Jinyuan Chang

/ Guanghua University Chair Professor

Dr. Jinyuan Chang received his Ph.D. in Economics from the Guanghua School of Management at Peking University in July 2013. From September 2013 to February 2017, he was a Research Fellow in the School of Mathematics and Statistics at the University of Melbourne. He currently serves as Executive Dean of the Institute of Statistics and Interdisciplinary Research at Southwestern University of Finance and Economics (SWUFE), and as Executive Director of the Joint Laboratory of Data Science and Business Intelligence, SWUFE . He is also a Professor in the Academy of Mathematics and Systems Science at Chinese Academy of Sciences.

E-mail：changjinyuan AT swufe.edu.cn

Awards and Honors

2025: New Era Youth Pioneer of 2025
2024: The Special Government Allowance of the State Council, The First Prize of the 19th Fok Ying Tung Education Foundation Higher Education Youth Science Award, The Third Prize of the 9th Outstanding Scientific Research Achievement Award in Institutes of Higher Education (Humanities and Social Sciences) by the Ministry of Education, The 27th Sichuan Youth May 4th Medal
2023: The Second Prize of Liu Shibai Fund for Outstanding Scientific Achievement
2021: The National Science Fund for Distinguished Young Scholars of China
2020: The First Prize of the 17th Fok Ying Tung Education Foundation Higher Education Young Teacher Award, The Third Prize of the 8th Excellent Scientific Research Achievement Award in Institutes of Higher Education (Humanities and Social Sciences) by the Ministry of Education, The 15th Sichuan Youth Science and Technology Award
2019: The First Prize of 12th Outstanding Scientific Achievement Paper Award by the Chinese Association of Quantitative Economics, The Third Prize of the 18th Excellent Social Science Achievement Award in Sichuan Province
2018: The First Prize of Liu Shibai Fund for Outstanding Scientific Achievement
2015: The Distinguished Expert of Sichuan Province
2013: The 11th Zhongjiaqing Mathematics Award, the Chinese Mathematical Society
2012: The Laha Award, Institute of Mathematical Statistics

Research Projects

“Statistical management theory in large-scale business scenarios”, National Natural Science Foundation of China (Major Program), 2025–2029
“data science theory in large-scale business scenarios”, Subproject of the Major Program of National Natural Science Foundation of China, 2025–2029
“Statistical inference for high-dimensional market microstructure noise in high-frequency financial data”, National Natural Science Foundation of China (Mathematics Tianyuan Program), 2024
“Computationally efficient algorithms for ultra high-dimensional econometric models and the related theoretical guarantees”, National Science Fund for Distinguished Young Scholars, 2022–2026
“Research on Risk Control Related Technologies”, Research Project Series with Sichuan XW Bank, 2021–2024
“Modelling and forecasting of spatio-temporal data”, National Natural Science Foundation of China (Major Program), 2020–2024
“Research on risk control related technologies based on complex data”, Research Project with Sichuan XW Bank, 2019–2020
“Several topics in high-dimensional high-frequency data”, National Natural Science Foundation of China (General Program), 2019–2022
“Covariance matrix estimation for high-dimensional market microstructure noise in high-frequency financial data”, The Fundamental Research Funds for the Central Universities, 2019
“Ultra high-dimensional estimating equations”, The Fund of Fok Ying-Tong Education Foundation for Young Teachers in the Higher Education Institutions of China, 2018–2021
“Statistical inference for the market microstructure noise in high-frequency data”, The Fundamental Research Fund for the Central Universities, 2018
“On the theory and applications of ultra high-dimensional models”, The Fundamental Research Funds for the Central Universities, 2017–2019
“Theory of ultra high-dimensional empirical likelihood and its applications”, The Fundamental Research Funds for the Central Universities, 2017
“Dimension reduction and modeling for high dimensional time series”, National Natural Science Foundation of China (Young Scientists Fund), 2016–2018
“Testing for ultra high-dimensional white noise”, The Fundamental Research Funds for the Central Universities, 2016

