Controlling False Discovery Rate Using Gaussian Mirrors

DOI10.1080/01621459.2021.1923510zbMath1514.62093arXiv1911.09761OpenAlexW3157176780MaRDI QIDQ6107203

Jun S. Liu, Xin Xing, Zhigen Zhao

Publication date: 3 July 2023

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1911.09761

zbMATH Keywords

false discovery rategenome-wide association studyhigh-dimensional linear regressionpost-selection inference

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Hypothesis testing in multivariate analysis (62H15)

Related Items (7)

A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models ⋮ Comment on “A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models” by Chengguang Dai, Buyu Lin, Xin Xing, and Jun S. Liu ⋮ Discussion of “A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models” ⋮ Discussion of “A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models” by Chenguang Dai, Buyu Lin, Xin Xing, and Jun S. Liu ⋮ Comments on “A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models” ⋮ False Discovery Rate Control via Data Splitting ⋮ StarTrek: combinatorial variable selection with false discovery rate control

Cites Work

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• On asymptotically optimal confidence regions and tests for high-dimensional models
• Multivariate and multiple permutation tests
• Exact post-selection inference, with application to the Lasso
• Valid post-selection inference
• Stability
• Statistics for high-dimensional data. Methods, theory and applications.
• Near-ideal model selection by \(\ell _{1}\) minimization
• High-dimensional variable selection
• Controlling the false discovery rate via knockoffs
• The sparsity and bias of the LASSO selection in high-dimensional linear regression
• Lasso-type recovery of sparse representations for high-dimensional data
• Estimating the dimension of a model
• Asymptotics for Lasso-type estimators.
• The Lasso problem and uniqueness
• Confidence intervals for high-dimensional linear regression: minimax rates and adaptivity
• Robust inference with knockoffs
• Relaxing the assumptions of knockoffs by conditioning
• High-dimensional generalized linear models and the lasso
• High-dimensional graphs and variable selection with the Lasso
• A note on data-splitting for the evaluation of significance levels
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• High-Dimensional Statistics
• Estimating False Discovery Proportion Under Arbitrary Covariance Dependence
• Post‐selection inference for ‐penalized likelihood models
• Panning for Gold: ‘Model-X’ Knockoffs for High Dimensional Controlled Variable Selection
• Regularization and Variable Selection Via the Elastic Net
• Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
• Veridical data science

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