Uncertainty quantification for robust variable selection and multiple testing
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Publication:2106788
DOI10.1214/22-EJS2088MaRDI QIDQ2106788
Nurzhan Nurushev, Eduard Belitser
Publication date: 19 December 2022
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2109.09239
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Cites Work
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- Risk hull method and regularization by projections of ill-posed inverse problems
- Distribution-free multiple testing
- Microarrays, empirical Bayes and the two-groups model
- Some problems of hypothesis testing leading to infinitely divisible distributions
- Variable selection with Hamming loss
- Minimax detection of a signal for \(l^ n\)-balls.
- Higher criticism for detecting sparse heterogeneous mixtures.
- On coverage and local radial rates of credible sets
- False discovery rate control with unknown null distribution: is it possible to mimic the oracle?
- On spike and slab empirical Bayes multiple testing
- Adaptive variable selection in nonparametric sparse regression
- Needles and straw in a haystack: robust confidence for possibly sparse sequences
- Adaptive variable selection in nonparametric sparse additive models
- Adapting to unknown sparsity by controlling the false discovery rate
- Generalizations of the familywise error rate
- Adaptive FDR control under independence and dependence
- Operating Characteristics and Extensions of the False Discovery Rate Procedure
- Optimal Rates and Tradeoffs in Multiple Testing
- Empirical Bayes Estimates for Large-Scale Prediction Problems
- Local Posterior Concentration Rate for Multilevel Sparse Sequences
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