ArticleAnnals of statistics2022
OPTIMAL FALSE DISCOVERY RATE CONTROL FOR LARGE SCALE MULTIPLE TESTING WITH AUXILIARY INFORMATION.
Article in Annals of statistics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed.
- Diagnosing scientific replicability through probabilistic distinguishability.Bioinformatics (Oxford, England) · 2026Article
- Coconut: covariate-assisted composite null hypothesis testing with applications to replicability analysis of high-throughput experimental data.BMC bioinformatics · 2025Article
- STAREG: Statistical replicability analysis of high throughput experiments with applications to spatial transcriptomic studies.PLoS genetics · 2024Article
- A powerful approach to identify replicable variants in genome-wide association studies.American journal of human genetics · 2024Article
- DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data.Proceedings of machine learning research · 2024Article
- Statistical analysis of spatially resolved transcriptomic data by incorporating multiomics auxiliary information.Genetics · 2022Article
- LinDA: linear models for differential abundance analysis of microbiome compositional data.Genome biology · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Large-scale multiple testing is a fundamental problem in high dimensional statistical inference. It is increasingly common that various types of auxiliary information, reflecting the structural relationship among the hypotheses, are available. Exploiting such auxiliary information can boost statistical power. To this end, we propose a framework based on a two-group mixture model with varying probabilities of being null for different hypotheses
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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.