LAWS: A locally adaptive weighting and screening approach to spatial multiple testing
LAWS: A locally adaptive weighting and screening approach to spatial multiple testing
复制标题
LAWS：用于空间多重测试的局部自适应加权和筛选方法
DOI：
10.1080/01621459.2020.1859379
复制
发表时间：
2021
影响因子：
3.7
通讯作者：
Yin Xia
中科院分区：
文献类型：
--
作者：
Tony Cai;Wenguang Sun;Yin Xia
在大规模多重测试中利用空间模式有望提高错误发现率(FDR)分析的能力和可解释性。本文提出了一类新的局部自适应加权和筛选(律)规则，它直接将有用的局部模式结合到推理中。该思想涉及到根据估计的局部稀疏性水平来构造稳健的和结构自适应的权重。法律为广泛的空间问题提供了一个统一的框架，并完全由数据驱动。结果表明，在较温和的相依条件下，律对FDR进行渐近控制。利用仿真数据对有限样本的性能进行了研究，结果表明，这些规律控制着FDR，并且在功率上优于现有的方法。在许多情况下，效率收益是相当可观的。通过在二维和三维图像分析中的应用，进一步说明了这些定律的优点。这篇文章的补充材料可以在网上找到。
Exploiting spatial patterns in large-scale multiple testing promises to improve both power and interpretability of false discovery rate (FDR) analyses. This article develops a new class of locally adaptive weighting and screening (LAWS) rules that directly incorporates useful local patterns into inference. The idea involves constructing robust and structure-adaptive weights according to the estimated local sparsity levels. LAWS provides a unified framework for a broad range of spatial problems and is fully data-driven. It is shown that LAWS controls the FDR asymptotically under mild conditions on dependence. The finite sample performance is investigated using simulated data, which demonstrates that LAWS controls the FDR and outperforms existing methods in power. The efficiency gain is substantial in many settings. We further illustrate the merits of LAWS through applications to the analysis of two-dimensional and three-dimensional images. Supplementary materials for this article are available online.