Jinyuan Chang earned his Ph.D. in economics from the Guanghua School of Management, Peking University, in July 2013. From September 2013 to February 2017, he conducted postdoctoral research at the School of Mathematics and Statistics, University of Melbourne, Australia. Currently, he serves as Executive Dean of the Institute for Statistics and Interdisciplinary Innovation at Southwestern University of Finance and Economics, as well as Director of the Joint Laboratory for Data Science and Business Intelligence. He is also a research fellow at the Academy of Mathematics and Systems Science, Chinese Academy of Sciences.
Chang, Jinyuan
/Guanghua Distinguish Professor
E-mail：changjinyuan AT swufe.edu.cn
Awards and honors
- 2024: the Special Government Allowances of the State Council Expert
- 2024: the First Prize of the 19th Fok Ying Tung Education Foundation Higher Education Youth Science Award
- 2024: the Third Prize of the 9th Excellent Scientific Achievement Award in Higher Education (Humanities and Social Sciences) by the Ministry of Education
- 2024: the 27th May 4th Youth Award of Sichuan
- 2023: the Second Prize of Liu Shibai Fund for Outstanding Scientific Achievement
- 2020: the First Prize of the 17th Fok Ying Tung Education Foundation Higher Education Young Teacher Award
- 2020: the Third Prize of the 8th Excellent Scientific Achievement Award in Higher Education (Humanities and Social Sciences) by the Ministry of Education
- 2020: 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
- 2019: 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
- 2013: the 11th Zhongjiaqing Mathematics Award, the Chinese Mathematical Society
- 2012: the Laha Award, the International Association for Mathematical Statistics
Lead research projects
- 2025.01 – 2029.12: Major Program of National Natural Science Foundation of China, “Statistical management theory of large scale business scenarios”
- 2025.01 – 2029.12: Project of the Major Program of National Natural Science Foundation of China, “Data science theory in large-scale nusiness scenarios”
- 2024.01 – 2024.12: Tianyuan Fund for Mathematics, National Natural Science Foundation of China, Statistical Inference of Microstructure Noise in High-Dimensional High-Frequency Financial Data.
- 2022.01 – 2026.12: National Science Fund for Distinguished Young Scholars of China, “Fast algorithm and theoretical analysis for solving ultra high dimensional econometric models”
- 2021.04 – 2024.10: Research Project Series with Sichuan XW Bank, “Research on risk control related technologies”
- 2020.01 – 2024.12: Project of the Major Program of National Natural Science Foundation of China, “Research on spatiotemporal data modeling and prediction”
- 2019.09 – 2020.12: Research Project with Sichuan XW Bank, “Research on risk control related technologies based on complex data”
- 2019.01 – 2022.12: General Program of National Natural Science Foundation of China , “Research on several issues in high dimensional and high frequency data”
- 2019.01 – 2019.12: Annual Cultivation Project of the Fundamental Research Funds for the Central Universities, “Covariance estimation of market microstructure noise in ultra high dimensional and high frequency financial data”
- 2018.03 – 2021.03: The 16th Young Teacher Funds for the Higher Education Institutions of China, Fok Ying Tung Education Foundation, “Theory and Practice of Ultra-High-Dimensional Estimating Equation Models”
- 2018.01 – 2018.12: Annual Cultivation Project of the Fundamental Research Funds for the Central Universities, “Statistical inference of microstructure noise in high frequency data”
- 2017.07 – 2019.06: Major Fundamental Theory Project of the Fundamental Research Funds for the Central Universities, “Models, theory, and practice of ultra high dimensional data analysis”
- 2017.01 – 2017.12: Youth Teacher Growth Project of the Fundamental Research Funds for the Central Universities, “Theory and application of ultra high dimensional empirical likelihood”
- 2016.01 – 2018.12: National Natural Science Foundation of China Young Scientists Fund, “Dimensionality reduction and modeling of high dimensional time series”
- 2016.01 – 2016.12: Youth Teacher Growth Project of the Fundamental Research Funds for the Central Universities, “Testing of ultra high dimensional white noise sequence”
Representative papers
- 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, in press.
- Chang, J., Jiang, Q., McElroy, T., & Shao, X. (2025+). Statistical inference for high-dimensional spectral density matrix. Journal of the American Statistical Association, in press.
- Chang, J., Fang, Q., Qiao, X., & Yao, Q. (2024+). On the modeling and prediction of high-dimensional functional time series. Journal of the American Statistical Association, in press.
- 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., 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: Statistical Methodology, 85, 127-148.
- 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.