scholarly journals Sample size and positive false discovery rate control for multiple testing

2007 ◽  
Vol 1 (0) ◽  
pp. 77-118 ◽  
Author(s):  
Zhiyi Chi
Keyword(s):  
Sample Size ◽  
False Discovery Rate ◽  
Rate Control ◽  
Multiple Testing ◽  
False Discovery Rate Control ◽  
False Discovery ◽  
Positive False Discovery Rate

scholarly journals Weighted False Discovery Rate Control in Large-Scale Multiple Testing

2018 ◽  
Vol 113 (523) ◽  
pp. 1172-1183 ◽  
Author(s):  
Pallavi Basu ◽  
T. Tony Cai ◽  
Kiranmoy Das ◽  
Wenguang Sun
Keyword(s):  
False Discovery Rate ◽  
Rate Control ◽  
Multiple Testing ◽  
Large Scale ◽  
False Discovery Rate Control ◽  
False Discovery

scholarly journals A general interactive framework for false discovery rate control under structural constraints

Biometrika ◽  
2020 ◽  
Author(s):  
Lihua Lei ◽  
Aaditya Ramdas ◽  
William Fithian
Keyword(s):  
False Discovery Rate ◽  
Rate Control ◽  
Multiple Testing ◽  
Structural Information ◽  
Directed Acyclic Graphs ◽  
Structural Constraints ◽  
False Discovery Rate Control ◽  
Region Detection ◽  
False Discovery ◽  
Data Adaptive

Summary We propose a general framework based on selectively traversed accumulation rules for interactive multiple testing with generic structural constraints on the rejection set. It combines accumulation tests from ordered multiple testing with data-carving ideas from post-selection inference, allowing highly flexible adaptation to generic structural information. Our procedure defines an interactive protocol for gradually pruning a candidate rejection set, beginning with the set of all hypotheses and shrinking the set with each step. By restricting the information at each step via a technique we call masking, our protocol enables interaction while controlling the false discovery rate in finite samples for any data-adaptive update rule that the analyst may choose. We suggest update rules for a variety of applications with complex structural constraints, demonstrate that selectively traversed accumulation rules perform well in problems ranging from convex region detection to false discovery rate control on directed acyclic graphs, and show how to extend the framework to regression problems where knockoff statistics are available in lieu of $p$-values.

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