论文标题
多元距离矩阵回归的歧管响应变量
Multivariate distance matrix regression for a manifold-valued response variable
论文作者
论文摘要
在本文中，我们建议将大地距离与多变量距离矩阵回归（称为几何MDMR）一起用作歧管值数据的强大第一步分析方法。从地震分析到分析大脑模式的文献中，歧管值的数据频率更高。考虑到这些数据的结构，可以增加分析的复杂性，但可以从数据方面产生更多可解释的结果。为了测试几何MDMR，我们开发了一种模拟fMRI数据的功能连接矩阵以执行仿真研究的方法，这表明我们的方法在fMRI分析中优于当前标准。
In this paper, we propose the use of geodesic distances in conjunction with multivariate distance matrix regression, called geometric-MDMR, as a powerful first step analysis method for manifold-valued data. Manifold-valued data is appearing more frequently in the literature from analyses of earthquake to analysing brain patterns. Accounting for the structure of this data increases the complexity of your analysis, but allows for much more interpretable results in terms of the data. To test geometric-MDMR, we develop a method to simulate functional connectivity matrices for fMRI data to perform a simulation study, which shows that our method outperforms the current standards in fMRI analysis.
