|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 listsAAAS members and friends event FERSA Kaleidoscope Conference de239's list
Other talksMeCP2 in the brain and beyond: from biology to disease 'High Frequency Measurements of Carbon Nanotube Double-Quantum Dots Hydrogen–deuterium exchange mass spectrometry Surface Charge Doping By Polymer Electrolyte Gating In Metals And 2d Systems: A Route To Synthetic Superconductivity Impingement of a plume on a rigid boundary Stem cell dynamics in the pancreas