Exponential Random Graph Models (ERGMs) have been developed for fitting social network
data on both static and dynamic levels. However, the lack of large sample asymptotic
properties makes it inadequate in assessing the goodness-of-fit of these ERGMs.
Simulation-based goodness-of-fit plots were proposed by Hunter et al (2006), comparing
the structured statistics of observed network with those of corresponding simulated
networks. In this research, we propose an improved approach to assess the goodness of fit of
ERGMs. Our method is shown to improve the existing graphical techniques. We also propose a simulation based test
statistic with which the model comparison can be easily achieved. / Biostatistics
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:AEU.10048/1593 |
| Date | 11 1900 |
| Creators | Li, Yin |
| Contributors | Keumhee Carriere Chough (Mathematical and Statistical Sciences), Peter Hooper (Mathematical and Statistical Sciences), Sentil Senthilselvan (Public Health) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 551079 bytes, application/pdf |
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