Asymptotic Bayes-optimality under sparsity of some multiple testing procedures
From MaRDI portal
Publication:638803
DOI10.1214/10-AOS869zbMath1221.62012arXiv1002.3501MaRDI QIDQ638803
Jayanta K. Ghosh, Florian Frommlet, Małgorzata Bogdan, Arijit Chakrabarti
Publication date: 14 September 2011
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1002.3501
Bayesian problems; characterization of Bayes procedures (62C10) Asymptotic properties of parametric tests (62F05) Paired and multiple comparisons; multiple testing (62J15) Compound decision problems in statistical decision theory (62C25)
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Cites Work
- Unnamed Item
- Estimating the proportion of false null hypotheses among a large number of independently tested hypotheses
- Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem
- Asymptotic Bayes-optimality under sparsity of some multiple testing procedures
- On optimality of the Benjamini-Hochberg procedure for the false discovery rate
- Optimal rates of convergence for estimating the null density and proportion of nonnull effects in large-scale multiple testing
- A multivariate empirical Bayes statistic for replicated microarray time course data
- Microarrays, empirical Bayes and the two-groups model
- On the false discovery rate and an asymptotically optimal rejection curve
- Minimax risk over \(l_ p\)-balls for \(l_ q\)-error
- The positive false discovery rate: A Bayesian interpretation and the \(q\)-value
- Some remarks on Simes-type multiple tests of significance.
- Multiple hypotheses testing and expected number of type I errors
- Higher criticism for detecting sparse heterogeneous mixtures.
- A stochastic process approach to false discovery control.
- False discovery rate control with multivariate \(p\)-values
- Optimal weighting for false discovery rate control
- An exploration of aspects of Bayesian multiple testing
- Asymptotic minimaxity of false discovery rate thresholding for sparse exponential data
- Power-enhanced multiple decision functions controlling family-wise error and false discovery rates
- On the performance of FDR control: constraints and a partial solution
- Estimation and confidence sets for sparse normal mixtures
- False discovery and false nondiscovery rates in single-step multiple testing procedures
- Adapting to unknown sparsity by controlling the false discovery rate
- Generalizations of the familywise error rate
- A Theory of some Multiple Decision Problems, I
- A Theory of Some Multiple Decision Problems. II
- The horseshoe estimator for sparse signals
- Oracle and Adaptive Compound Decision Rules for False Discovery Rate Control
- Empirical Bayes Analysis of a Microarray Experiment
- Optimal Sample Size for Multiple Testing
- Strong Control, Conservative Point Estimation and Simultaneous Conservative Consistency of False Discovery Rates: A Unified Approach
- Operating Characteristics and Extensions of the False Discovery Rate Procedure
- An improved Bonferroni procedure for multiple tests of significance
- Estimating the Null and the Proportion of Nonnull Effects in Large-Scale Multiple Comparisons
- Large-Scale Simultaneous Hypothesis Testing
- A Bayesian Mixture Model for Differential Gene Expression