Copula Gaussian Graphical Models for Functional Data

• 成果类型: Article
• 署名作者: Solea, Eftychia; Li, Bing
• 署名单位: University of Cyprus; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI)
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1817750
• 发表日期: 2022
• 页码: 781-793
• 关键词: statistical-analysis; selection; networks

摘要

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.for this article are available online.