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

• 成果类型: Article
• 署名作者: Cai, T. Tony; Sun, Wenguang; Xia, Yin
• 署名单位: University of Pennsylvania; University of Southern California; Fudan University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1859379
• 发表日期: 2022
• 页码: 1370-1383
• 关键词: false discovery rate; gene-expression; EMPIRICAL BAYES; bootstrap; POWER

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.