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Yong He (何勇)

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Professor,
Institute for Financial Studies,
Shandong University.
No. 27 Shanda South Road,
Jinan City, 250100, China
Phone: (+86) 0531-883-63299
E-mail: heyongAT(@)sdu.edu.cn


Website in Chinese


About me

I currently work as a professor at Institute for Financial Studies, Shandong University. My research interest includes financial econometrics, high-dimensional statistical inference, statistical machine learning and biostatistics. I received my PhD from the Fudan University in 2017 (advised by Xinsheng Zhang) and my Bachelor's degree in mathematics from Shandong University in 2012.

Education Background

Ph.D, Fudan University, 06.2017

B.Sc, Shandong University, 06.2012

Visiting Experience

University of Wisconsin Madison, 09.2015-08.2016

National University of Singapore, 07.2019-08.2019

Research Interest

My research interests include:

  • Financial Econometrics: Factor model; Quantile analysis; Risk Management; Portfolio Allocation; Empirical Finance.

  • High-dimensional Statistical Inference: Matrix-valued data and High-order Tensor data; Dimension Reduction (PCA); Graphical Model; Large-scale multiple testing.

  • Machine (Statistical) Learning: Transfer Learning; Federated Learning; Supervised/Unsupervised Learning (Clustering); Regularization Method; Distributed Statistical Inference; Online updating/detection.

  • Biostatistics: Multi-omics data; functional Magnetic Resonance Imaging data (fMRI).

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Preprints(Please cite our papers if you use the developed packages, thanks.)

  1. He Y, Li L†, Liu D†, Zhou W. Huber Principal Component Analysis for Large-dimensional Factor Model. Journal of Econometrics , major revision required. [arXiv:2303.02817][R package]

  2. He Y, Wang Y†, Yu L, Zhou W, Zhou W. A new non-parametric Kendall’s tau for matrix-valued elliptical observations. (Former title: Matrix Kendall's tau in High-dimensions: A Tail Robust Statistic for Matrix Factor Model.) Bernoulli, major revision required. [arXiv:2207.09633][R package]

  3. Chen H†, Guo Y, He Y*, Liu D, Liu L, Yin Y†, Zhou X. Cooperative Differential Network Learning with Hubs for Multi-center fMRI data. Annals of Applied Statistics, major revision required. [arXiv:2106.03334]

  4. He Y, Liu D†, Pan G, Wang Y†. Penalized Principal Component Analysis for Large-dimension Factor Model with Group Pursuit [arXiv:2407.19378]

  5. He Y, Liu D†, Wang F†, Zhang M, Zhou W. Subgroup Identification with Latent Factor Structure. [arXiv:2407.00882][R package]

  6. He Y, Kong X, Liu D†, Zhao R†. Winsorized Mean Matrix Factor Model.[arXiv:2306.03317][R package]

  7. He Y, Zhao R†, Zhou W. Iterative Alternating Least Square Estimation for Large-dimensional Matrix Factor Model. [arXiv:2301.00360][R package]

  8. He Y, Ma X†, Wang X†, Wang Y†. Large-dimensional Robust Factor Analysis with Group Structure. [arXiv:2112.13479]

  9. Chen H†, He Y, Huang W†, Liu L. Transfer Learning in High-dimensional Differential Network Analysis with Application to Brain Connectivity Analysis. Submitted

  10. Barigozzi M, He Y*, Li L†, Trapani L. Robust Tensor Factor Analysis: Inference on large dimensional, tensor-valued time series in the presence of heavy tails.[arXiv:2303.18163]

  11. Barigozzi M, He Y*, Li L†, Trapani L. Statistical Inference for Large-dimensional Tensor Factor Model by Iterative Projection. [arXiv:2206.09800]

  12. Li Z, Qin K†, He Y*, Zhou W, Zhang X. Knowledge Transfer across Multiple Principal Component Analysis Studies. [arXiv:2207.09633]

  13. He Y, Kong X, Yu L, Zhao P. Quantile factor analysis for large-dimensional time series with statistical guarantee. [arXiv:2006.08214]

  14. Yu L, He Y, Zhang X, Zhu J. Network-Assisted Estimation for Large-dimensional Factor Model with Guaranteed Convergence Rate Improvement. [arXiv:2001.10955]

  15. Li Z, Liu D†, He Y*, Zhang X. Simultaneous Estimation and Dataset Selection for Transfer Learning in High Dimensions by a Non-convex Penalty. [arXiv:2306.04182][R package]

Selected publications

  1. 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, 1646-1670. [arXiv:2112.13479][Manuscript & Supplement][R package]

  2. He Y, Liu Z†, Wang Y† (2024+). Distributed Learning for Principal Eigenspaces without Moment Constraints. Journal of Computational and Graphical Statistics, in press. [arXiv:2204.14049]

  3. Li Z, He Y*, Kong X, Zhang X (2024+). Robust Two-way Dimension Reduction by Grassmannian Barycenter. Journal of Multivariate Analysis, accepted, [arXiv:2203.14063]

  4. Qiao S, He Y, Zhou W (2024). Transfer Learning for High-dimensional Quantile Regression with Statistical Guarantee. Transactions on Machine Learning Research. [Preprint]

  5. He Y, Kong X, Yu L, Zhang X, Zhao C† (2024). Matrix Factor Analysis: From Least Squares to Iterative Projection. Journal of Business and Economic Statistics, 42, 322-334. [arXiv:2112.04186]

  6. He Y, Kong X, Trapani L, Yu L (2023). One-way or Two-way Factor Model for Matrix Sequences? Journal of Econometrics, 235, 1981-2004. [arXiv:2110.01008] [Manuscript & Supplement]

  7. Yu L, He Y, Kong X, Zhang X (2022). Projected Estimation Method for Large-dimensional Matrix Factor Models. Journal of Econometrics, 229, 201-217.