Published Paper

Yang, L., Huang, Y., Chang, J., Zheng, O., Ma, M., & Jiang, X. (2026). EMMS: Evidential multi-label multi-dimensional selection. Proceedings of the 35th International Joint Conference on Artificial Intelligence, in press.
Chang, J., Fang, Q., Kolaczyk, E., MacDonald, P., & Yao, Q. (2026+). Autoregressive networks with dependent edges. Journal of the Royal Statistical Society Series B, in press.
Chang, J., Yang, L., Zha, M., & Zhou, W.-X. (2026+). Adapting to noise tails in private linear regression. Journal of the American Statistical Association, in press.
Chang, J., Du, Y., He, J., & Yao, Q. (2026+). Testing independence and conditional independence in high dimensions via coordinatewise Gaussianization. Journal of the American Statistical Association, in press.
Chang, J., Du, Y., Huang, G. & Yao, Q. (2026). Identification and estimation for matrix time series CP-factor models , The Annals of Statistics, 54, 1372–1397.
Chang, J., Fang, Q., Qiao, X., & Yao, Q. (2025). On the modeling and prediction of high-dimensional functional time series. Journal of the American Statistical Association, 120, 2181-2195.
Chang, J., Tang, C. Y., & Zhu, Y. (2025). Bayesian penalized empirical likelihood and Markov Chain Monte Carlo sampling. Journal of the Royal Statistical Society Series B, 87, 1127-1149.
Chang, J., Jiang, Q., McElroy, T., & Shao, X. (2025). Statistical inference for high-dimensional spectral density matrix. Journal of the American Statistical Association, 120, 1960-1974.
Chang, J., Ding, Y., Shi, Z., & Zhang, B. (2025). Zero variance portfolio.
Chang, J., Duan, C., Jiao, Y., Li, R., Yang, J. Z., & Yuan, C. (2025). Provable diffusion posterior sampling for Bayesian inversion.
Chang, J., Huang, G., Yao, Q., & Yu, L. (2025). CP-factorization for high-dimensional tensor time series and double projection iterations.
Chang, J., Yang, L., Zhang, Y., & Zhou, W.-X. (2025). Feature-rich, data-private: A sparse learning framework for the high-dimensional newsvendor.
Chang, J., Du, Y., Hong, S., Lin, C., & Zhou, W.-X. (2025). Predictive tail-risk regression for heavy-tailed time series.
Yan, H., Gao, Y., Chang, J., & Wang, H. (2025). A semi-parametric neural network approach for auxiliary learning.
Chang, J., Li, C., Tang, C. Y., & Zhu, Z. (2025). Controlling the false discovery rate in high-dimensional linear models using model-X knockoffs and p-values.
Chang, J., Hu, Q., Shi, Z., & Zhang, J. (2025). Empirical likelihood approach for high-dimensional moment restrictions with dependent data.
Chang, J., Chen, C., Qiao, X., & Yao, Q. (2024). An autocovariance-based learning framework for high-dimensional functional time series. Journal of Econometrics, 239, 105385.
Chang, J., Hu, Q., Liu, C., & Tang, C. Y. (2024). Optimal covariance matrix estimation for high-dimensional noise in high-frequency data. Journal of Econometrics, 239, 105329.
Chang, J., Hu, Q., Kolaczyk, E. D., Yao, Q., & Yi, F. (2024). Edge differentially private estimation in the β-model via jittering and method of moments. Annals of Statistics, 52, 708-728.
Chang, J., He, J., Kang, J., & Wu, M. (2024). Statistical inferences for complex dependence of multimodal imaging data. Journal of the American Statistical Association, 119, 1486-1499.
Chang, J., Chen, X., & Wu, M. (2024). Central limit theorems for high dimensional dependent data. Bernoulli, 30, 712-742.
Chang, J., Ding, Z., Jiao, Y., Li, R., & Yang, J. Z. (2024). Deep conditional distribution learning via conditional Föllmer flow.
