Journal article
A Bayesian mixture model for Poisson network autoregression
- Abstract:
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Multivariate count time series arise in a wide range of applications, including the number of COVID-19 cases recorded each week in different counties of the Republic of Ireland. In this example, it is natural to view the counties as nodes in a network, with edges between counties reflecting proximity. One could then model disease spread on a network through a regression model. Often Gaussian errors are assumed for such a model, but for count data this assumption may not be natural. With this motivating example in mind, we develop a model with the following features. We assume that the time series occur on the nodes of a known underlying network where the edges dictate the form of a structural vector autoregression model. In contrast to using a full vector autoregressive model, the network assumption is a means of imposing sparsity. Moreover we aim for a model that is able to accommodate heterogeneous node dynamics, and to cluster nodes that exhibit similar behaviour. To address these aims, we propose a new Bayesian Poisson network autoregression mixture model that we call a PNARM model, which combines ideas from Poisson network autoregression models, grouped network autoregression models, and non-uniform co-clustering priors.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 3.5MB, Terms of use)
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- Publisher copy:
- 10.1007/s13278-025-01485-0
Authors
- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- MATH_PA0901 / PO 4549016
- EP/T018445/1
- Publisher:
- Springer
- Journal:
- Social Network Analysis and Mining More from this journal
- Volume:
- 15
- Issue:
- 1
- Article number:
- 70
- Publication date:
- 2025-07-17
- Acceptance date:
- 2025-06-13
- DOI:
- EISSN:
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1869-5469
- ISSN:
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1869-5450
- Language:
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English
- Keywords:
- Pubs id:
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2130509
- Local pid:
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pubs:2130509
- Deposit date:
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2025-06-17
- ARK identifier:
Terms of use
- Copyright holder:
- Hung et al.
- Copyright date:
- 2025
- Rights statement:
- Copyright © 2025, The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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