False Discovery Rate Smoothing

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 with...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of the American Statistical Association Jg. 113; H. 523; S. 1156 - 1171
Hauptverfasser: Tansey, Wesley, Koyejo, Oluwasanmi, Poldrack, Russell A., Scott, James G.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Alexandria Taylor & Francis 03.07.2018
Taylor & Francis Group,LLC
Taylor & Francis Ltd
Schlagworte:
Algorithms
data collection
Discovery
Empirical Bayes
equations
False discovery rate
FDR
Functional magnetic resonance imaging
Fused lasso
magnetic resonance imaging
Multiple hypothesis testing
Noise
Optimization
Regions
Regression analysis
Short term memory
Simulation
Smoothing
Spatial smoothing
Statistical methods
Statistical significance
Statistical tests
Statistics
system optimization
Theory and Methods
ISSN:0162-1459, 1537-274X, 1537-274X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0162-1459
1537-274X
1537-274X
DOI:10.1080/01621459.2017.1319838