李东 副教授

研究方向：复杂时间序列的统计分析，非欧数据分析，空间统计，网络数据分析，机器学习，金融计量学

地址： 清华大学伟清楼203-B室


电话： 010-62780177


邮箱： malidong@tsinghua.edu.cn


工作经历
  • 2016/12-现今，清华大学统计学研究中心，副教授
  • 2015/10-2016/12, 清华大学统计学研究中心，助理教授
  • 2013/09-2015/10, 清华大学丘成桐数学科学中心，助理教授
教育背景
  • 博士 香港科技大学 (2010)
  • 硕士 中科院数学与系统科学研究院 (2005)
  • 学士 曲阜师范大学 (2002)
访问经历
  • 2019/07-2019/08，香港大学，访问学者
  • 2018/07-2018/08，香港大学，访问学者
  • 2017/07-2017/08，香港大学，访问学者
  • 2015/10-2015/10，香港科技大学，访问学者
  • 2013/02-2013/08，香港科技大学，访问学者
  • 2012/05-2012/05，英国伦敦经济与政治学院，访问学者
  • 2011/08-2013/02，美国爱荷华大学，博士后
  • 2011/02-2011/07，香港科技大学，博士后
  • 2005/09-2006/05，香港科技大学，研究助理
研究兴趣
  • 复杂时间序列的统计分析
  • 非欧数据分析
  • 空间统计
  • 网络数据分析
  • 机器学习
  • 金融计量学
发表论文
  • Yang, X., Zhu, Z., Li, D. and Zhu, K. (2023+). Asset pricing via the conditional quantile variational autoencoder. Journal of Business & Economic Statistics.
  • Zhang, X. and Li, D. (2023+). Smooth transition moving average models: Estimation, testing and computation. Journal of Time Series Analysis. 
  • Zhang, X., Li, D.* and Tong, H. (2023+). On the least squares estimation of multiple-threshold-variable autoregressive models. Journal of Business & Economic Statistics.
  • Tao, Y., Li, D. and Niu, X. (2023+). Grouped network Poisson autoregressive model. Statistica Sinica.
  • Li, D., Tao, Y., Yang, Y. and Zhang, R.M. (2023). Maximum likelihood estimation for α-stable double autoregressive models. Journal of Econometrics 236(1), 105471.
  • Jiang, F.Y., Li, D., Li, W.K. and Zhu, K. (2023). Testing and modelling for the structural change in covariance matrix time series with multiplicative form. Statistica Sinica 33(2), 787-818.
  • Luo, D., Zhu, K., Gong, H. and Li, D.* (2023). Testing error distribution by kernelized Stein discrepancy in multivariate time series models. Journal of Business & Economic Statistics 41(1), 111-125.
  • Li, D., Li, M. and Zeng, L. (2022). Simulation and application of subsampling for threshold autoregressive moving-average models. Communications in Statistics: Simulation and Computation 51(5), 2110-2121.
  • Yang, X. and Li, D.* (2022). Estimation of the empirical risk-return relation: A generalized-risk-in-mean model. Journal of Time Series Analysis 43(6), 938-963.
  • Nils Chr. Stenseth*, Yuxin Tao, Chutian Zhang, Barbara Bramanti, Ulf Büntgen, Xianbin Cong, Yujun Cui, Hu Zhou, Lorna Dawson, Sacha Mooney, Dong Li, Henry Fell, Samuel Cohn, Florent Sebbane, Philip Slavin, Wannian Liang, Howell Tong , Ruifu Yang*, Lei Xu* (2022). No evidence for permanent natural plague reservoirs in historical and modern Europe. Natl. Acad. Sci. USA. 119(51), e2209816119.
  • Liu, J., Li, Y., Li, D., Wang, Y. and Wei, S. (2022). The burden of coronary heart disease and stroke attributable to dietary cadmium exposure in Chinese adults, 2017. Science of the Total Environment 825, 153997.
  • Sun, L.Y. and Li, D.* (2021). Change-point detection for expected shortfall in time series. Journal of Management Science and Engineering 6, 324-335.
  • Jiang, F.Y., Li, D. and Zhu, K. (2021). Adaptive inference for a semiparametric GARCH model. Journal of Econometrics 224, 306-329.
  • Jiang, F.Y., Li, D. and Zhu, K. (2020). Non-standard inference for augmented double autoregressive models with null volatility coefficients. Journal of Econometrics 215, 165-183.
  • Li, D. and Tong, H. (2020). On an absolute autoregressive model and skew symmetric distributions. Statistica 80, 177-198.
  • Zhou, J., Li, D., Pan, R. and Wang, H. S. (2020). Network GARCH model. Statistica Sinica 30, 1723-1740.
  • Gong, H. and Li, D.* (2020). On the three-step non-Gaussian quasi-maximum likelihood estimation of heavy-tailed double AR models. Journal of Time Series Analysis 41, 883-891.
