Generalized α-investing: definitions, optimality results and application to public databases
Generalized α-investing: definitions, optimality results and application to public databases
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DOI：
10.1111/rssb.12048
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发表时间：
2014-09-01
影响因子：
5.8
通讯作者：
Rosset, Saharon
中科院分区：
文献类型：
--
作者：
Aharoni, Ehud;Rosset, Saharon
大型公共数据库的日益普及和实用性需要开发适当的方法来控制错误发现。出于这一挑战，我们讨论了测试一个可能无限流的零假设的一般问题。在这种情况下，Foster和Stine提出了一种名为alpha投资的新方法，用于控制称为mFDR的错误发现措施。我们开发了一个更一般的程序来控制mFDR，其中阿尔法投资是一个特例。我们表明，在常见的实际情况下，一般的程序可以优化，以产生一个预期的回报最优版本，这是更强大的阿尔法投资。然后，我们提出了质量保持数据库的概念，这最初是由Aharoni和同事，正式有效的公共数据库管理，以节省成本，同时控制错误的发现。我们展示了如何广义α投资的一个变体可以用来控制mFDR在质量保持数据库，并导致显着降低成本相比，天真的方法来控制家庭明智的错误率实施Aharoni和同事。
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.