ArticleJournal of the American Statistical Association2018
Weighted False Discovery Rate Control in Large-Scale Multiple Testing.
Article in Journal of the American Statistical Association, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Joint mirror procedure: controlling false discovery rate for identifying simultaneous signals.Biometrics · 2024Article
- False Discovery Rate Control for Lesion-Symptom Mapping With Heterogeneous Data via Weighted p-Values.Biometrical journal. Biometrische Zeitschrift · 2024Article
- DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data.Proceedings of machine learning research · 2024Article
- Directional false discovery rate control in large-scale multiple comparisons.Journal of applied statistics · 2024Article
- Article
- OPTIMAL FALSE DISCOVERY RATE CONTROL FOR LARGE SCALE MULTIPLE TESTING WITH AUXILIARY INFORMATION.Annals of statistics · 2022Article
- Null-free False Discovery Rate Control Using Decoy Permutations.Acta mathematicae applicatae Sinica (English series) · 2022Article
- Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR.PLoS genetics · 2021Article
- Accurate error control in high-dimensional association testing using conditional false discovery rates.Biometrical journal. Biometrische Zeitschrift · 2021Article
- Flexible and Fast Spatial Return Level Estimation Via a Spatially Fused Penalty.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2021Article
- Multiple comparison correction methods for whole-body magnetic resonance imaging.Journal of medical imaging (Bellingham, Wash.) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The use of weights provides an effective strategy to incorporate prior domain knowledge in large-scale inference. This paper studies weighted multiple testing in a decision-theoretic framework. We develop oracle and data-driven procedures that aim to maximize the expected number of true positives subject to a constraint on the weighted false discovery rate. The asymptotic validity and optimality of the proposed methods are established. The results demonstrate that incorporating informative domain knowledge enhances the interpretability of results and precision of inference. Simulation studies show that the proposed method controls the error rate at the nominal level, and the gain in power over existing methods is substantial in many settings. An application to a genome-wide association study is discussed.
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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.