  • Li, D.* and Qiu, J.M. (2020). The marginal density of a TMA (1) process. Journal of Time Series Analysis 41, 476-484.
  • Yang, Y. and Li, D.* (2020). Self-weighted LAD-based inference for heavy-tailed continuous threshold autoregressive models. Journal of Time Series Analysis 41,163-172.
  • Li, D. and Zhu, K. (2020). Inference for asymmetric exponentially weighted moving average models. Journal of Time Series Analysis 41,154-162.
  • Guo, S., Li, D. and Li, M.Y. (2019). Strict stationarity testing and GLAD estimation of double autoregressive models. Journal of Econometrics 211, 319-337.
  • Li, D., Guo, S. and Zhu, K. (2019). Double AR model without intercept: An alternative to modeling nonstationarity and heteroscedasticity. Econometric Reviews 38, 319-331.
  • Li, D., Ling, S., Tong, H. and Yang, G.R. (2019). On Brownian motion approximation of compound Poisson processes with applications to threshold models. Advances in Decision Sciences 23. Bridging.pdf
  • Li, D. and Wu, W. (2018). Renorming volatilities in a family of GARCH models. Econometric Theory 34, 1370-1382.
  • Liu, F., Li, D.* and Kang, X.M. (2018). Sample path properties of an explosive double AR model. Econometric Reviews 37, 484-490.
  • Li, D., Zhang, X., Zhu, K. and Ling, S. (2018). The ZD-GARCH model: A new way to study heteroscedasticity. Journal of Econometrics 202, 1-17.
  • Li, D. and Tong, H. (2016). Nested sub-sample search algorithm for estimation of threshold models. Statistica Sinica 26, 1543-1554.
  • Li, D., Ling, S. and Zhang, R.M. (2016). On a threshold double autoregressive model. Journal of Business & Economic Statistics 34, 68-80.
  • Li, D., Ling, S. and Zakoïan, J.-M. (2015). Asymptotic inference in multiple-threshold double autoregressive models. Journal of Econometrics 189, 415-427.
  • Li, D., Li, M. and Wu, W. (2014). On dynamics of volatilities in nonstationary GARCH models. Statistics and Probability Letter 94, 86-90.
  • Chen, M., Li, D.* and Ling, S. (2014). Non-stationarity and quasi-maximum likelihood estimation on a double autoregressive model. Journal of Time Series Analysis 35, 189-202.
  • Chan, K.S., Li, D., Ling, S. and Tong, H. (2014). On conditionally heteroscedastic AR models with thresholds. Statistica Sinica 24, 625-652.
  • Li, D. (2014). Weak convergence of the sequential empirical processes of residuals in TAR models. Science China: Mathematics 57, 173-180.
  • Li, D., Chan, K.S. and Schilling, K.E. (2013). Nitrate concentration trends in Iowa’s rivers, 1998 to 2012: What challenges await nutrient reduction initiatives? Journal of Environmental Quality 42, 1822-1828.
  • Li, D., Ling, S. and Li, W. K. (2013). Asymptotic theory on the least squares estimation of threshold moving-average models. Econometric Theory 29, 482-516.
  • Wu, W., Li, D., Pan, S. and Chen, M. (2013) Three-regime mean reversion, TAR and its applications. Systems Engineering - Theory & Practice 33, 901-909.
  • Li, D. (2012). A note on moving-average models with feedback. Journal of Time Series Analysis 33, 873-879.
  • Li, D., Ling, S. and Tong, H. (2012). On moving-average models with feedback. Bernoulli 18, 735-745.
  • Li, D. and Ling, S. (2012). On the least squares estimation of multiple-regime threshold autoregressive models.  Journal of Econometrics 167, 240-253
  • Li, D., Li, W. K. and Ling, S. (2011). On the least squares estimation of threshold autoregressive and moving-average models. Statistics and Its Interface 4, 183-196.
  • Ling, S. and Li, D. (2008). Asymptotic inference for a non-stationary double AR(1) model. Biometrika 95, 257-263.
