Dr. Jing He graduated from Guanghua School of Management, Peking University in July 2016 with a Ph.D. in Statistics. She is currently a professor at the School of Statistics and Data Science, Southwestern University of Finance and Economics. Her main research interests include high-dimensional data analysis and complex data statistical inference.
Jing He
/ Professor
E-mail：he_jing AT swufe.edu.cn
Awards and Honors
Research Projects
- “Ultra High-Dimensional (Conditional) Independence Tests and Their Applications in Economics”, National Natural Science Foundation of China (General Program), 2025–2028
- “Statistical Inference of Market Microstructure Noise Structure for Ultra High-Dimensional and High-Frequency Data”, The Fundamental Research Funds for the Central Universities (Annual Cultivation Project ), 2021
- “Testing High-Dimensional Covariance Matrix Structures under Non-Normality”, National Natural Science Foundation of China (Young Scientists Fund), 2018–2020
- “Testing and Estimation of the Independence Structure of Ultra High-Dimensional Data”, The Fundamental Research Funds for the Central Universities (Young Faculty Development Program), 2018
- “Research on Testing High-Dimensional Covariance Matrices of Two Populations Based on Sub-Diagonal Structure”, The Fundamental Research Funds for the Central Universities (Young Faculty Cultivation Program), 2017
Published Paper
- 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., 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., 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.
- Zheng, X., Guo, B., He, J., & Chen, S. X. (2021). Effects of corona virus disease‐19 control measures on air quality in North China. Environmetrics, 32, e2673.
- He, J., & Chen, S. X. (2018). High-dimensional two-sample covariance matrix testing via super-diagonals. Statistica Sinica, 28, 2671-2696.
- Chen, L., Guo, B., Huang, J., He, J., Wang, H., Zhang, S., & Chen, S. X. (2018). Assessing air-quality in Beijing-Tianjin-Hebei region: The method and mixed tales of PM2.5 and O3. Atmospheric Environment, 193, 290-301.
- Zhang, S., Guo, B., Dong, A., He, J., Xu, Z., & Chen, S. X. (2017). Cautionary tales on air-quality improvement in Beijing. Proceedings of the Royal Society A, 473, 20170457.
- He, J., & Chen, S. X. (2016). Testing super-diagonal structure in high dimensional covariance matrices. Journal of Econometrics, 194, 283-297.