Abstract
The false discovery rate (FDR) has been a widely used error measure in situations where a large number of tests are conducted simultaneously. Most methods that control the FDR at a prespeci ed level, or estimate the FDR of a multiple testing procedure (FDR procedures), were essentially developed for continuous test statistics. As such, their performances need to be carefully assessed when applied to discrete test statistics. We review some popular FDR procedures, point out a key reason for their excessive conservativeness when applied to discrete p-values, and suggest an improvement for these methods for such p-values.
Keywords
False discovery rate (FDR), Adaptive FDR procedures, Discrete p-values, Generalized FDR estimator
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Chen, Xiongzhi and Doerge, R. W.
(2012).
"TOWARDS BETTER FDR PROCEDURES FOR DISCRETE TEST STATISTICS,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1040
TOWARDS BETTER FDR PROCEDURES FOR DISCRETE TEST STATISTICS
The false discovery rate (FDR) has been a widely used error measure in situations where a large number of tests are conducted simultaneously. Most methods that control the FDR at a prespeci ed level, or estimate the FDR of a multiple testing procedure (FDR procedures), were essentially developed for continuous test statistics. As such, their performances need to be carefully assessed when applied to discrete test statistics. We review some popular FDR procedures, point out a key reason for their excessive conservativeness when applied to discrete p-values, and suggest an improvement for these methods for such p-values.
