讲座时间：2025年11月12日(周三)  15：00

地点： 综合楼644会议室

报告题目：High-Dimensional Spatial Autoregression with Latent Factors by Diversified Projections

报告人简介：  

王汉生，北京大学光华管理色情网址大全商务统计与经济计量系，教授，博导。国家杰出青年基金获得者，教育部长江学者特聘教授，美国数理统计协会（IMS）Fellow，美国统计学会（ASA）Fellow，国际统计协会（ISI）Elected Member。先后历任10个国际学术期刊副主编（Associate Editor / Editor）。国内外各种专业杂志上发表文章180+篇，并合著有英文专著共1本，（合）著中文教材4本。爱思唯尔中国高被引学者学者，斯坦福全球2%顶尖科学家。

报告摘要： 

We study one particular type of multivariate spatial autoregression (MSAR) model with diverging dimensions in both responses and covariates. This makes the usual MSAR models no longer applicable due to the high computational cost. To address this issue, we propose a factor-augmented spatial autoregression (FSAR) model. FSAR is a special case of MSAR but with a novel factor structure imposed on the high-dimensional  random error vector. The latent factors of FSAR are assumed to be of a fixed dimension. Therefore, they can be estimated consistently by the diversified projections method \citep{fan2022learning}, as long as the dimension of the multivariate response is diverging. Once the fixed-dimensional latent factors are consistently estimated, they are then fed back into the original SAR model and serve as exogenous covariates. This leads to a novel FSAR model. Thereafter, different components of the high-dimensional response can be modeled separately. To handle the high-dimensional feature, a smoothly clipped absolute deviation (SCAD) type penalized estimator is developed for each response component. We show theoretically that the resulting SCAD estimator is uniformly selection consistent, as long as the tuning parameter is selected appropriately. For practical selection of the tuning parameter, a novel BIC method is developed. Extensive numerical studies are conducted to demonstrate the finite sample performance of the proposed method.