ArticleThe annals of applied statistics2019
MULTILAYER KNOCKOFF FILTER: CONTROLLED VARIABLE SELECTION AT MULTIPLE RESOLUTIONS.
Article in The annals of applied statistics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies.Nature communications · 2022Pooled it
- CIT-Lasso: a scalable approach beyond guilty by association for identifying causal variants from genome-wide summary statistics.Genome biology · 2026Article
- A Tree-Based Model for Addressing Sparsity and Taxa Covariance in Microbiome Compositional Count Data.Statistics in medicine · 2026Article
- Multiple Testing of Mix-and-Match Feature Sets in Multi-Omics.Statistics in medicine · 2026Article
- Catch me if you can: signal localization with knockoffJournal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- Controlling the False Split Rate in Tree-Based Aggregation.Journal of the American Statistical Association · 2025Article
- Second-order group knockoffs with applications to genome-wide association studies.Bioinformatics (Oxford, England) · 2024Article
- Transcriptome data are insufficient to control false discoveries in regulatory network inference.Cell systems · 2024Article
- Article
- Analysis of Microbiome Data.Annual review of statistics and its application · 2024Article
- Grace-AKO: a novel and stable knockoff filter for variable selection incorporating gene network structures.BMC bioinformatics · 2022Article
- Smoothed Nested Testing on Directed Acyclic Graphs.Biometrika · 2022Article
- False discovery rate control in genome-wide association studies with population structure.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Multi-resolution localization of causal variants across the genome.Nature communications · 2020Article
- Discussion of 'Gene hunting with hidden Markov model knockoffs'.Biometrika · 2019Article
- MULTILAYER KNOCKOFF FILTER: CONTROLLED VARIABLE SELECTION AT MULTIPLE RESOLUTIONS.The annals of applied statistics · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We tackle the problem of selecting from among a large number of variables those that are "important" for an outcome. We consider situations where groups of variables are also of interest. For example, each variable might be a genetic polymorphism, and we might want to study how a trait depends on variability in genes, segments of DNA that typically contain multiple such polymorphisms. In this context, to discover that a variable is relevant for the outcome implies discovering that the larger entity it represents is also important. To guarantee meaningful results with high chance of replicability, we suggest controlling the rate of false discoveries for findings at the level of individual variables and at the level of groups. Building on the knockoff construction of Barber and Candès [
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Registered trials
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