Journal article
Estimating the correlation in network disturbance models
- Abstract:
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The network disturbance model of P. Doreian (1989), expresses the dependency between observations taken at the vertices of a network by modelling the correlation between neighbouring vertices, using a single correlation parameter p. It has been observed that estimation of p in dense graphs, using the method of maximum likelihood, leads to results that can be both biased and very unstable. In this article, we sketch why this is the case, showing that the variability cannot be avoided, no matter how large the network. We also propose a more intuitive estimator of p, which shows little bias. The related network effects model is briefly discussed.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 411.0KB, Terms of use)
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- Publisher copy:
- 10.1093/comnet/cnab028
Authors
- Grant:
- EP/R018472/1
- EP/T018445/1
- Publisher:
- Oxford University Press
- Journal:
- Journal of Complex Networks More from this journal
- Volume:
- 9
- Issue:
- 5
- Article number:
- cnab028
- Publication date:
- 2021-09-18
- Acceptance date:
- 2021-08-24
- DOI:
- EISSN:
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2051-1329
- ISSN:
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2051-1310
- Keywords:
- Pubs id:
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1192207
- Local pid:
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pubs:1192207
- Deposit date:
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2022-11-25
Terms of use
- Copyright holder:
- Barbour and Reinert
- Copyright date:
- 2021
- Rights statement:
- © The Author(s) 2021. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
- Licence:
- CC Attribution (CC BY)
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