Copula Gaussian Graphical Models for Functional Data
Copula Gaussian Graphical Models for Functional Data
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DOI：
10.1080/01621459.2020.1817750
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发表时间：
2020-09
影响因子：
3.7
通讯作者：
Eftychia Solea;Bing Li
中科院分区：
文献类型：
--
作者：
Eftychia Solea;Bing Li
摘要我们介绍了一种多元函数数据的统计图形模型，这些数据在医学应用中很常见，如EEG和fMRI。最近发表的泛函图形模型依赖于多元高斯过程假设，但我们通过引入泛函Copula高斯图形模型(FCGGM)来放松它。该模型去掉了边际高斯假设，但保留了高斯相依结构的简单性，这对大数据特别有吸引力。我们建立了FCGGM的四个估计量，并证明了其中一个估计量的相合性和收敛速度。我们通过仿真将我们的FCGGM模型与现有的函数高斯图形模型进行了比较，并将我们的方法应用于脑电数据集来构建大脑网络。这篇文章的补充材料可以在网上找到。
Abstract We introduce a statistical graphical model for multivariate functional data, which are common in medical applications such as EEG and fMRI. Recently published functional graphical models rely on the multivariate Gaussian process assumption, but we relax it by introducing the functional copula Gaussian graphical model (FCGGM). This model removes the marginal Gaussian assumption but retains the simplicity of the Gaussian dependence structure, which is particularly attractive for large data. We develop four estimators for the FCGGM and establish the consistency and the convergence rates of one of them. We compare our FCGGM with the existing functional Gaussian graphical model by simulations, and apply our method to an EEG dataset to construct brain networks. Supplementary materials for this article are available online.