Add to ORKGThis paper explores a connection between empirical Bayes posterior distributions and false discovery rate (FDR) control. In the Gaussian sequence model, this work shows that empirical Bayes-calibrated spike and slab posterior distributions allow a correct FDR control under sparsity. Doing so, it offers a frequentist theoretical validation of empirical Bayes methods in the context of multiple testing. Our theoretical results are illustrated with numerical experiments
<p>This thesis investigates frequentist properties of Bayesian multiple testing procedures in a vari...
In high dimensional variable selection problems, statisticians often seek to design multiple testing...
In the sparse normal means model, coverage of adaptive Bayesian posterior credible sets associated t...
This paper explores a connection between empirical Bayes posterior distributions and false discovery...
We introduce a Bayesian approach to multiple testing. The method is an extension of the false discov...
This paper investigates the multiple testing problem for high-dimensional sparse binary sequences, m...
Recently, the field of multiple hypothesis testing has experienced a great expansion, basically beca...
This paper presents an overview of criteria and methods in multiple testing, with an emphasis on the...
Abstract: This paper considers Bayesian multiple testing under sparsity for polynomial-tailed distri...
Multiple testing, a situation where multiple hypothesis tests are performed simultaneously, is a cor...
Multiple testing, a situation where multiple hypothesis tests are performed simultaneously, is a cor...
The problem of multiple testing is rarely addressed in disease mapping or descriptive epidemiology. ...
The problem of multiple testing is rarely addressed in disease mapping or descriptive epidemiology. ...
Controlling false discovery rate (FDR) is a powerful approach to multiple testing. In many applicati...
This article proposes resampling-based empirical Bayes multiple testing procedures for controlling a...
<p>This thesis investigates frequentist properties of Bayesian multiple testing procedures in a vari...
In high dimensional variable selection problems, statisticians often seek to design multiple testing...
In the sparse normal means model, coverage of adaptive Bayesian posterior credible sets associated t...
This paper explores a connection between empirical Bayes posterior distributions and false discovery...
We introduce a Bayesian approach to multiple testing. The method is an extension of the false discov...
This paper investigates the multiple testing problem for high-dimensional sparse binary sequences, m...
Recently, the field of multiple hypothesis testing has experienced a great expansion, basically beca...
This paper presents an overview of criteria and methods in multiple testing, with an emphasis on the...
Abstract: This paper considers Bayesian multiple testing under sparsity for polynomial-tailed distri...
Multiple testing, a situation where multiple hypothesis tests are performed simultaneously, is a cor...
Multiple testing, a situation where multiple hypothesis tests are performed simultaneously, is a cor...
The problem of multiple testing is rarely addressed in disease mapping or descriptive epidemiology. ...
The problem of multiple testing is rarely addressed in disease mapping or descriptive epidemiology. ...
Controlling false discovery rate (FDR) is a powerful approach to multiple testing. In many applicati...
This article proposes resampling-based empirical Bayes multiple testing procedures for controlling a...
<p>This thesis investigates frequentist properties of Bayesian multiple testing procedures in a vari...
In high dimensional variable selection problems, statisticians often seek to design multiple testing...
In the sparse normal means model, coverage of adaptive Bayesian posterior credible sets associated t...