报告人：曲连强
报告地点：腾讯会议ID:243-226-267
报告时间：2022年10月19日星期三10:00-11:00
报告摘要：
We propose a new model for the analysis of network recurrent event data. Our model can flexibly accommodate homophily and degree heterogeneity, and can provide a natural explanation for node popularity. Theoretically, we show that the proposed method can handle the sparse network to some extent. Our method is based on multivariate counting processes, in which each subject is assigned two individual-specific time-varying degree parameters. It results in a high-dimensional estimation problem. We develop new strategies to establish the consistency and asymptotic normality of the estimators obtained by a kernel smoothing method. Simulation studies are provided to assess the finite performance of the proposed method and a real data analysis illustrates its practical utility.
主讲人简介：
曲连强，博士毕业于中科院数学与系统科学研究院，2017年入职华中师范大学，研究方向为生存数据分析及高维数据分析，论文发表在JMLR、JBES等国际知名期刊。
