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Activity Number:
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266
- Recent Advances in Statistical Network Analysis with Applications
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Type:
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Invited
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Date/Time:
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Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
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Sponsor:
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Section on Statistical Graphics
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Abstract #316645
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Title:
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Mixed-Effect Time-Varying Network Model
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Author(s):
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Emma Jingfei Zhang* and Will Wei Sun and Lexin Li
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Companies:
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University of Miami and Purdue University and University of California, Berkeley
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Keywords:
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brain connectivity analysis;
fused lasso;
generalized linear mixed-effect model;
stochastic blockmodel;
time-varying network
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Abstract:
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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 talk, 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 multi-step 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.
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Authors who are presenting talks have a * after their name.
