ArticleJournal of the American Statistical Association2020
RANK: Large-Scale Inference with Graphical Nonlinear Knockoffs.
Article in Journal of the American Statistical Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- High-Dimensional Knockoffs Inference for Time Series Data.Journal of the American Statistical Association · 2025Article
- Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic.Journal of econometrics · 2023Article
- Null-free False Discovery Rate Control Using Decoy Permutations.Acta mathematicae applicatae Sinica (English series) · 2022Article
- Asymptotic Theory of Eigenvectors for Random Matrices with Diverging Spikes.Journal of the American Statistical Association · 2022Article
- DeepLINK: Deep learning inference using knockoffs with applications to genomics.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- KIMI: Knockoff Inference for Motif Identification from molecular sequences with controlled false discovery rate.Bioinformatics (Oxford, England) · 2021Article
- IPAD: Stable Interpretable Forecasting with Knockoffs Inference.Journal of the American Statistical Association · 2020Article
- Nonuniformity of P-values Can Occur Early in Diverging Dimensions.Journal of machine learning research : JMLR · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Power and reproducibility are key to enabling refined scientific discoveries in contemporary big data applications with general high-dimensional nonlinear models. In this paper, we provide theoretical foundations on the power and robustness for the model-X knockoffs procedure introduced recently in Candès, Fan, Janson and Lv (2018) in high-dimensional setting when the covariate distribution is characterized by Gaussian graphical model. We establish that under mild regularity conditions, the power of the oracle knockoffs procedure with known covariate distribution in high-dimensional linear models is asymptotically one as sample size goes to infinity. When moving away from the ideal case, we suggest the modified model-X knockoffs method called graphical nonlin-ear knockoffs (RANK) to accommodate the unknown covariate distribution. We provide theoretical justifications on the robustness of our modified procedure by showing that the false discovery rate (FDR) is asymptotically controlled at the target level and the power is asymptotically one with the estimated covariate distribution. To the best of our knowledge, this is the first formal theoretical result on the power for the knockoffs procedure. Simulation results demonstrate that compared to existing approaches, our method performs competitively in both FDR control and power. A real data set is analyzed to further assess the performance of the suggested knockoffs procedure.
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Registered trials
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