报告人：马宗明
报告地点：综合楼324教室
报告时间：2019年07月24日星期三09:30-10:30
邀请人：郑术蓉
报告摘要：
Latent space models are effective tools for statistical modeling and visualization of network data. Due to their close connection to generalized linear models, it is also natural to incorporate covariate information in them. The talk presents two universal fitting algorithms for networks with edge covariates: one based on nuclear norm penalization and the other based on projected gradient descent. Both algorithms are motivated by maximizing the likelihood function for an existing class of inner-product models, and we establish their statistical rates of convergence for these models. In addition, the theory informs us that both methods work simultaneously for a wide range of different latent space models that allow latent positions to affect edge formation in flexible ways, such as distance models. Furthermore, the effectiveness of the methods is demonstrated on a number of real world network datasets for different statistical tasks.
主讲人简介：
马宗明博士于2010年在斯坦福大学统计系获得博士学位。此后在宾夕法尼亚大学沃顿商学院统计系任教，目前担任副教授。马宗明的主要研究兴趣包括高维与非参数统计推断，以及网络数据分析与应用。马宗明曾获得美国自然科学基金的Career Award和一项Sloan Fellowship。
