Academic Report of School of Mathematical Sciences [2026] No. 060

(Series Report for High-Level University Construction No. 1319)

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

Speaker：Yicheng Zeng, Associate Professor (Sun Yat-sen University)

Time：10:00-11:00, June 23, 2026

Location：Room 304, Alumni Plaza, Yuehai Campus

Abstract: 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.

Speaker Profile：Dr. Yicheng Zeng is currently an associate professor and doctoral advisor in the School of Science at Sun Yat-sen University. Dr. Zeng earned his master’s degree from the School of Mathematics at Zhejiang University and his Ph.D. from the Department of Mathematics at Hong Kong Baptist University, followed by postdoctoral research in the Department of Statistics at the University of Toronto in Canada. His primary research interests include high-dimensional statistics, random matrix theory, and their applications in high-dimensional statistics and machine learning. His research findings have been published in statistical journals such as Statistica Sinica, Bioinformatics, JMVA, and CSDA, as well as at machine learning conferences including ICML and WWW. Dr. Zeng has led one Young Scientist Project funded by the National Natural Science Foundation of China and one Shenzhen Outstanding Science and Technology Innovation Talent Cultivation Project.

Faculty and students are welcome to attend!

Invited by: Xianghong Hu

School of Mathematical Sciences

June 15, 2026