Testing and Support Recovery in Population-Based Image Data

• 成果类型: Article
• 署名作者: Qu, Lianqiang; Huang, Jian; Sun, Liuquan; Zhu, Hongtu
• 署名单位: Central China Normal University; Central China Normal University; Hong Kong Polytechnic University; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2025.2525585
• 发表日期: 2026-01-02
• 页码: 440-453
• 关键词: high dimensionality; Multi-sample test; Multiscale adaptive method; neuroimaging data; support recovery; 2-sample test

摘要

In this article, we propose a multiscale adaptive test to detect differences between two samples of intrinsically smoothed image data in high-dimensional context. The test aggregates data from nearby locations using adaptive weights, significantly enhancing statistical power. We demonstrate that the test statistic converges to a Gumbel extreme value distribution under the null hypothesis. Moreover, we investigate its multiscale nature, showing that the chosen scales can grow at a specific polynomial rate of the sample size. We also evaluate its power against sparse alternatives and establish that with probability approaching one, the proposed method can identify the locations where the two means differ from each other. Additionally, we extend the proposed method to multi-sample ANOVA tests. Simulation results suggest that the proposed test outperforms the non-multiscale method that ignores spatial features of imaging data. The procedures are illustrated using a real dataset from the Alzheimer's Disease Neuroimaging Initiative study. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.