嘉宾简介：

王学钦，中国科学技术大学讲席教授，2003年毕业于宾汉姆顿大学，教育部高层次人才入选者，国际统计学会(ISI)当选会员。现担任教育部高等学校统计学类专业教学指导委员会委员、中国现场统计研究会副理事长、中国现场统计研究会教育统计与管理分会理事长主要研究方向为大规模复杂数据的统计学理论、方法与算法；统计机器学习；精准医疗；医疗政策；风险管理和政策评估。

讲座简介：

In addressing the challenge of distinguishing global from group-specific factors within the group factor model, especially when faced with strongly correlated local factors, this paper proposes a novel objective function. This function is designed to optimize the identification process by maximizing the sum of correlations between global and group factors, utilizing eigen-decomposition of the aggregated projection matrix that these factors form. Our method is not only computationally efficient but also robust against the distortions caused by closely correlated local factors. We establish a solid theoretical foundation for our approach, demonstrating the consistency of our global factor number estimation, and the consistency and asymptotic distributions of the estimated global/local factors and their loadings. Through extensive simulation studies, we prove our method's superior performance over existing approaches across various scenarios. Applied to the analysis of the growth rates of house prices in the United States, our method effectively identifies a primary global factor significantly influencing national house price trends, along with several local factors that highlight unique state-level impacts. This application not only showcases the practical utility of our approach but also deepens our understanding of economic dynamics through factor analysis.