北京大学统计科学中心与清华大学统计学研究中心于2016年联合发起“北大-清华统计论坛”,两校统计学师生济济一堂,发挥两校的学科优势,互通有无,着力推动中国优秀统计青年人才的成长,助力中国统计学科的发展,论坛已成功举办六届。为传承兄弟院校间的团结协作和友好交流,两校将于2023年5月25日举办第七届北大-清华统计论坛。
报名时间:即日起至2023年5月15日
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张志华,北京大学数学科学学院教授,担任JMLR、CSIAM等多个国际期刊编委,多次担任NeurIPS、ICML、ICLR等国际重要人工智能和机器学习会议领域主席
Topic: Stochastic Optimization, Stochastic Approximation and Statistical Inference
Abstract: 优化在统计中不仅起着计算工具的角色,同时也是方法论的来源。这个报告将讨论随机优化的统计估计和推断问题。特别是,基于随机逼近的框架,讨论机器学习中一些重要方法,比如 Local SGD, Q-Learning等的统计性质。

苏良军,清华大学经济管理学院教授,中国数量经济学会副理事长
Topic: Three-Dimensional Factor Models with Global and Local Factors
Abstract: This paper considers a three-dimensional latent factor model in the presence of one set of global factors and two sets of local factors. We allow the numbers of local factors to vary across individuals and show that the numbers of global and local factors can be estimated uniformly consistently. Given the number of global and local factors, we propose a two-step estimation procedure based on principal component analysis (PCA). Our first step estimates the global factors and their factor loadings, after which we estimate the two sets of local factors and factor loadings sequentially. Our second step improves the estimation efficiency. The asymptotic theories for our estimators are established. Monte Carlo simulations demonstrate that they perform well in finite samples. Applications to two datasets in international trade and economic growth reveal the relative importance of different types of factors. In the international trade application, we find that the global factors, source country factors, and destination country factors are all important. In the industrial growth application, there is no global factor and the country factors are far more important than the industry factors. The extension to the 3D factor model with covariates is also studied.