Mixed-Effect Time-Varying Network Model and Application in Brain Connectivity Analysis

• 成果类型: Article
• 署名作者: Zhang, Jingfei; Sun, Will Wei; Li, Lexin
• 署名单位: University of Miami; Purdue University System; Purdue University; University of California System; University of California Berkeley
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1677242
• 发表日期: 2020
• 页码: 2022-2036
• 关键词: mixture model; risk bounds; covariance; likelihood; selection

摘要

Time-varying networks are fast emerging in a wide range of scientific and business applications. Most existing dynamic network models are limited to a single-subject and discrete-time setting. In this article, we propose a mixed-effect network model that characterizes the continuous time-varying behavior of the network at the population level, meanwhile taking into account both the individual subject variability as well as the prior module information. We develop a multistep optimization procedure for a constrained likelihood estimation and derive the associated asymptotic properties. We demonstrate the effectiveness of our method through both simulations and an application to a study of brain development in youth. for this article are available online.