False Discovery Rate Smoothing (smoothfdr). The smoothfdr package provides an empirical-Bayes method for exploiting spatial structure in large multiple-testing problems. FDR smoothing automatically finds spatially localized regions of significant test statistics. It then relaxes the threshold of statistical significance within these regions, and tightens it elsewhere, in a manner that controls the overall false-discovery rate at a given level. This results in increased power and cleaner spatial separation of signals from noise. It tends to detect patterns that are much more biologically plausible than those detected by existing FDR-controlling methods. For a detailed description of how FDR smoothing works, see the paper on arXiv.
Keywords for this software
References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Consonni, Guido; Fouskakis, Dimitris; Liseo, Brunero; Ntzoufras, Ioannis: Prior distributions for objective Bayesian analysis (2018)
- Durante, Daniele; Dunson, David B.: Bayesian inference and testing of group differences in brain networks (2018)
- Madrid-Padilla, Oscar-Hernan; Polson, Nicholas G.; Scott, James: A deconvolution path for mixtures (2018)
- Tansey, Wesley; Koyejo, Oluwasanmi; Poldrack, Russell A.; Scott, James G.: False discovery rate smoothing (2018)