Weighted False Discovery Rate Control in Large-Scale Multiple Testing

Basu, P and Cai, T T and Das, K and Sun, W (2018) Weighted False Discovery Rate Control in Large-Scale Multiple Testing. Journal of the American Statistical Association, 113 (523). pp. 1172-1183.

Full text not available from this repository. (Request a copy)

Abstract

ABSTRACTThe use of weights provides an effective strategy to incorporate prior domain knowledge in large-scale inference. This article studies weighted multiple testing in a decision-theoretical 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 a genome-wide association study is discussed. Supplementary materials for this article are available online.

Affiliation: Indian School of Business
ISB Creiators:
ISB Creators
ORCiD
Basu, P
UNSPECIFIED
Item Type: Article
Additional Information: The research paper was published by the author with the affiliation of Tel Aviv University.
Uncontrolled Keywords: Class Weights, Decision Weights, Prioritized, Subsets, Value-to-cost, Ratio, Weighted, p-value
Subjects: Operations Management
Depositing User: Gurusrinivasan K
Date Deposited: 09 May 2021 06:14
Last Modified: 09 May 2021 06:14
URI: https://eprints.exchange.isb.edu/id/eprint/1476
Publisher URL: https://doi.org/10.1080/01621459.2017.1336443
Publisher OA policy: https://v2.sherpa.ac.uk/id/publication/20802
Related URLs:

Actions (login required)

View Item View Item
Statistics for DESI ePrint 1476 Statistics for this ePrint Item