【专家简介】：桑培俊, 加拿大滑铁卢大学统计与精算系担任副教授。在Annals of Statistics, Biometrika, Biometrics, 等统计杂志发表过多篇文章。主要研究方向是函数型数据和正样本和无标记数据的半监督学习（PU Learning），尤其是函数型数据分析回归模型中的统计推断问题以及在线学习问题，和PU Learning的半参模型。
【报告摘要】：We propose a functional stochastic block model whose vertices involve functional data information. This new model extends the classic stochastic block model with vector-valued nodal information, and finds applications in real-world networks whose nodal information could be functional curves. Examples include international trade data in which a network vertex (country) is associated with the annual or quarterly GDP over a certain time period, and MyFitnessPal data in which a network vertex (MyFitnessPal user) is associated with daily calorie information measured over a certain time period. Two statistical tasks will be jointly executed. First, we will detect community structures of the network vertices assisted by the functional nodal information. Second, we propose a computationally efficient variational testto examine the significance of the functional nodal information. We show that the community detection algorithms achieve weak and strong consistency, and the variational test is asymptotically chi-square with diverging degrees of freedom. As a byproduct, we propose pointwise confidence intervals for the slope function of the functional nodal information. Our methods are examined through both simulated and real datasets.
【报告时间】：2025年07月09日10:00-10:40
【报告地点】：崇真楼110
