|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
Modelling Network Data
If you have a question about this talk, please contact Richard Samworth.
Networks are fast becoming a primary object of interest in statistical data analysis, with important applications spanning the social, biological, and information sciences. A common aim across these fields is to test for and explain the presence of structure in network data. In this talk we show how characterizing the structural features of a network corresponds to estimating the parameters of various random network models, allowing us to obtain new results for likelihood-based inference and uncertainty quantification in this context. We discuss asymptotics for stochastic blockmodels with growing numbers of classes, the determination of confidence sets for network structure, and a more general point process modeling for network data taking the form of repeated interactions between senders and receivers, where we show consistency and asymptotic normality of partial-likelihood-based estimators related to the Cox proportional hazards model (arXiv:1201.5871, 1105.6245, 1011.4644, 1011.1703).
This talk is part of the Statistics series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsViolence Research Centre Engineering Department Geotechnical Research Seminars
Other talksHalocarbons in Nanomedicine and Nanotechnology: New opportunities and challenges Carving Romanesque Chiaroscuro Barred from work in Scottish prisons, data science to the rescue: discoveries about drugs-related deaths and women via record linkage - A part of Women in Data Science (WiDS) Writing Sound : Designing Notation : Carolingian Musical Techne On anonymisation and discrimination A lab of one’s own: science & suffrage in the First World War