论文标题
带有附带信息的仿制
Knockoffs with Side Information
论文作者
论文摘要
我们考虑在可用的侧面信息时评估来自数据集的多个变量或因素的重要性的问题。原则上，使用附带信息可以使统计学家注意具有更大潜力的变量，从而导致更多发现。我们引入了一个自适应仿冒滤波器，该过滤器概括了仿冒程序（Barber andCandès，2015;Candès等，2018），因为它同时使用手头和侧面信息来适应研究中的变量，并专注于最有前途的变量。自适应仿冒品控制有限样本的错误发现率（FDR），我们通过将其与其他结构化多个测试方法进行比较来演示其功能。我们还将方法应用于实际遗传数据，以便找到遗传变异与克罗恩病和脂质水平等各种表型之间的关联。在这里，自适应仿冒品比以前在同一数据集的研究中报道的发现更多。
We consider the problem of assessing the importance of multiple variables or factors from a dataset when side information is available. In principle, using side information can allow the statistician to pay attention to variables with a greater potential, which in turn, may lead to more discoveries. We introduce an adaptive knockoff filter, which generalizes the knockoff procedure (Barber and Candès, 2015; Candès et al., 2018) in that it uses both the data at hand and side information to adaptively order the variables under study and focus on those that are most promising. Adaptive knockoffs controls the finite-sample false discovery rate (FDR) and we demonstrate its power by comparing it with other structured multiple testing methods. We also apply our methodology to real genetic data in order to find associations between genetic variants and various phenotypes such as Crohn's disease and lipid levels. Here, adaptive knockoffs makes more discoveries than reported in previous studies on the same datasets.
