I obtained my PhD in Economics from the School of Statistics at Southwestern University of Finance and Economics in June 2019. From September 2017 to September 2018, I underwent joint doctoral training at the Department of Statistics and Data Science at the National University of Singapore. From June 2019 to June 2021, I conducted post doctoral research at the School of Statistics at Southwestern University of Finance and Economics. I am currently an associate professor at the School of Statistics, Southwestern University of Finance and Economics. My main research direction is high-dimensional empirical likelihood methods and their applications, as well as sufficient dimension reduction.
Zhang Jia
/Associate Professor
E-mail：zhangjia AT swufe.edu.cn
Awards and honors
Lead research projects
- 2021.01-2023.12: National Natural Science Foundation of China Young Scientists Fund “Research on the Identification of Correct Moment Conditions in High Dimensional Estimation Equation Models”
- 2020.01-2020.12: Youth Teacher Growth Project of the Fundamental Research Funds for the Central Universities “Selection of Ultra High Dimensional Moment Conditions Based on Double Punishment Experience Likelihood”
Representative papers
- 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.