  • Ling, S., Tong, H. and Li, D. (2007). Ergodicity and invertibility of threshold moving-average models. Bernoulli 13, 161-168.
在审论文
  • Yu, C., Li, D., Jiang, F. and Zhu, K. (2023). Matrix GARCH model: Inference and application.
  • Li, D., Qiao, X. and Wang, Z.H. (2023). Factor-guided estimation of large covariance matrix function with functional sparsity.
  • Guo, S., Li, D., Qiao, X. and Wang, Y. (2023). From sparse to dense functional data: Phase transitions from a concentration perspective.
  • Zhuang, Y., Li, D., Yu, L.H. and Li, W.K. (2023). On buffered moving average model.
  • Tao, Y. and Li, D.* (2023). Asymmetric GARCH modelling without moment conditions.
  • Ma, X.T., Li, D.* and Tong, H. (2023). On buffered autoregressive modelling with thresholds for time series of counts.
  • Yang, X., Li, D.* and Zhang, T. (2023). A simple random coefficient nonlinear AR model with application to bubble.
  • Li, D. (2023). On the QMLE for AR models with nonstationary GARCH errors.
  • Li, D., Qiao, X. and Yu, C. (2023). Large covariance matrix estimation with factor-assisted variable clustering.
  • Li, D., Qiao, X. and Wang, Z.H. (2023). Factor modelling for matrix-variate functional time series in high dimensions.
教  学
  • 研究生课程
  • 《时间序列分析》(2017/Spring，2022/Fall)
  • 《高等概率论 I》 (2016-22/Fall)
  • 《多元统计分析》 (2014,2015/Spring)
  • 《高等统计(学)》 (2014/Fall)
  • 本科生课程
  • 《应用时间序列分析》(2017,2018,2020,2022/Spring)
  • 《初等概率论》 (2016/Fall)
  • 《统计学引论》(2018/Fall, 2020-22/Spring with Dr. K. Deng)
  • 《金融统计》 (2017, 2019/Spring)
  • 《多元统计分析》（2021/Spring）
毕业学生
社会服务
  • 北京应用统计学会理事【首届】
  • 北京大数据协会理事、常务理事【第2届】
  • 中国现场统计研究会多元分析应用专业委员会理事、常务理事
  • 中国现场统计研究会经济与金融统计分会理事、常务理事
  • 中国现场统计研究会计算统计分会理事【首届】
  • 全国工业统计学教学研究会常务理事【第9届】
  • 全国工业统计学教学研究会青年统计学家协会常务理事【首届】
  • 全国工业统计学教学研究会数字经济与区块链技术协会常务理事【首届】
  • 中国数学会概率统计分会副秘书长【第11届】
  • 中国数学会概率统计分会理事、常务理事【第12届】
组织会议
  • Co-organizer, the international conference on Complex Time Series Modelling and Forecasting: Dynamic Network, Spatio-temporal Data, and Functional Processes, Tsinghua-Sanya International Mathematics Forum, Jan. 8-12, 2018. (with Professor Marc Genton at KAUST, Professor Eric D. Kolaczyk at Boston University, and Professor Qiwei Yao at the LSE)
  • Organizer, Mini workshop on Big Data and Internet Finance, Tsinghua University, Dec. 18, 2016.
  • Co-organizer, 2016 Tsinghua Symposium on Statistics and Data Science for Young Scholars, Tsinghua University, Dec. 9-11, 2016. (with Ke Deng and Lin Hou)
  • Co-organizer, the international conference on Time Series Econometrics, Tsinghua-Sanya International Mathematics Forum, Dec. 18-20, 2015. (with Professor Shiqing Ling at HKUST and Professor Chuanzhong Chen at Hainan Normal University)
杂志审稿
  • Applied Stochastic Models in Business and Industry，Annals of Statistics，Biometrika，Colombian Journal of Statistics，Communications in Statistics - Simulation and Computation，Computational Statistics & Data Analysis，European Journal of Industrial Engineering，Econometric Theory，Journal of Econometrics，Journal of the Korean Statistical Society，Journal of Risk and Financial Management，Metrika，Statistica Sinica，Stochastic Environmental Research and Risk Assessment，Statistics & Probability Letters，
在研项目
  • 国家自然科学基金面上项目，"带有外生变量的非线性非平稳时间序列模型的统计推断及应用"，主持，2020/01-2023/12.
  • 国家自然科学基金面上项目，“高维时间序列的网络分析”，主持，2018/01-2021/12.
  • 国家自然科学基金面上项目，“时间序列分析中几种假设检验问题的研究”，参与，2016/01-2019/12.
  • 国家自然科学青年基金，“带有稳定新息的条件异方差模型的统计推断及其应用”, 主持，2015/01-2017/12.
Mathematics Genealogy (https://genealogy.math.ndsu.nodak.edu/id.php?id=264334&fChrono=1)
  • Essentially, all models are wrong, but some are useful. —— Box, G. P.
  • When solving a given problem, try to avoid solving a more general problem as an intermediate step. —— Vapnik, V.