Testing for global covariate effects in dynamic interaction event networks
In statistical network analysis it is common to observe so called interaction data. Such data is characterized by actors forming the vertices and interacting along edges of the network, where edges are randomly formed and dissolved over the observation horizon. In addition, covariates are observed a...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2024
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| In: |
Journal of business & economic statistics
Year: 2024, Jahrgang: 42, Heft: 2, Pages: 457-468 |
| ISSN: | 1537-2707 |
| DOI: | 10.1080/07350015.2023.2263537 |
| Schlagworte: | |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://www.tandfonline.com/doi/full/10.1080/07350015.2023.2263537 Resolving-System, lizenzpflichtig, Volltext: https://doi.org/10.1080/07350015.2023.2263537 Verlag, lizenzpflichtig: https://www.tandfonline.com/doi/pdf/10.1080/07350015.2023.2263537 
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| Verfasserangaben: | Alexander Kreiss, Enno Mammen, and Wolfgang Polonik |
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| 520 | |a In statistical network analysis it is common to observe so called interaction data. Such data is characterized by actors forming the vertices and interacting along edges of the network, where edges are randomly formed and dissolved over the observation horizon. In addition, covariates are observed and the goal is to model the impact of the covariates on the interactions. We distinguish two types of covariates: global, system-wide covariates (i.e., covariates taking the same value for all individuals, such as seasonality) and local, dyadic covariates modeling interactions between two individuals in the network. Existing continuous time network models are extended to allow for comparing a completely parametric model and a model that is parametric only in the local covariates but has a global nonparametric time component. This allows, for instance, to test whether global time dynamics can be explained by simple global covariates like weather, seasonality etc. The procedure is applied to a bike-sharing network by using weather and weekdays as global covariates and distances between the bike stations as local covariates. | ||
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