• 演讲人： Qiwei Yao（姚琦伟），英国伦敦经济与政治科学学院统计系教授
• 时间：2020年11月16日 星期一 下午16：00（北京时间）
• 地点：腾讯会议 ID：407 489 377
• 主办单位：浙江大学数据科学研究中心

摘要：We propose a first-order autoregressive model for dynamic network processes in which edges change over time while nodes remain unchanged. The model depicts the dynamic changes explicitly. It also facilitates simple and efficient statistical inference such as the maximum likelihood estimators which are proved to be (uniformly) consistent and asymptotically normal. The model diagnostic checking can be carried out easily using a permutation test. The proposed model can apply to any Erd\"os-Renyi network processes with various underlying structures. As an illustration, an autoregressive stochastic block model has been investigated in depth, which characterizes the latent communities by the transition probabilities over time. This leads to a more effective spectral clustering algorithm for identifying the latent communities. Inference for a change-point is incorporated into the autoregressive stochastic block model to cater for possible structure changes. The developed asymptotic theory as well as the simulation study affirm the performance of the proposed methods. Application with three real data sets illustrates both relevance and usefulness of the proposed models.

(Joint work with Binyan Jiang and Jialiang Li.)

主持人：张荣茂教授 浙江大学数学科学学院

报告人简介：Qiwei Yao（姚琦伟），英国伦敦经济与政治科学学院统计系教授、北京大学光华管理学院特聘教授、香港大学统计与精算学系名誉教授、英国皇家统计学会名誉会士、美国统计协会会士、数理统计学会会士。曾担任了多家知名统计学杂志的副主编和联合编辑（co-editor），其中包括皇家统计学会期刊、统计学年报、时间序列分析期刊等，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。他还是皇家统计学会荣誉会员、国际统计研究所推选成员、美国统计协会、数理统计学会，泛华统计学会会员。