Optimal false discovery control of minimax estimators
From MaRDI portal
Publication:6103221
DOI10.3150/22-bej1527arXiv1812.10013OpenAlexW4367318142MaRDI QIDQ6103221
Publication date: 2 June 2023
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1812.10013
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Nearly unbiased variable selection under minimax concave penalty
- SLOPE is adaptive to unknown sparsity and asymptotically minimax
- The horseshoe estimator: posterior concentration around nearly black vectors
- Distribution-free multiple testing
- Asymptotic Bayes-optimality under sparsity of some multiple testing procedures
- SLOPE-adaptive variable selection via convex optimization
- Minimax risk over \(l_ p\)-balls for \(l_ q\)-error
- False discoveries occur early on the Lasso path
- Bayesian estimation of sparse signals with a continuous spike-and-slab prior
- Variable selection with Hamming loss
- Needles and straw in haystacks: Empirical Bayes estimates of possibly sparse sequences
- Needles and straw in a haystack: posterior concentration for possibly sparse sequences
- Model selection and sharp asymptotic minimaxity
- Bayesian shrinkage towards sharp minimaxity
- On the asymptotic properties of SLOPE
- On spike and slab empirical Bayes multiple testing
- A general framework for Bayes structured linear models
- Adapting to unknown sparsity by controlling the false discovery rate
- Nearly optimal Bayesian shrinkage for high-dimensional regression
- Calibration and empirical Bayes variable selection
- The Covariance Inflation Criterion for Adaptive Model Selection
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Local asymptotic coding and the minimum description length
- Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
- Minimax Rates of Estimation for High-Dimensional Linear Regression Over $\ell_q$-Balls
- Dirichlet–Laplace Priors for Optimal Shrinkage
- High-Dimensional Variable Selection With Reciprocal L1-Regularization
- Gaussian model selection
This page was built for publication: Optimal false discovery control of minimax estimators