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Yong He (何勇)

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Full 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 full professor at Institute for Financial Studies, Shandong University. My research interest includes high-dimensional statistical inference, financial econometrics, biostatistics and statistical learning. 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: Tensors; Variable Selection; Graphical Model; Large-scale multiple testing; Copula.

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

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

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Preprints

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

  2. He Y, Wang Y†, Yu L, Zhou W, Zhou W. Matrix Kendall's tau in High-dimensions: A Tail Robust Statistic for Matrix Factor Model. [arXiv:2207.09633]

  3. He Y, Kong X, Trapani L, Yu L. Online Change-point Detection for Matrix-valued Time Series with Latent Two-way Factor Structure. The Annals of Statistics, major revision required. [arXiv:2112.13479][Manuscript & Supplement][R package]

  4. 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]

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

  5. He Y, Kong X, Liu D†, Zhao R†. Robust Statistical Inference for Large-dimensional Matrix-valued Time Series via Iterative Huber Regression.[arXiv:2306.03317][R package]

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

  6. He Y, Zhao R†, Zhou W. Iterative Alternating Least Square Estimation for Large-dimensional Matrix Factor Model. Journal of Computational and Graphical Statistics, under revision. [arXiv:2301.00360][R package]

  7. 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]

  8. Barigozzi M, He Y*, Li L†, Trapani L. Statistical Inference for Large-dimensional Tensor Factor Model by Iterative Projection. Journal of Business and Economic Statistics, R & R, [arXiv:2206.09800]

  9. Li Z, He Y*, Kong X, Zhang X. Robust Two-way Dimension Reduction by Grassmannian Barycenter. [arXiv:2203.14063]

  10. He Y, Kong X, Yu L, Zhao P. Quantile factor analysis for large-dimensional time series with statistical guarantee. Science China: Mathematics, under revision. [arXiv:2006.08214]

  11. 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. [arXiv:2106.03334]

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

  13. Li Z, Liu D†, He Y*, Zhang X. The Role of Fine-tuning: Transfer Learning for High-dimensional M-estimators with Decomposbale Regularizers. [arXiv:2306.04182][R package]

Selected publications

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

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

  3. 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]

  4. 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]

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

  6. 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]

  7. 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]

  8. 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]

  9. 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]

  10. 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]

  11. 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.

  12. 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.

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

  14. 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. Expectile Factor Model

  5. Transfer Learning for High-dimensional Linear Discriminant Analysis