常晉源

常晉源

常晉源，西南財經大學光華特聘教授、數據科學與商業智慧型聯合實驗室執行主任、博士生導師、國家傑出青年科學基金獲得者、四川省特聘專家、四川省統計專家諮詢委員會委員。主要從事“超高維數據分析”和“高頻金融數據分析”兩個領域的研究。曾榮獲第八屆高等學校科學研究優秀成果獎三等獎（2020）、第十五屆四川省青年科技獎（2020）、四川省第十八次社會科學優秀成果三等獎（2019）、中國數學會鐘家慶數學獎（2013）和國際數理統計協會Laha Award（2012）等獎勵。

基本介紹

人物經歷,工作經歷,教育經歷,主要學術兼職,代表性論文,

人物經歷

工作經歷

  • 2017.11—至今：西南財經大學統計學院 教授
  • 2017.03—2017.11：西南財經大學統計學院 副教授
  • 2013.09—2017.02：澳大利亞墨爾本大學數學與統計學院 Research Fellow

教育經歷

  • 2009.09—2013.07：北京大學光華管理學院 經濟學博士
  • 2005.09—2009.07：北京師範大學數學科學學院 理學學士

主要學術兼職

  • 2023.01—至今：《Journal of the American Statistical Association》 副主編（Associate Editor）
  • 2018.09—至今：《Journal of Business & Economic Statistics》 副主編（Associate Editor）
  • 2017.10—2021.12：《Journal of the Royal Statistical Society Series B》 副主編（Associate Editor）
  • 2017.08—至今：《Statistica Sinica》 副主編（Associate Editor）
  • 2022.01—至今：《管理科學學報》 領域編輯
  • 2019.03—至今：《套用機率統計》 編委
  • 2023.06—至今： 中國現場統計研究會多元分析套用專業委員會 副理事長
  • 2023.06—至今： 中國現場統計研究會經濟與金融統計分會 副會長
  • 2022.12—至今： 中國數學會機率統計分會 常務理事
  • 2019.04—至今： 全國工業統計學教學研究會青年統計學家協會 副會長
  • 2019.04—至今： 中國現場統計研究會高維數據統計分會 常務理事
  • 2018.10—2022.12：中國數學會機率統計分會 理事
  • 2018.10—至今： 中國數量經濟學會 理事

代表性論文

  • Chang, J., He, J., Wu, M. & Kang, J. (2023+). Statistical inferences for complex dependence of multimodal imaging data, Journal of the American Statistical Association, in press.
  • Chang, J., Chen, X. & Wu, M. (2023+). Central limit theorems for high dimensional dependent data, Bernoulli, in press.
  • Chang, J., Chen, C., Qiao, X. & Yao, Q. (2023+). An autocovariance-based learning framework for high-dimensional functional time series, Journal of Econometrics, in press.
  • Chang, J., Hu, Q., Liu, C. & Tang, C. Y. (2023+). Optimal covariance matrix estimation for high-dimensional noise in high-frequency data, Journal of Econometrics, in press.
  • Chang, J., Jiang, Q. & Shao, X. (2022). Testing the martingale difference hypothesis in high dimension, Journal of Econometrics, Vol. 235, pp. 972-1000.
  • Chang, J., Shi, Z. & Zhang, J. (2022). Culling the herd of moments with penalized empirical likelihood, Journal of Business & Economic Statistics, Vol. 41, pp. 791-805.
  • Chang, J., He, J., Yang, L. & Yao, Q. (2022). Modelling matrix time series via a tensor CP-decomposition, Journal of the Royal Statistical Society Series B, Vol. 85, pp. 127-148.
  • Chang, J., Cheng, G. & Yao, Q. (2022). Testing for unit roots based on sample autocovariances, Biometrika, Vol. 109, pp. 543-550.
  • Chang, J., Kolaczyk, E. D. & Yao, Q. (2022). Estimation of subgraph densities in noisy networks, Journal of the American Statistical Association, Vol. 117, pp. 361-374.
  • Chang, J., Chen, S. X., Tang, C. Y. & Wu, T. T. (2021). High-dimensional empirical likelihood inference, Biometrika, Vol. 108, pp. 127-147.
  • Chang, J., Kolaczyk, E. D. & Yao, Q. (2020). Discussion of 'Network cross-validation by edge sampling', Biometrika, Vol. 107, pp. 277-280.
  • Chang, J., Qiu, Y., Yao, Q. & Zou, T. (2018). Confidence regions for entries of a large precision matrix, Journal of Econometrics, Vol. 206, pp. 57-82.
  • Chang, J., Tang, C. Y. & Wu, T. T. (2018). A new scope of penalized empirical likelihood with high-dimensional estimating equations, The Annals of Statistics, Vol. 46, pp. 3185-3216.
  • Chang, J., Guo, B. & Yao, Q. (2018). Principal component analysis for second-order stationary vector time series, The Annals of Statistics, Vol. 46, pp. 2094-2124.
  • Chang, J., Delaigle, A., Hall, P. & Tang, C. Y. (2018). A frequency domain analysis of the error distribution from noisy high-frequency data, Biometrika, Vol. 105, pp. 353-369.
  • Chang, J., Zheng, C., Zhou, W. X. & Zhou, W. (2017). Simulation-based hypothesis testing of high dimensional means under covariance heterogeneity, Biometrics, Vol. 73, pp. 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, Vol. 73, pp. 31-41.
  • Chang, J., Yao, Q. & Zhou, W. (2017). Testing for high-dimensional white noise using maximum cross-correlations, Biometrika, Vol. 104, pp. 111-127.
  • Chang, J., Shao, Q. M. & Zhou, W. X. (2016). Cramer-type moderate deviations for Studentized two-sample U-statistics with applications, The Annals of Statistics, Vol. 44, pp. 1931-1956.
  • Chang, J., Tang, C. Y. & Wu, Y. (2016). Local independence feature screening for nonparametric and semiparametric models by marginal empirical likelihood, The Annals of Statistics, Vol. 44, pp. 515-539.
  • Chang, J., Guo, B. & Yao, Q. (2015). High dimensional stochastic regression with latent factors, endogeneity and nonlinearity, Journal of Econometrics, Vol. 189, pp. 297-312.
  • Chang, J. & Hall, P. (2015). Double-bootstrap methods that use a single double-bootstrap simulation, Biometrika, Vol. 102, pp. 203-214.
  • Chang, J., Chen, S. X. & Chen, X. (2015). High dimensional generalized empirical likelihood for moment restrictions with dependent data, Journal of Econometrics, Vol. 185, pp. 283-304.
  • Chang, J., Tang, C. Y. & Wu, Y. (2013). Marginal empirical likelihood and sure independence feature screening, The Annals of Statistics, Vol. 41, pp. 2123-2148.
  • Chang, J. & Chen, S. X. (2011). On the approximate maximum likelihood estimation for diffusion processes, The Annals of Statistics, Vol. 39, pp. 2820-2851.

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