Generalized α-investing: definitions, optimality results and application to public databases
type="main" xml:id="rssb12048-abs-0001"> <title type="main">Summary</title> <p>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 α-investing for controlling a false discovery measure known as mFDR. We develop a more general procedure for controlling mFDR, of which α-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 α-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 α-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.
Year of publication: |
2014
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Authors: | Aharoni, Ehud ; Rosset, Saharon |
Published in: |
Journal of the Royal Statistical Society Series B. - Royal Statistical Society - RSS, ISSN 1369-7412. - Vol. 76.2014, 4, p. 771-794
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Publisher: |
Royal Statistical Society - RSS |
Saved in:
Online Resource
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