统计与数据科学系系列学术报告之四百三十期

时    间：2024年5月15日（周三）16:00-17:00

地    点：史带楼401室

主持人：复旦大学 管理学院 统计与数据科学系 张新生 教授

报告人：常晋源 教授  西南财经大学

题    目：Autoregressive networks with dependent edges

摘    要：We propose an autoregressive framework for modelling dynamic networks with dependent edges. It encompasses the models which accommodate, for example, transitivity, density-dependent and other stylized features often observed in real network data. By assuming the edges of network at each time are independent conditionally on their lagged values, the models, which exhibit a close connection with temporal ERGMs, facilitate both simulation and the maximum likelihood estimation (MLE) in the straightforward manner. Due to the possible large number of parameters in the models, the initial MLEs may suffer from slow convergence rates. An improved estimator for each component parameter is proposed based on an iteration based on the projection which mitigates the impact of the other parameters (Chang et al., 2021; Chang, Shi and Zhang, 2023). Based on a martingale difference structure, the asymptotic distribution of the improved estimator is derived without the stationarity assumption. The limiting distribution of the estimator is not normal in general, and it reduces to normal when the underlying process satisfies some mixing conditions. Illustration with a transitivity model was carried out in both simulation and with two real network data sets.

个人简介：常晋源，西南财经大学光华特聘教授、中科院数学与系统科学研究院研究员，国家杰出青年科学基金获得者，主要从事“超高维数据分析”和“高频金融数据分析”相关的研究工作，正担任Journal of the American Statistical Association, Journal of Business & Economic Statistics以及Statistica Sinica的Associate Editor。

统计与数据科学系

2024-5-7