Chang, J., Jiang, Q., & Shao, X. (2023). Testing the martingale difference hypothesis in high dimension. Journal of Econometrics, 235, 972-1000.
Chang, J., Shi, Z., & Zhang, J. (2023). Culling the herd of moments with penalized empirical likelihood. Journal of Business & Economic Statistics, 41, 791-805.
Chang, J., He, J., Yang, L., & Yao, Q. (2023). Modelling matrix time series via a tensor CP-decomposition. Journal of the Royal Statistical Society Series B, 85, 127-148.
Chang, J., Tang, C. Y., & Zhu, Y. (2023). Efficiently handling constraints with Metropolis-adjusted Langevin algorithms.
Chang, J., Cheng, G., & Yao, Q. (2022). Testing for unit roots based on sample autocovariances. Biometrika, 109, 543-550.
Chang, J., Kolaczyk, E. D., & Yao, Q. (2022). Estimation of subgraph densities in noisy networks. Journal of the American Statistical Association, 117, 361-374.
Chang, J., Chen, S. X., Tang, C. Y., & Wu, T. T. (2021). High-dimensional empirical likelihood inference. Biometrika, 108, 127-147.
Chang, J., Kolaczyk, E. D., & Yao, Q. (2020). Discussion of ‘Network cross-validation by edge sampling’. Biometrika, 107, 277-280.
Chang, J., Tang, C. Y., & Wu, T. T. (2018). A new scope of penalized empirical likelihood with high-dimensional estimating equations. Annals of Statistics, 46, 3185-3216.
Chang, J., Guo, B., & Yao, Q. (2018). Principal component analysis for second-order stationary vector time series. Annals of Statistics, 46, 2094-2124.
Chang, J., Qiu, Y., Yao, Q., & Zou, T. (2018). Confidence regions for entries of a large precision matrix. Journal of Econometrics, 206, 57-82.
Chang, J., Delaigle, A., Hall, P., & Tang, C. Y. (2018). A frequency domain analysis of the error distribution from noisy high-frequency data. Biometrika, 105, 353-369.
Chang, J., Guo, J., & Tang, C. Y. (2018). Peter Hall’s contribution to empirical likelihood. Statistica Sinica, 28, 2375–2387.
Chang, J., Zheng, C., Zhou, W. X., & Zhou, W. (2017). Simulation-based hypothesis testing of high dimensional means under covariance heterogeneity. Biometrics, 73, 1300-1310.
Chang, J., Zhou, W., Zhou, W. X., & Wang, L. (2017). Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering. Biometrics, 73, 31-41.
Chang, J., Yao, Q., & Zhou, W. (2017). Testing for high-dimensional white noise using maximum cross-correlations. Biometrika, 104, 111-127.
Chang, J., Shao, Q. M., & Zhou, W. X. (2016). Cramér-type moderate deviations for Studentized two-sample U-statistics with applications. Annals of Statistics, 44, 1931-1956.
Chang, J., Tang, C. Y., & Wu, Y. (2016). Local independence feature screening for nonparametric and semiparametric models by marginal empirical likelihood. Annals of Statistics, 44, 515-539.
Chang, J., Guo, B., & Yao, Q. (2015). High dimensional stochastic regression with latent factors, endogeneity and nonlinearity. Journal of Econometrics, 189, 297-312.
Chang, J., & Hall, P. (2015). Double-bootstrap methods that use a single double-bootstrap simulation. Biometrika, 102, 203-214.
Chang, J., Chen, S. X., & Chen, X. (2015). High dimensional generalized empirical likelihood for moment restrictions with dependent data. Journal of Econometrics, 185, 283-304.
Chang, J., Tang, C. Y., & Wu, Y. (2013). Marginal empirical likelihood and sure independence feature screening. Annals of Statistics, 41, 2123-2148.
Chang, J., & Chen, S. X. (2011). On the approximate maximum likelihood estimation for diffusion processes. Annals of Statistics, 39, 2820-2851.