李丰

李丰博士现任中央财经大学统计与数学学院副院长、副教授、硕士生导师,中国统计教育学会高等教育分会会副秘书长。博士毕业于瑞典斯德哥尔摩大学,研究领域包括贝叶斯统计学,预测方法,大数据分布式学习等。曾获瑞典皇家统计学会 Cramér 奖,国际贝叶斯学会青年奖励基金, 第二届全国高校经管类实验教学案例大赛二等奖。主持和参与多项国家自然科学基金项目。

李丰博士最新研究成果发表在统计期刊 Journal of Computational and Graphical Statistics,Statistical Analysis and Data Mining,经济与管理学期刊 International Journal of Forecasting,Journal of Business Research,Journal of the Operational Research Society,人工智能期刊Expert Systems with Applications,医学期刊 BMJ Open、Journal of Surgical Research、Journal of Affective Disorders等。同时著有 Bayesian Modeling of Conditional Densities,《大数据分布式计算与案例》和《统计计算》。

李丰博士在世界贝叶斯大会国际预测大会等作过邀请报告。他的报告幻灯片可以从这里下载

英文简历 | 中文简历

工作信息

中央财经大学 统计与数学学院 副院长、副教授、硕士生导师

个人网页https://feng.li/
电子邮箱feng.li@cufe.edu.cn
办公电话:+86-(0)10-6177-6189 
办公地址:中央财经大学(沙河校区)1号学院楼210房间
     北京市昌平区沙河高教园 邮编:102206

教育背景

研究兴趣

贝叶斯统计学 · 计量经济学 · 预测方法 · 大数据分布式学习

科研项目

  • 国家自然科学基金面上项目(82074282):中医药临床疗效评价中基于目标值法的单臂临床研究方法体系的构建。2021/01-今、项目主要参与人,在研。
  • 国家自然科学基金青年项目(11501587):贝叶斯柔性密度方法及其在高维金融数据中的应用。2016/01-2018/12、项目负责人,结项。
  • 教育部基金项目:贝叶斯弹性高维密度方法在复杂数据的研究。2014/01-2017/12、项目负责人,结项。
  • 国家自然科学基金青年项目(11401603):复发事件的均值模型和纵向数据的分位数回归的统计与推断。2015/01-2017/12、结项、参加。
  • 国家自然科学基金青年项目(71401192):公司财务困境预警模型研究:基于财务波动信息的区间数据刻画方法、2015/01-2017/12、结项、参加。
  • 国家自然科学基金面上项目(71473279):货币总量转向信用总量:全球虚拟经济与实体经济背离机理与宏观政策应对、2015/01-2017/12、结项、参加。

工作论文

  1. Forecasting reconciliation with a top-down alignment of independent level forecasts. (with Matthias Anderer)
  2. Forecast with Forecasts: Diversity Matters. (with Yanfei Kang, Wei Cao, Fotios Petropoulos)
  3. Forecasting: theory and practice. (with Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M.Z., Barrow, D.K., Bergmeir, C., Bessa, R.J., Boylan, J.E., Browell, J., Carnevale, C., Castle, J.L., Cirillo, P., Clements, M.P., Cordeiro, C., Cyrino Oliveira, F.L., De Baets, S., Dokumentov, A., Fiszeder, P., Franses, P.H., Gilliland, M., Gönül, M.S., Goodwin, P., Grossi, L., Grushka-Cockayne, Y., Guidolin, M., Guidolin, M., Gunter, U., Guo, X., Guseo, R., Harvey, N., Hendry, D.F., Hollyman, R., Januschowski, T., Jeon, J., Jose, V.R.R., Kang, Y., Koehler, A.B., Kolassa, S., Kourentzes, N., Leva, S., Litsiou, K., Makridakis, S., Martinez, A.B., Meeran, S., Modis, T., Nikolopoulos, K., Önkal, D., Paccagnini, A., Panapakidis, I., Pavía, J.M., Pedio, M., Pedregal Tercero, D.J., Pinson, P., Ramos, P., Rapach, D., Reade, J.J., Rostami-Tabar, B., Rubaszek, M., Sermpinis, G., Shang, H.L., Spiliotis, E., Syntetos, A.A., Talagala, P.D., Talagala, T.S., Tashman, L., Thomakos, D., Thorarinsdottir, T., Todini, E., Trapero Arenas, J.R., Wang, X., Winkler, R.L., Yusupova, A., Ziel, Z.) 
  4. Distributed Forecasting for Ultra-long Time Series. (with Xiaoqian Wang, Yanfei Kang and Rob J Hyndman)

