题目：Missingness-Adaptive Factor Identification in High-Dimensional Data

报告人：曾奕程

报告时间：2026年4月23日 10:00

地点：综合楼644会议室

报告人简介：

曾奕程博士现为中山大学理学院副教授、博士生导师。曾博士于浙江大学数学学院获得硕士学位，于香港浸会大学数学系获得博士学位，随后在加拿大多伦多大学统计系从事博士后研究。他的主要研究方向包括高维统计、随机矩阵理论及其在高维统计与机器学习中的应用。研究成果发表在Statistica Sinica、Bioinformatics、JMVA、CSDA等统计学期刊，以及ICML、NeurIPS等机器学习会议上。曾博士主持国家自然科学基金青年项目和深圳市优秀科技创新人才培养项目各一项。

报告摘要：

Determining the number of factors in high-dimensional factor models remains a fundamental challenge, particularly when data are incomplete. This paper introduces the concept of identifiable factors—those that can be reliably recovered despite missing observations—and proposes the Missingness-Adaptive Thresholding Estimator (MATE). To our knowledge, MATE is the first missingness-adaptive framework for factor number determination that accommodates both homogeneous and heterogeneous missingness without imposing restrictive assumptions on factor strength. Notably, it operates without data imputation, circumventing the computational burden associated with most existing approaches. We establish a rigorous theoretical foundation for MATE, proving its consistency under a range of structural conditions. Extensive simulations and real-world applications demonstrate that MATE consistently outperforms state-of-the-art methods, exhibiting superior robustness in settings with high missingness rates and weak factor signals.