2013年7月于北京大学光华管理学院取得经济学博士学位，2013年9月至2017年2月在澳大利亚墨尔本大学数学与统计学院从事博士后研究。现为西南财经大学统计交叉创新研究院执行院长、数据科学与商业智能联合实验室主任，中国科学院数学与系统科学研究院研究员。
常晋源
/光华特聘教授、博士生导师
电子邮箱：changjinyuan AT swufe.edu.cn
所获奖项与荣誉
- 2024年：国务院政府特殊津贴
- 2024年：霍英东教育基金会第十九届高等院校青年科学奖一等奖
- 2024年：教育部第九届高等学校科学研究优秀成果奖（人文社会科学）三等奖
- 2024年：第二十七届四川青年五四奖章
- 2023年：刘诗白奖励基金优秀科研成果奖二等奖
- 2020年：霍英东教育基金会第十七届高等院校青年教师奖一等奖
- 2020年：教育部第八届高等学校科学研究优秀成果奖（人文社会科学）三等奖
- 2020年：第十五届四川省青年科技奖
- 2019年：中国数量经济学会第十二届优秀科研成果奖论文一等奖
- 2019年：四川省第十八次社会科学优秀成果奖三等奖
- 2018年：刘诗白奖励基金优秀科研成果奖一等奖
- 2013年：中国数学会第十一届钟家庆数学奖
- 2012年：国际数理统计协会Laha Award
主持科研项目
- 2025.01—2029.12：国家自然科学基金重大项目《大规模商务场景的统计管理理论》
- 2025.01—2029.12：国家自然科学基金重大项目课题《大规模商务场景下的数据科学理论》
- 2024.01—2024.12：国家自然科学基金数学天元基金项目《高维高频金融数据中微观结构噪声的统计推断》
- 2022.01—2026.12：国家杰出青年科学基金项目《求解超高维计量经济模型的快速算法及其理论分析》
- 2021.04—2024.10：四川新网银行横向课题系列项目《风险控制相关技术研究》
- 2020.01—2024.12：国家自然科学基金重大项目课题《时空数据建模和预测研究》
- 2019.09—2020.12：四川新网银行横向课题项目《基于复杂数据风险控制相关技术研究》
- 2019.01—2022.12：国家自然科学基金面上项目《高维高频数据中若干问题的研究》
- 2019.01—2019.12：中央高校基本科研业务费专项基金年度培育项目《超高维高频金融数据中市场微观结构噪音的协方差估计》
- 2018.03—2021.03：霍英东教育基金会第十六届高等院校青年教师基金项目《超高维估计方程模型的理论与实践》
- 2018.01—2018.12：中央高校基本科研业务费专项基金年度培育项目《高频数据中微观结构噪音的统计推断》
- 2017.07—2019.06：中央高校基本科研业务费专项基金重大基础理论研究项目《超高维数据分析的模型、理论与实践》
- 2017.01—2017.12：中央高校基本科研业务费专项基金青年教师成长项目《超高维经验似然的理论与应用》
- 2016.01—2018.12：国家自然科学基金青年基金项目《高维时间序列的降维与建模》
- 2016.01—2016.12：中央高校基本科研业务费专项基金青年教师成长项目《超高维白噪声序列的检验》
代表性论文
- 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.