ORCID: https://orcid.org/0000-0001-6324-4713 and Kauermann, Göran
(2021):
Iterative estimation of mixed exponential random graph models with nodal random effects.
In: Network Science, Vol. 9, No. 4: pp. 478-498
Abstract
The presence of unobserved node-specific heterogeneity in exponential random graph models (ERGM) is a general concern, both with respect to model validity as well as estimation instability. We, therefore, include node-specific random effects in the ERGM that account for unobserved heterogeneity in the network. This leads to a mixed model with parametric as well as random coefficients, labelled as mixed ERGM. Estimation is carried out by iterating between approximate pseudolikelihood estimation for the random effects and maximum likelihood estimation for the remaining parameters in the model. This approach provides a stable algorithm, which allows to fit nodal heterogeneity effects even for large scale networks. We also propose model selection based on the Akaike Information Criterion to check for node-specific heterogeneity.
Item Type: | Journal article |
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Faculties: | Mathematics, Computer Science and Statistics > Statistics |
Subjects: | 300 Social sciences > 310 Statistics |
ISSN: | 2050-1242 |
Language: | English |
Item ID: | 94855 |
Date Deposited: | 03. Mar 2023, 07:21 |
Last Modified: | 03. Mar 2023, 07:21 |