A0863
Title: Steins method of moment estimators for local dependency exponential random graph models
Authors: Gesine Reinert - Oxford University (United Kingdom) [presenting]
Adrian Fischer - University of Oxford (United Kingdom)
Wenkai Xu - University of Warwick (United Kingdom)
Abstract: Providing theoretical guarantees for parameter estimation in exponential random graph models is a largely open problem. While maximum likelihood estimation has theoretical guarantees in principle, verifying the assumptions for these guarantees to hold can be very difficult. Moreover, in complex networks, numerical maximum likelihood estimation is computer-intensive and may not converge in a reasonable time. To ameliorate this issue, local dependency exponential random graph models have been introduced, which assume that the network consists of many independent exponential random graphs. In this setting, progress towards maximum likelihood estimation has been made. However, the estimation is still computer-intensive. Instead, the proposal is to use so-called Stein estimators: The Stein characterizations are used to obtain new estimators for local dependency exponential random graph models.
