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False Discovery Rate Smoothing.
- Source :
-
Journal of the American Statistical Association . Sep2018, Vol. 113 Issue 523, p1156-1171. 16p. - Publication Year :
- 2018
-
Abstract
- We present false discovery rate (FDR) smoothing, 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. The approach requires solving a nonstandard high-dimensional optimization problem, for which an efficient augmented-Lagrangian algorithm is presented. In simulation studies, FDR smoothing exhibits state-of-the-art performance at modest computational cost. In particular, it is shown to be far more robust than existing methods for spatially dependent multiple testing. We also apply the method to a dataset from an fMRI experiment on spatial working memory, where it detects patterns that are much more biologically plausible than those detected by standard FDR-controlling methods. All code for FDR smoothing is publicly available in Python and R (https://github.com/tansey/smoothfdr). Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01621459
- Volume :
- 113
- Issue :
- 523
- Database :
- Academic Search Index
- Journal :
- Journal of the American Statistical Association
- Publication Type :
- Academic Journal
- Accession number :
- 132271360
- Full Text :
- https://doi.org/10.1080/01621459.2017.1319838