexible feature selection with false discovery control.Bioinformatics (Oxford, England) · 2025Article
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Authors and funding
2 authors.
Funding
Abstract
Stability selection is a popular method for improving feature selection algorithms. One of its key attributes is that it provides theoretical upper bounds on the expected number of false positives, E(FP), enabling false positive control in practice. However, stability selection often selects few features because existing bounds on E(FP) are relatively loose. In this paper, we introduce a novel approach to stability selection based on integrating stability paths rather than maximizing over them. This yields upper bounds on E(FP) that are much stronger than previous bounds, leading to significantly more true positives in practice for the same target E(FP). Furthermore, our method requires no more computation than the original stability selection algorithm. We demonstrate the method on simulations and real data from two cancer studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.