论文标题
纳帕：邻里辅助和后部调整后的两样本推断
NAPA: Neighborhood-Assisted and Posterior-Adjusted Two-sample Inference
论文作者
论文摘要
在各种科学应用中，经常出现稀疏空间数据的两个样本多重测试问题。在本文中，我们开发了一种新颖的社区辅助和后置调节方法（NAPA）方法，以结合空间平滑度和稀疏类型的侧面信息，以提高测试的功能，同时控制多个测试的错误发现。我们将侧面信息转换为一组权重，以调整$ p $值，其中空间模式是由位置的排序编码的，并且稀疏结构由一组辅助协变量编码。我们建立了拟议的测试的理论特性，包括对某些最新替代测试的保证功率提高以及渐近假发现控制。我们通过密集的模拟和两个神经影像应用证明了测试的功效。
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.