  8. Liu D†, Zhao C†, He Y*, Guo Y, Liu L, Zhang X (2023). Simultaneous Cluster Structure Learning and Estimation of Heterogeneous Graphs for Matrix-variate fMRI Data. Biometrics, 79, 2246-2259. [arXiv:2110.04516][code]

  9. 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, 967-989. [code]

  10. He Y, Li Q†, Hu Q, Liu L (2022). Transfer Learning in High-dimensional Semi-parametric Graphical Models with Application to Brain Connectivity Analysis. Statistics in Medicine, 41, 4112-4129. [arXiv:2112.13356][code]

  11. He Y, Kong X, Yu L, Zhang X (2022). Large-dimensional Factor Analysis without Moment Constraints. Journal of Business and Economic Statistics, 40, 302-312. [code]

  12. Ji J, He Y*, Liu L, Xie L (2021). Brain Connectivity Alteration Detection via Matrix-variate Differential Network Model, Biometrics, 77, 1409-1421. ( Featured as the Cover Image of the issue ).[code]

  13. He Y, Liu P, Zhang X, Zhou W (2021). Robust Covariance Estimation for High-dimensional Compositional Data with Application to Microbial Communities Analysis. Statistics in Medicine, 40, 3499-3515.

  14. He Y, Chen H†, Sun H†, Ji J, Shi Y, Zhang X, Liu L (2020). High-dimensional Integrative Copula Discriminant Analysis for Multiomics Data. Statistics in Medicine, 39, 4869-4884.

  15. Yu L, He Y*, Zhang X (2019). Robust Factor Number Specification for Large-dimensional Elliptical Factor Model, Journal of Multivariate Analysis, 174, 104543.

  16. He Y, Zhang L†, Ji J, Zhang X. (2019) Robust Feature Screening for Elliptical Copula Regression Model. Journal of Multivariate Analysis, 173, 568-582.

Note: * denote the corresponding author, # denote co-first authors, † denote students/postdocs advised.

Full list of publications in Google Scholar.

Academic service

Reviewer * Multiple times

  • American Mathematical Review*

  • The Annals of Statistics

  • Journal of the Royal Statistical Society : Series B*

  • Journal of the American Statistical Association

  • Journal of Econometrics

  • Journal of Business and Economic Statistics*

  • Biometrics*

  • Annals of Applied Statistics

  • Neuroimage

  • Electronic Journal of Statistics

  • Statistica Sinica

  • Journal of the Royal Statistical Society : Series C*

  • Briefings in Bioinformatics

  • Statistics in Medicine*

  • Journal of Multivariate Analysis*

  • Canadian Journal of Statistics

  • Computational Statistics & Data Analysis

  • Computational Statistics

  • IEEE Transactions on CSVT

  • Journal of Statistical Computation and Simulation

  • Statistics and its Interface

  • International Journal of Forecasting

  • Statistics and Probability Letters

Projects

  1. 2022-2025, Statistical Modelling for High-dimensional Matrix-valued observations (12171282), National Natural Science Foundation of China (PI).

  2. 2019-2021, Variable Selection and Change Point Detection for High-dimensional Elliptical Copula Regression Model (11801316),National Natural Science Foundation of China for Young Scholar (PI).

  3. 2021-2023, Itrative Estimation Theory for Large-dimensional Quantile Factor Model with Application to Financial Risk Control (2019LZ09), National Statistical Scientific Key Project (PI).

In Progress

  1. He Y, Liu D†, Qin K†, Zhou W. Robust Two-way Principal Component Analysis for Matrix-variate Observations in High-dimensional non-Gaussian Distributions.

  2. He Y, Hou Y†, Wang Y†, Zhou W. Statistical Inference for Large-dimensional Tensor Factor Model by Random Projection.

  3. He Y, Wang Y†, Zhang Y†. Network-Assisted Estimation for Large-dimensional Matrix Factor Model

  4. He Y, Ma X†, Wang X†. Factor Modelling for Bi-Clustering Large-dimensional Matrix-valued Time Series

  5. He Y, Hou Y†, Liu H†, Wang Y†. Generalized Principal Component Analysis for Large-dimensional Matrix Factor Model

  6. He Y, Hou Y†, Wang Y†. A New Robust Method for Large-dimensional Factor Models by Time Series Segmentation

  7. He Y, Kong X, Trapani L, Yu L. A Specification Test for Matrix Factor Structure

PhD Student (Co)Supervised

  1. Hao Chen Current Position: Assistant Professor, School of Public Health, Shandong University.

  2. Dong Liu Current Position: Postdoc, Nanyang Technological University.

  3. Dingzi Guo 2022-Present.

  4. Yalin Wang 2024-Present.

  5. Kangxiang Qin 2024-Present.