Dr. Jia Zhang obtained her PhD in Economics from the School of Statistics and Data Science at Southwestern University of Finance and Economics in June 2019. From September 2017 to September 2018, she underwent joint doctoral training at the Department of Statistics and Data Science at the National University of Singapore. From June 2019 to June 2021, she conducted postdoctoral research at the School of Statistics and Data Science at Southwestern University of Finance and Economics. She is currently an associate professor at the School of Statistics and Data Science, Southwestern University of Finance and Economics. Her main research directions include high-dimensional empirical likelihood methods and their applications, as well as sufficient dimension reduction.
Jia Zhang
/ Associate Professor
E-mail：zhangjia AT swufe.edu.cn
Awards and Honors
Research Projects
- “Research on the Identification of Correct Moment Conditions in High-Dimensional Estimation Equation Models”, National Natural Science Foundation of China (Young Scientists Fund), 2021–2024
- “Research on High-Dimensional Moment Condition Selection Based on Double-Penalized Empirical Likelihood Estimation”, General Program of China Postdoctoral Science Foundation, 2020–2021
- “Selection of Ultra High-Dimensional Moment Conditions Based on Double-Penalized Empirical Likelihood”, The Fundamental Research Funds for the Central Universities (Young Faculty Development Program), 2020
Published Paper
- Zhang, J., Wu, R., & Chen, X. (2025+). Optimal sparse sliced inverse regression via random projection. Journal of Computational and Graphical Statistics, in press.
- Chen, X., Wu, J., Yao, Z., & Zhang, J. (2024+). Sufficient dimension reduction for classification. Statistica Sinica, in press.
- Chen, X., Deng, C., He, S., Wu, R., & Zhang, J. (2024). High-dimensional sparse single-index regression via Hilbert-Schmidt independence criterion. Statistics and Computing, 34, 86.
- Chang, J., Shi, Z., & Zhang, J. (2023). Culling the herd of moments with penalized empirical likelihood. Journal of Business & Economic Statistics, 41, 791-805.
- Chen, X., Zhang, J., & Zhou, W. (2022). High dimensional elliptical sliced inverse regression in non-Gaussian distributions. Journal of Business & Economic Statistics, 40, 1204-1215.
- Zhang, J., & Chen, X. (2020). Principal envelope model. Journal of Statistical Planning and Inference, 206, 249-262.
- Zhang, J., & Chen, X. (2019). Robust sufficient dimension reduction via ball covariance. Computational Statistics & Data Analysis, 140, 144-154.
- Zhang, J., Shi, H., Tian, L., & Xiao, F. (2019), Penalized generalized empirical likelihood in high-dimensional weakly dependent data. Journal of Multivariate Analysis, 171, 270-283.