Generalized α-investing: definitions, optimality results and application to public databases

• 成果类型: Article
• 署名作者: Aharoni, Ehud; Rosset, Saharon
• 署名单位: International Business Machines (IBM); IBM ISRAEL; Tel Aviv University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12048
• 发表日期: 2014
• 页码: 771-794
• 关键词: false discovery rate; association

摘要

The increasing prevalence and utility of large public databases necessitates the development of appropriate methods for controlling false discovery. Motivated by this challenge, we discuss the generic problem of testing a possibly infinite stream of null hypotheses. In this context, Foster and Stine suggested a novel method named alpha-investing for controlling a false discovery measure known as mFDR. We develop a more general procedure for controlling mFDR, of which alpha-investing is a special case. We show that, in common practical situations, the general procedure can be optimized to produce an expected reward optimal version, which is more powerful than alpha-investing. We then present the concept of quality preserving databases which was originally introduced by Aharoni and co-workers, which formalizes efficient public database management to save costs and to control false discovery simultaneously. We show how one variant of generalized alpha-investing can be used to control mFDR in a quality preserving database and to lead to significant reduction in costs compared with naive approaches for controlling the familywise error rate implemented by Aharoni and co-workers.