False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation
From MaRDI portal
Publication:139626
DOI10.48550/arXiv.2002.11992zbMath1514.62143arXiv2002.11992OpenAlexW3174206601MaRDI QIDQ139626
Xu Guo, Lilun Du, Wenguang Sun, Changliang Zou, Lilun Du, Xu Guo, Wenguang Sun, Changliang Zou
Publication date: 27 February 2020
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2002.11992
uniform convergenceempirical distributionsample-splittingmoderate deviation theoryintegrative multiple testing
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Paired and multiple comparisons; multiple testing (62J15)
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Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Innovated higher criticism for detecting sparse signals in correlated noise
- Sparse inverse covariance estimation with the graphical lasso
- Phase transition and regularized bootstrap in large-scale \(t\)-tests with false discovery rate control
- High-dimensional variable selection
- Controlling the false discovery rate via knockoffs
- SLOPE-adaptive variable selection via convex optimization
- Genome-wide significance levels and weighted hypothesis testing
- Robustness of multiple testing procedures against dependence
- Asymptotic behavior of M-estimators of p regression parameters when \(p^ 2/n\) is large. I. Consistency
- Nonconcave penalized likelihood with a diverging number of parameters.
- High-dimensional semiparametric Gaussian copula graphical models
- On the conditions used to prove oracle results for the Lasso
- A rate optimal procedure for recovering sparse differences between high-dimensional means under dependence
- Robust inference with knockoffs
- A significance test for the lasso
- High-dimensional variable screening and bias in subsequent inference, with an empirical comparison
- A knockoff filter for high-dimensional selective inference
- False discovery rate control via debiased Lasso
- Dependency and false discovery rate: asymptotics
- On false discovery control under dependence
- False discovery rate analysis of brain diffusion direction maps
- Adapting to unknown sparsity by controlling the false discovery rate
- Significance analysis of microarrays applied to the ionizing radiation response
- Large-Scale Multiple Testing under Dependence
- A general framework for multiple testing dependence
- A Factor Model Approach to Multiple Testing Under Dependence
- p-Values for High-Dimensional Regression
- A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation
- Multiple Testing with the Structure-Adaptive Benjamini–Hochberg Algorithm
- The effect of correlation in false discovery rate estimation
- Oracle and Adaptive Compound Decision Rules for False Discovery Rate Control
- False Discovery Rates for Spatial Signals
- Multiple Hypotheses Testing with Weights
- Empirical Bayes Analysis of a Microarray Experiment
- Robustness and Accuracy of Methods for High Dimensional Data Analysis Based on Student’s t-Statistic
- Estimating False Discovery Proportion Under Arbitrary Covariance Dependence
- Comment
- False Discovery Control for Random Fields
- Strong Control, Conservative Point Estimation and Simultaneous Conservative Consistency of False Discovery Rates: A Unified Approach
- Panning for Gold: ‘Model-X’ Knockoffs for High Dimensional Controlled Variable Selection
- AdaPT: An Interactive Procedure for Multiple Testing with Side Information
- A general interactive framework for false discovery rate control under structural constraints
- GAP: A General Framework for Information Pooling in Two-Sample Sparse Inference
- Covariate-Assisted Ranking and Screening for Large-Scale Two-Sample Inference
- Correlated z-Values and the Accuracy of Large-Scale Statistical Estimates
- Multiple Testing for Pattern Identification, With Applications to Microarray Time-Course Experiments
- Correlation and Large-Scale Simultaneous Significance Testing
- Variance of the Number of False Discoveries
- Estimation of the False Discovery Proportion with Unknown Dependence
- False Discovery Control in Large-Scale Spatial Multiple Testing
- Large-Scale Simultaneous Hypothesis Testing