ArticleJournal of the American Statistical Association2019
FarmTest: Factor-adjusted robust multiple testing with approximate false discovery control.
Article in Journal of the American Statistical Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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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
6 citing papers in PubMed.
- Article
- Asymptotic uncertainty of false discovery proportion.Biometrics · 2024Article
- The roles, challenges, and merits of the p value.Patterns (New York, N.Y.) · 2023Review
- Robust high dimensional factor models with applications to statistical machine learning.Statistical science : a review journal of the Institute of Mathematical Statistics · 2021Article
- A Projection-based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models.Journal of econometrics · 2020Article
- NOISY MATRIX COMPLETION: UNDERSTANDING STATISTICAL GUARANTEES FOR CONVEX RELAXATION VIA NONCONVEX OPTIMIZATION.SIAM journal on optimization : a publication of the Society for Industrial and Applied Mathematics · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Large-scale multiple testing with correlated and heavy-tailed data arises in a wide range of research areas from genomics, medical imaging to finance. Conventional methods for estimating the false discovery proportion (FDP) often ignore the effect of heavy-tailedness and the dependence structure among test statistics, and thus may lead to inefficient or even inconsistent estimation. Also, the commonly imposed joint normality assumption is arguably too stringent for many applications. To address these challenges, in this paper we propose a Factor-Adjusted Robust Multiple Testing (
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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.