学术报告
Autoregressive Networks - 蒋滨雁（香港理工大学）
“2021首师大青年统计论坛”系列报告
题目：Autoregressive Networks
报告人：蒋滨雁（香港理工大学）
时间：2021年4月29日周四晚上20:00-21:00
地点：线上腾讯会议（会议号：709 455 5647）
Abstract : 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 network processes with various underlying structures but with independent edges. 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 affirms the performance of the proposed methods. Application with three real data sets illustrates both relevance and usefulness of the proposed models.
报告人简介：蒋滨雁博士于2007 年获中国科学技术大学统计学学士学位，2012 年获新加坡国立大学统计与应用概率学博士学位。博士毕业后在美国卡内基梅隆大学从事博士后工作。2015 年8 月份加入香港理工大学，于应用数学系担任助理教授。其主要研究领域是统计学，近期的研究课题包括高维复杂数据的预测模型，精准医学相关的统计方法，以及网络数据分析等。
联系人：周洁、胡涛
举办单位：77779193永利官网统计系 、北京应用统计学会、 交叉科学研究院
