LAWS: A Locally Adaptive Weighting and Screening Approach to Spatial Multiple Testing

被引:12
作者
Cai, T. Tony [1 ]
Sun, Wenguang [2 ]
Xia, Yin [3 ]
机构
[1] Univ Penn, Wharton Sch, Dept Stat, Philadelphia, PA 19104 USA
[2] Univ Southern Calif, Dept Data Sci & Operat, Los Angeles, CA 90007 USA
[3] Fudan Univ, Sch Management, Dept Stat, Shanghai 200433, Peoples R China
基金
美国国家科学基金会;
关键词
Adjusted p-value; Covariate-assisted inference; Dependent tests; False discovery rate; Structured multiple testing; FALSE DISCOVERY RATE; GENE-EXPRESSION; EMPIRICAL BAYES; BOOTSTRAP; POWER;
D O I
10.1080/01621459.2020.1859379
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
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. for this article are available online.
引用
收藏
页码:1370 / 1383
页数:14
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