Name: Yu Long
Title: Associate Professor
Research Areas: Factor models, high-dimensional data analysis, random matrix theory
Courses Taught: Undergraduate course "Statistics", Master's and Doctoral course "Statistical Learning", Professional Master's course
E - mail: yulong@mail.shufe.edu.cn Phone: 021-65901469
Research Project
Serial Number | Project Name | Project Number | Project Source | Start and End Time | Project Funding |
1 | Research on Several Nonlinear High-Dimensional Matrix-Valued Factor Models | 12301350 | National Natural Science Foundation Youth Project | 2024-2026 | 300,000 yuan |
2 | Factor modeling methods for high-dimensional tensor data and their applications in fields such as finance and economics | 23PJ1402700 | Shanghai Pujiang Talent Program Category A | 2023-2025 | 300,000 yuan |
Education Background
2015.9-2020.6 Fudan University School of Management Department of Statistics PhD
2018.9-2019.8 University of Michigan, Ann Arbor Department of Statistics Joint Training
2011.9-2015.6 Fudan University School of Management Department of Statistics Bachelor's Degree
Work Experience
2024.7-Present Shanghai University of Finance and Economics School of Statistics and Management Associate Professor
2022.9-2024.6 Shanghai University of Finance and Economics, School of Statistics and Management, Assistant Professor
2020.9-2022.9 Postdoctoral Researcher, Department of Data Science and Statistics, National University of Singapore
Academic Part-time Jobs
Served as a member of the Early Career Advisory Board for the Journal of Multivariate Analysis.
https://www.sciencedirect.com/journal/journal-of-multivariate-analysis/about/editorial-board
China Statistical Research Society Multivariate Analysis Application Professional Committee Director
China Statistical Society Random Matrix Theory and Applications Branch Director
[1].Yu, L., Zhao, P., & Zhou, W. (2025). Testing the number of common factors by bootstrapped sample covariance matrix in high-dimensional factor models. Journal of the American Statistical Association, 120(549), 448-459.https://doi.org/10.1080/01621459.2024.2346364
[2].Wang, Y., & Yu, L. (2025). Robust factorization for high-dimensional matrix-variate observations. Journal of Multivariate Analysis, 105467.https://doi.org/10.1016/j.jmva.2025.105467
[3].He, Y., Wang, Y., Yu, L., Zhou, W., & Zhou, W. X. (2025). A new non-parametric Kendall’s tau for matrix-valued elliptical observations. Bernoulli, 31(4), 3331-3355.DOI: 10.3150/24-BEJ1849
[4].He, Y., Kong, X., Trapani, L., & Yu, L. (2024). Online change-point detection for matrix-valued time series with latent two-way factor structure. The Annals of Statistics, 52(4), 1646-1670.DOI: 10.1214/24-AOS2410
[5].He, Y., Kong, X., Yu, L., Zhang, X., & Zhao, C. (2024). Matrix factor analysis: From least squares to iterative projection. Journal of Business & Economic Statistics, 42(1), 322-334.https://doi.org/10.1080/07350015.2023.2191676
[6].Yu L., Xie J., Zhou W. (2023). Testing Kronecker Product Covariance Matrices for High-Dimensional Matrix-Variate Data. Biometrika, 110(3), 799-814.doi: 10.1093/biomet/asac063.
[7].He Y., Kong X., Trapani L., & Yu L. (2023). One-way or Two-way Factor Model for Matrix Sequences? Journal of Econometrics, 235(2), 1981-2004.https://doi.org/10.1016/j.jeconom.2023.02.008
[8].Yu, L., He, Y., Kong, X., & Zhang, X. (2022). Projected estimation for large-dimensional matrix factor models. Journal of Econometrics, 229(1), 201-217.https://doi.org/10.1016/j.jeconom.2021.04.001
[9].He, Y., Kong, X., Yu, L, & Zhang, X. (2022). Large-dimensional factor analysis without moment constraints. Journal of Business & Economic Statistics, 40(1), 302-312.https://doi.org/10.1080/07350015.2020.1811101
[10].Wen, J., Xie J., Yu L, & Zhou, W. (2022). Tracy-Widom limit for the largest eigenvalue of high-dimensional covariance matrices in elliptical distributions. Bernoulli, 28(4), 2941-2967.DOI: 10.3150/21-BEJ1443
[11].Chen H., Guo Y., He Y., Ji J., Liu L., Shi Y., Wang Y., Yu L, Zhang X. (2022). Simultaneous Differential Network Analysis and Classification for Matrix-variate Data with Application to Brain Connectivity. Biostatistics, 23(3), 967–989.https://doi.org/10.1093/biostatistics/kxab007
[12].Zhao, B., Yu, L., Wang, C., Shuai, C., Zhu, J., Qu, S., Taiebat M, & Xu, M. (2021). Urban Air Pollution Mapping Using Fleet Vehicles as Mobile Monitors and Machin Learning. Environmental Science & Technology, 2021 55(8), 5579-5588.https://doi.org/10.1021/acs.est.0c08034
[13].Yu, L., He, Y., & Zhang, X. (2019). Robust factor number specification for large-dimensional elliptical factor model. Journal of Multivariate analysis, 174, 104543.https://doi.org/10.1016/j.jmva.2019.104543
Preprint:
[1].Ding X., Xie J., Yu L., Zhou W. (2025+). Multiplier bootstrap meets high-dimensional PCA: the good, the bad and the modification. https://xcding1212.github.io/m1.pdf
[2].Kong, X., Liu, Y., Yu, L., & Zhao, P. (2025+). Matrix Quantile Factor Model. arXiv:2208.08693
