報告人簡介
姚琦偉，英國倫敦經濟與政治科學學院統計系教授，英國皇家統計學會會士，美國統計協會會士，數理統計學會會士，國際統計研究學會選舉會員。姚琦偉教授是國際知名的統計學家，一直從事統計學的教學和科研工作，主要研究領域為：時間序列分析、時空過程分析、金融計量經濟學。他在非線性和高維時間序列方面的研究國際領先。姚琦偉教授迄今已發表學術論文80多篇，并獲得EPSRC、BBSRC等英國國家基金會支持的多項研究基金項目。其專著《非線性時間序列：非參數及參數方法》（與范劍青合著）于2003年由Springer出版，《計量金融簡要》（與范劍青合著）于2017年由劍橋出版社出版。姚琦偉教授現任Journal of the Royal Statistical Society 的聯合主編，曾任包括Annals of Statistics，Journal of the American Statistics Association等多個頂級雜志副主編，曾任Statistica Sinica的聯合主編。
內容簡介
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.