发表论文

标星(*)为通讯作者

  1. Rui Pan, Tunan Ren, Baishan Guo, Feng Li, Guodong Li and Hansheng Wang (2021). A Note on Distributed Quantile Regression by Pilot Sampling and One-Step Updating. Journal of Business and Economic Statistics. (in press).
  2. Thiyanga S. Talagala, Feng Li, Yanfei Kang (2021). FFORMPP: Feature-based forecast model performance prediction. International Journal of Forecasting. (in press)
  3. Xuening Zhu, Feng Li*, & Hansheng Wang (2021). Least-square approximation for a distributed system. Journal of Computational and Graphical Statistics. (in press).
  4. Xiaoqian Wang, Yanfei Kang, Fotios Petropoulos, & Feng Li* (2021). The uncertainty estimation of feature-based forecast combinations, Journal of the Operational Research Society. (in press).
  5. Yanfei Kang, Evangelos Spiliotis, Fotios Petropoulos, Nikolaos Athiniotis, Feng Li*, & Vassilios Assimakopoulo (2021). Déjà vu: A data-centric forecasting approach through time series cross-similarityJournal of Business Research. 132(2021):719-731.
  6. Megan GJaneway, Xiang Zhao, Max Rosenthaler, Yi Zuo, Kumar Balasubramaniyane, Michael Poulson, Miriam Neufeld, Jeffrey J. Siracuse, Courtney E. Takahashif, Lisa, Allee, Tracey Dechert, Peter A Burke, Feng Li, and Bindu Kalesan (2021). Clinical diagnostic phenotypes in hospitalizations due to self-inflicted firearm injuryJournal of Affective Disorders 278(1):172-180.
  7. Xixi Li, Yanfei Kang,  & Feng Li* (2020). Forecasting with time series imagingExpert Systems with Applications 160: 113680.
  8. Yanfei Kang, Rob J Hyndman,  & Feng Li* (2020). GRATIS: GeneRAting TIme Series with diverse and controllable characteristicsStatistical Analysis and Data Mining 13(4): 354-376.
  9. Chengcheng Hao, Feng Li,  & Dietrich von Rosen (2020). A Bilinear Reduced Rank ModelIn Jianqing Fan and Jianxin Pan (eds.), Contemporary Experimental Design, Multivariate Analysis and Data Mining, Springer.
  10. Bindu Kalesan, Siran Zhao, Michael Poulson, Miriam Neufeld, Tracey Dechert, Jeffrey J Siracuse, Yi Zuo, and Feng Li (2020). Intersections between firearm suicide, drug mortality and economic dependency in rural AmericaJournal of Surgical Research. 256, pp 96-102. Journal’s Cover Paper.
  11. Hyndman, R.J., & Athanasopoulos, G.著. 预测:方法与实践(第2版),康雁飞、李丰(译)https://otexts.com/fppcn/
  12. Hannah M Bailey, Yi Zuo, Feng Li, Jae Min, Krishna Vaddiparti, Mattia Prosperi, Jeffrey Fagan, Sandro Galea,  & Bindu Kalesan (2019). Changes in patterns of mortality rates and years of life lost due to firearms in the united states,1999 to 2016: A joinpoint analysisPLoS One, 14(11).
  13. Feng Li  & Zhuojing He (2019). Credit risk clustering in a business group: which matters more, systematic or idiosyncratic risk? Cogent Economics & Finance, page 1632528.
  14. Feng Li & Yanfei Kang (2018). Improving forecasting performance using covariate-dependent copula models. International Journal of Forecasting, 34(3):456–476.
  15. Elizabeth C Pino, Yi Zuo, Camila Maciel DeOlivera, Shruthi Mahalingaiah, Olivia Keiser, Lynn L Moore, Feng Li, Ramachandran S Vasan, Barbara E Corkey  & Bindu Kalesan (2018). Cohort profile: The multistudy diabetes research (multitude) consortiumBMJ Open, 8(5):e020640.
  16. 李丰(2016)大数据分布式计算与案例。中国人民大学出版社。ISBN 9787300230276. 
  17. Feng Li (2013). Bayesian Modeling of Conditional Densities. Ph.D. thesis, Department of Statistics, Stockholm University. ISBN: 978-91-7447-665-1.
  18. Feng Li  & Mattias Villani (2013). Efficient Bayesian multivariate surface regressionScandinavian Journal of Statistics, 40(4):706–723.
  19. Feng Li, Mattias Villani  & Robert Kohn (2011.). Modeling conditional densities using finite smooth mixtures. In Kerrie Mengersen, Christian Robert, Mike Titterington (eds.), Mixtures: estimation and applications, pages 123–144. John Wiley & Sons Inc, Chichester.
  20. Feng Li, Mattias Villani  & Robert Kohn (2010). Flexible modeling of conditional distributions using smooth mixtures of asymmetric student t densitiesJournal of Statistical Planning and Inference,140(12):3638–3654

