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

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  • Jingfei Zhang
  • Will Wei Sun
  • Lexin Li

Abstract

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. Supplementary materials for this article are available online.

Suggested Citation

  • Jingfei Zhang & Will Wei Sun & Lexin Li, 2020. "Mixed-Effect Time-Varying Network Model and Application in Brain Connectivity Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 2022-2036, December.
    • Handle: RePEc:taf:jnlasa:v:115:y:2020:i:532:p:2022-2036
    DOI: 10.1080/01621459.2019.1677242
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    Cited by:

    1. Fan, Xinyan & Fang, Kuangnan & Pu, Dan & Qin, Ruixuan, 2024. "Generalized latent space model for one-mode networks with awareness of two-mode networks," Computational Statistics & Data Analysis, Elsevier, vol. 193(C).

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