题目：Autoregressive networks and stylized features

报告人：姚琦伟

报告时间：2025年3月4日 14:00

地点：综合楼644会议室

报告人简介：

       姚琦伟，英国伦敦经济政治经济学院统计系教授，英国皇家统计学会、美国统计协会（ASA）及数理统计学会(IMS)的Fellow，国际统计学会(ISI)的Elected member。姚琦伟教授是国际知名的统计学家，他的研究兴趣包括：时间序列分析、高维时间序列建模与预测、降维和因子建模、动态网络建模、时空建模和金融计量经济学等，已在包括统计学顶刊JASA、AoS、JRSSB和计量经济学顶刊JoE等上发表论文110余篇，并著有2本专著：《非线性时间序列：非参数及参数方法》和《计量金融简要》。担任了Journal of the Royal Statistical Society (Series B)，Statistica Sinica 的联合主编，及Annals of Statistics，Journal of the American Statistics Association等多个顶级杂志副主编。姚琦伟教授还曾为巴克莱银行，法国电力公司以及Winton资本等多家企业提供咨询。

报告摘要：

We give a brief introduction on the autoregressive (AR) model for dynamic network processes. The model depicts the dynamic changes explicitly. It also facilitates simple and efficient statistical inference such as MLEs and a permutation test for model diagnostic checking. We illustrate how this AR model can serve as a building block to accommodate more complex structures such as stochastic latent blocks, change-points. We also elucidate how some stylized features often observed in real network data, including node heterogeneity, edge sparsity, persistence, transitivity and density dependence, can be embedded in the AR framework. Then the framework needs to be extended for dynamic networks with dependent edges, which poses new technical challenges. Illustration with real network data for the practical relevance of the proposed AR framework is also presented.