受邀学术报告

  1. ICSA Conference on Data Science, January 11-13, 2019, Xishuangbanna, China.
  2. School of Data Science, Fudan University, Oct 28-30, 2017, Shanghai, China
  3. IMS-China International Conference on Statistics and Probability, June 28 – July 1, 2017, Nanning, China.
  4. The 1st International Conference on Econometrics and Statistics, Hong Kong, 15-17 June 2017.
  5. The 2016 World Meeting of the International Society for Bayesian Analysis, Jun 13—17, 2016, Sardinia, Italy.
  6. IMS-China International Conference on Statistics and Probability, June 1-4, 2015, Kunming, China.
  7. International Symposium on Financial Engineering and Risk Management 2014, June 27, 2014, Beijing, China.
  8. Guanghua School of Management, Peking University, Oct 14, 2013, Beijing, China
  9. The Stockholm University Forskardagarna, 2-3 Oct, 2013, Stockholm, Sweden.
  10. The 59th World Statistics Congress, August 25-29, 2013, Hong Kong.
  11. The 2012 World Meeting of the International Society for Bayesian Analysis, Jun 25—29, 2012, Japan.
  12. The third Linnaeus University Workshop in Stochastic Analysis and Applications, May 24—25, 2012, Växjö, Sweden.
  13. Workshop on “Analysis of High-Dimensional Data”, Jönköping International Business School, Feb 16—17, 2012, Sweden.
  14. The LiU Seminar Series in Statistics and Mathematical Statistics, Linköping University, Oct 11, 2011, Sweden.
  15. The 42nd Winter Conference in Statistics — Incomplete data: semi-parametric and Bayesian methods, Mar 6—10, 2011, Sweden.
  16. The 2010 World Meeting of the International Society for Bayesian Analysis, Jun 3—8, 2010, Spain.

学术兼职

  • 2018—今    全国工业统计学教学研究会常务理事、中国青年统计学家协会副秘书长
  • 2014—今     中国统计教学学会高等教育分会副秘书长
  • 担任 Journal of Business and Economic Statistics, International Journal of Forecasting, Computational Statistics and Data Analysis, Journal of Official Statistics, Quantitative Finance, Information Sciences 等期刊审稿人

组织会议

  • The 2017 Beijing Workshop on Forecasting
  • 中国数量经济学会2016年年会
  • 2014年金融工程与风险管理国际研讨会

学术奖励

  • 第二届全国高校经管类实验教学案例大赛二等奖,2017年12月。
  • 瑞典皇家统计学会 Cramér 奖(最佳博士奖), 2014 年 3 月。
  • 国际贝叶斯学会青年奖励基金, 2012 年 6 月。
  • 瑞典 Knut & Alice Wallenberg 基金奖励, 2011 年 8 月。
  • 北京市级优秀毕业生,2007 年 7 月。