Goodness of fit tests for random multigraph models

Shafie, Termeh

Goodness of fit tests for random multigraph models

Dateien

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Datum

2023

Open Access-Veröffentlichung

Open Access Hybrid

Sammlungen

Politik- und Verwaltungswissenschaft

Publikationstyp

Zeitschriftenartikel

Publikationsstatus

Published

Erschienen in

Journal of Applied Statistics. Taylor & Francis. 2023, 50(15), pp. 3062-3087. ISSN 0266-4763. eISSN 1360-0532. Available under: doi: 10.1080/02664763.2022.2099816

Zusammenfassung

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 χ2-distribution. The non-null distributions of the test statistics can be well approximated by proposed adjusted 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.

Fachgebiet (DDC)

310 Statistik

Schlagwörter

Network model, multivariate networks, data aggregation, random multigraphs, goodness of fit, random stub matching

Zitieren

ISO 690

SHAFIE, Termeh, 2023. Goodness of fit tests for random multigraph models. In: Journal of Applied Statistics. Taylor & Francis. 2023, 50(15), pp. 3062-3087. ISSN 0266-4763. eISSN 1360-0532. Available under: doi: 10.1080/02664763.2022.2099816

BibTex

@article{Shafie2023Goodn-66689,
  year={2023},
  doi={10.1080/02664763.2022.2099816},
  title={Goodness of fit tests for random multigraph models},
  number={15},
  volume={50},
  issn={0266-4763},
  journal={Journal of Applied Statistics},
  pages={3062--3087},
  author={Shafie, Termeh}
}

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