报告人简介
姚琦伟，英国伦敦经济与政治科学学院统计系教授，英国皇家统计学会会士，美国统计协会会士，数理统计学会会士，国际统计研究学会选举会员。姚琦伟教授是国际知名的统计学家，一直从事统计学的教学和科研工作，主要研究领域为：时间序列分析、时空过程分析、金融计量经济学。他在非线性和高维时间序列方面的研究国际领先。姚琦伟教授迄今已发表学术论文80多篇，并获得EPSRC、BBSRC等英国国家基金会支持的多项研究基金项目。其专著《非线性时间序列：非参数及参数方法》（与范剑青合著）于2003年由Springer出版，《计量金融简要》（与范剑青合著）于2017年由剑桥出版社出版。姚琦伟教授现任Journal of the Royal Statistical Society 的联合主编，曾任包括Annals of Statistics，Journal of the American Statistics Association等多个顶级杂志副主编，曾任Statistica Sinica的联合主编。
内容简介
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.
