Dynamic Networks with Multi-scale Temporal Structure

Xinyu Kang, Apratim Ganguly and Eric D. Kolaczyk ()
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Xinyu Kang: Boston University
Apratim Ganguly: Boston University
Eric D. Kolaczyk: Boston University

Sankhya A: The Indian Journal of Statistics, 2022, vol. 84, issue 1, No 7, 218-260

Abstract: Abstract We describe a novel method for modeling non-stationary multivariate time series, with time-varying conditional dependencies represented through dynamic networks. Our proposed approach combines traditional multi-scale modeling and network based neighborhood selection, aiming at capturing temporally local structure in the data while maintaining sparsity of the potential interactions. Our multi-scale framework is based on recursive dyadic partitioning, which recursively partitions the temporal axis into finer intervals and allows us to detect local network structural changes at varying temporal resolutions. The dynamic neighborhood selection is achieved through penalized likelihood estimation, where the penalty seeks to limit the number of neighbors used to model the data. We present theoretical and numerical results describing the performance of our method, which is motivated and illustrated using task-based magnetoencephalography (MEG) data in neuroscience.

Keywords: Dynamic network; multiscale modeling; vector autoregressive model.; Primary: 62M10; Secondary: 05C82; 62P10 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13171-021-00256-1

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