标题： Stein 方法在局部依赖指数随机图模型矩估计量中的应用 显示英文标题

标题： Stein's method of moment estimators for local dependency exponential random graph models

摘要： 在指数随机图模型中提供参数估计的理论保证仍然是一个开放性问题。 虽然最大似然估计在原则上具有理论保证，但验证这些保证成立所需的假设可能非常困难。 此外，在复杂网络中，数值上的最大似然估计计算密集且可能无法在合理的时间内收敛。 为了解决这个问题，引入了局部依赖性指数随机图模型，该模型假设网络由许多独立的指数随机图组成。 在这种情况下，已经取得了一些关于最大似然估计的进展。 然而，这种估计仍然计算密集。 相反，我们建议使用所谓的Stein估计量：利用Stein刻画来为局部依赖性指数随机图模型获得新的估计量。 显示英文摘要

摘要： 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 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, we propose to use so-called Stein estimators: we use the Stein characterizations to obtain new estimators for local dependency exponential random graph models.