非随机仿冒品:利用e值控制错误发现率

IF 3.1 1区 数学 Q1 STATISTICS & PROBABILITY
Zhimei Ren, Rina Foygel Barber
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引用次数: 4

摘要

Model-X仿制品是一种灵活的高维回归算法包装方法,为控制错误发现率(FDR)提供了保证。由于该方法固有的随机性,在同一数据集上运行不同的模型x仿制品通常会产生不同的选择变量集,这在实践中是不希望的。在本文中,我们介绍了一种具有可证明的FDR控制的去随机化模型x仿制品的方法。我们提出的方法的关键见解在于发现仿制程序本质上是一个e-BH程序。我们利用这种联系,并通过汇总多个仿冒实现产生的e值来消除模型x仿冒的随机性。我们证明,在没有任何附加条件的情况下,非随机化过程将FDR控制在期望的水平(相反,先前提出的非随机化方法不能保证FDR控制)。通过数值实验对所提出的方法进行了评估,我们发现,与模型x仿制品相比,非随机程序实现了相当的功率,并显着降低了选择变异性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Derandomised knockoffs: leveraging e-values for false discovery rate control
Abstract Model-X knockoffs is a flexible wrapper method for high-dimensional regression algorithms, which provides guaranteed control of the false discovery rate (FDR). Due to the randomness inherent to the method, different runs of model-X knockoffs on the same dataset often result in different sets of selected variables, which is undesirable in practice. In this article, we introduce a methodology for derandomising model-X knockoffs with provable FDR control. The key insight of our proposed method lies in the discovery that the knockoffs procedure is in essence an e-BH procedure. We make use of this connection and derandomise model-X knockoffs by aggregating the e-values resulting from multiple knockoff realisations. We prove that the derandomised procedure controls the FDR at the desired level, without any additional conditions (in contrast, previously proposed methods for derandomisation are not able to guarantee FDR control). The proposed method is evaluated with numerical experiments, where we find that the derandomised procedure achieves comparable power and dramatically decreased selection variability when compared with model-X knockoffs.
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来源期刊
CiteScore
8.80
自引率
0.00%
发文量
83
审稿时长
>12 weeks
期刊介绍: Series B (Statistical Methodology) aims to publish high quality papers on the methodological aspects of statistics and data science more broadly. The objective of papers should be to contribute to the understanding of statistical methodology and/or to develop and improve statistical methods; any mathematical theory should be directed towards these aims. The kinds of contribution considered include descriptions of new methods of collecting or analysing data, with the underlying theory, an indication of the scope of application and preferably a real example. Also considered are comparisons, critical evaluations and new applications of existing methods, contributions to probability theory which have a clear practical bearing (including the formulation and analysis of stochastic models), statistical computation or simulation where original methodology is involved and original contributions to the foundations of statistical science. Reviews of methodological techniques are also considered. A paper, even if correct and well presented, is likely to be rejected if it only presents straightforward special cases of previously published work, if it is of mathematical interest only, if it is too long in relation to the importance of the new material that it contains or if it is dominated by computations or simulations of a routine nature.
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