Department of Statistics, University of California, Irvine, Irvine, California, USA.
Center for Brain and Learning Science, Beijing Normal University, Beijing, China.
Stat Med. 2021 Oct 30;40(24):5313-5332. doi: 10.1002/sim.9127. Epub 2021 Jul 2.
We propose a ridge-penalized adaptive Mantel test (AdaMant) for evaluating the association of two high-dimensional sets of features. By introducing a ridge penalty, AdaMant tests the association across many metrics simultaneously. We demonstrate how ridge penalization bridges Euclidean and Mahalanobis distances and their corresponding linear models from the perspective of association measurement and testing. This result is not only theoretically interesting but also has important implications in penalized hypothesis testing, especially in high-dimensional settings such as imaging genetics. Applying the proposed method to an imaging genetic study of visual working memory in healthy adults, we identified interesting associations of brain connectivity (measured by electroencephalogram coherence) with selected genetic features.
我们提出了一种基于岭惩罚的自适应曼特尔检验（AdaMant），用于评估两组高维特征之间的关联。通过引入岭惩罚，AdaMant 可以同时测试多个指标之间的关联。我们从关联度量和检验的角度展示了岭惩罚如何桥接欧几里得距离和马氏距离及其相应的线性模型。这一结果不仅在理论上很有趣，而且在惩罚假设检验中具有重要意义，特别是在像影像遗传学这样的高维环境中。将所提出的方法应用于健康成年人视觉工作记忆的影像遗传学研究，我们发现了大脑连通性（通过脑电图相干性测量）与选定遗传特征之间的有趣关联。
