NAPA：邻域辅助与后验调整的两样本推断
NAPA: Neighborhood-Assisted and Posterior-Adjusted Two-sample Inference
AI总结：
提出NAPA方法结合空间平滑性与稀疏性辅助信息调整$p$-值，在控制错误发现的同时提升两样本多重检验效能，并给出理论保证与实验验证。
摘要：
稀疏空间数据的两样本多重检验问题在各种科学应用中频繁出现。本文开发了一种新颖的邻域辅助与后验调整（NAPA）方法，结合空间平滑性和稀疏性类型的辅助信息，在控制多重检验错误发现的同时提高检验效能。我们将辅助信息转化为一组权重以调整$p$-值，其中空间模式由位置的排序编码，稀疏结构由一组辅助协变量编码。我们建立了所提检验的理论性质，包括相对于一些最先进替代检验的效能提升保证，以及渐近错误发现控制。通过大量模拟和两个神经影像学应用，我们证明了该检验的有效性。
英文摘要：
Two-sample multiple testing problems of sparse spatial data are frequently arising in a variety of scientific applications. In this article, we develop a novel neighborhood-assisted and posterior-adjusted (NAPA) approach to incorporate both the spatial smoothness and sparsity type side information to improve the power of the test while controlling the false discovery of multiple testing. We translate the side information into a set of weights to adjust the $p$-values, where the spatial pattern is encoded by the ordering of the locations, and the sparsity structure is encoded by a set of auxiliary covariates. We establish the theoretical properties of the proposed test, including the guaranteed power improvement over some state-of-the-art alternative tests, and the asymptotic false discovery control. We demonstrate the efficacy of the test through intensive simulations and two neuroimaging applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
