Goodness of fit tests for random multigraph models

Termeh Shafie

Journal of Applied Statistics, 2023, vol. 50, issue 15, 3062-3087

Abstract: Goodness of fit tests for two probabilistic multigraph models are presented. The first model is random stub matching given fixed degrees (RSM) so that edge assignments to vertex pair sites are dependent, and the second is independent edge assignments (IEA) according to a common probability distribution. Tests are performed using goodness of fit measures between the edge multiplicity sequence of an observed multigraph, and the expected one according to a simple or composite hypothesis. Test statistics of Pearson type and of likelihood ratio type are used, and the expected values of the Pearson statistic under the different models are derived. Test performances based on simulations indicate that even for small number of edges, the null distributions of both statistics are well approximated by their asymptotic $ \chi ^{2} $ χ2-distribution. The non-null distributions of the test statistics can be well approximated by proposed adjusted $ \chi ^{2} $ χ2-distributions used for power approximations. The influence of RSM on both test statistics is substantial for small number of edges and implies a shift of their distributions towards smaller values compared to what holds true for the null distributions under IEA. Two applications on social networks are included to illustrate how the tests can guide in the analysis of social structure.

Date: 2023
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DOI: 10.1080/02664763.2022.2099816

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