Date of this Version
Journal of the American Statistical Association
The use of weights provides an effective strategy to incorporate prior domain knowledge in large-scale inference. This paper studies weighted multiple testing in a decisiontheoretic framework. We develop oracle and data-driven procedures that aim to maximize the expected number of true positives subject to a constraint on the weighted false discovery rate. The asymptotic validity and optimality of the proposed methods are established. The results demonstrate that incorporating informative domain knowledge enhances the interpretability of results and precision of inference. Simulation studies show that the proposed method controls the error rate at the nominal level, and the gain in power over existing methods is substantial in many settings. An application to genome-wide association study is discussed.
Class weights, Decision weights, Multiple testing with groups, Prioritized subsets, Value to cost ratio, Weighted p-value
Basu, P., Cai, T., Das, K., & Sun, W. (2017). Weighted False Discovery Rate Control in Large-Scale Multiple Testing. Journal of the American Statistical Association, http://dx.doi.org/10.1080/01621459.2017.1336443
Available for download on Sunday, July 01, 2018
Date Posted: 27 November 2017
This document has been peer reviewed.