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。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,Journal of Business and Economic Statistics, Statistical Analysis and Data Mining,经济与管理学期刊 International Journal of Forecasting,Journal of Business Research,运筹学期刊European Journal of Operational 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. Bayesian forecast combination using time-varying features. (with Li Li and Yanfei Kang)
  2. Distributed Forecasting for Ultra-long Time Series. (with Xiaoqian Wang, Yanfei Kang and Rob J Hyndman)

学术发表

标星(*)为通讯作者

  1. Matthias Anderer & Feng Li*, (2021). Hierarchical forecasting with a top-down alignment of independent level forecasts. International Journal of Forecasting. (In Press)
  2. Fotios Petropoulos, Daniele Apiletti, Vassilios Assimakopoulos, Mohamed Zied Babai, Devon K. Barrow, Souhaib Ben Taieb, Christoph Bergmeir, Ricardo J. Bessa, Jakub Bijak, John E. Boylan, Jethro Browell, Claudio Carnevale, Jennifer L. Castle, Pasquale Cirillo, Michael P. Clements, Clara Cordeiro, Fernando Luiz Cyrino Oliveira, Shari De Baets, Alexander Dokumentov, Joanne Ellison, Piotr Fiszeder, Philip Hans Franses, David T. Frazier, Michael Gilliland, M. Sinan Gönül, Paul Goodwin, Luigi Grossi, Yael Grushka-Cockayne, Mariangela Guidolin, Massimo Guidolin, Ulrich Gunter, Xiaojia Guo, Renato Guseo, Nigel Harvey, David F. Hendry, Ross Hollyman, Tim Januschowski, Jooyoung Jeon, Victor Richmond R. Jose, Yanfei Kang, Anne B. Koehler, Stephan Kolassa, Nikolaos Kourentzes, Sonia Leva, Feng Li, Konstantia Litsiou, Spyros Makridakis, Gael M. Martin, Andrew B. Martinez, Sheik Meeran, Theodore Modis, Konstantinos Nikolopoulos, Dilek Önkal, Alessia Paccagnini, Anastasios Panagiotelis, Ioannis Panapakidis, Jose M. Pavía, Manuela Pedio, Diego J. Pedregal, Pierre Pinson, Patrícia Ramos, David E. Rapach, J. James Reade, Bahman Rostami-Tabar, Michał Rubaszek, Georgios Sermpinis, Han Lin Shang, Evangelos Spiliotis, Aris A. Syntetos, Priyanga Dilini Talagala, Thiyanga S. Talagala, Len Tashman, Dimitrios Thomakos, Thordis Thorarinsdottir, Ezio Todini, Juan Ramón Trapero Arenas, Xiaoqian Wang, Robert L. Winkler, Alisa Yusupova, & Florian Zie (2021). Forecasting: theory and practiceInternational Journal of Forecasting (In Press)
  3. Yanfei Kang, Wei Cao, Fotios Petropoulos & Feng Li* (2021). Forecast with Forecasts: Diversity MattersEuropean Journal of Operational Research. (In Press)
  4. Rui Pan, Tunan Ren, Baishan Guo, Feng Li, Guodong Li & Hansheng Wang (2021). A Note on Distributed Quantile Regression by Pilot Sampling and One-Step UpdatingJournal of Business and Economic Statistics. (In Press).
  5. Thiyanga S. Talagala, Feng Li, Yanfei Kang (2021). FFORMPP: Feature-based forecast model performance predictionInternational Journal of Forecasting. (In Press)
  6. Xuening Zhu, Feng Li*, & Hansheng Wang (2021). Least-square approximation for a distributed systemJournal of Computational and Graphical Statistics. 30(4):1004-1018.
  7. 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).
  8. 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.
  9. Megan G Janeway, 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.
  10. Xixi Li, Yanfei Kang,  & Feng Li* (2020). Forecasting with time series imagingExpert Systems with Applications 160: 113680.
  11. 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.
  12. 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.
  13. 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.
  14. 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/
  15. 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).
  16. Feng Li  & Zhuojing He (2019). Credit risk clustering in a business group: which matters more, systematic or idiosyncratic risk? Cogent Economics & Finance, page 1632528.
  17. Feng Li & Yanfei Kang (2018). Improving forecasting performance using covariate-dependent copula modelsInternational Journal of Forecasting, 34(3):456–476.
  18. 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.
  19. 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. 
  20. Feng Li (2013). Bayesian Modeling of Conditional Densities. Ph.D. thesis, Department of Statistics, Stockholm University. ISBN: 978-91-7447-665-1.
  21. Feng Li  & Mattias Villani (2013). Efficient Bayesian multivariate surface regressionScandinavian Journal of Statistics, 40(4):706–723.
  22. Feng Li, Mattias Villani  & Robert Kohn (2011.). Modeling conditional densities using finite smooth mixturesIn Kerrie Mengersen, Christian Robert, Mike Titterington (eds.), Mixtures: estimation and applications, pages 123–144. John Wiley & Sons Inc, Chichester.
  23. 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

报告与访谈

Slides are available from https://github.com/feng-li/talks.

Time
(时间)
Venue (地点)Topic (主题)
2021-11-14Forecasting Impact PodcastComputing, forecasting and learning with massive machines
2021-11-20第14届中国R会议软件工具专场Developing Distributed Models with Spark
2021-10-24首都师范大学海量数据驱动场景及其数据科学方法
2021-09-13Data Skeptic Podcast Distributed ARIMA Models for Ultra-long Time Series
2021-09-06狗熊会复杂数据的高可延展分布式建模与计算机实现
2021-07-02The 2021 World Meeting of the International Society for Bayesian AnalysisDistributed Forecasting with Large Bayesian Vector Autoregressions
2021-06-28The 41st International Symposium on ForecastingHighly scalable distributed modelling and forecasting with dependent data
2020-10-26The 40th International Symposium on ForecastingFeature-based Bayesian Forecast Model Averaging
2019-07-08The 12th International Conference on Monte Carlo Methods and Applications, Sydney, AustraliaBayesian high-dimensional covariate-dependent copula modeling with application to stocks and text sentiments
2019-06-17The 39th International Symposium on Forecasting, Thessaloniki, GreeceTime series forecasting based on automatic feature extraction
2016-11-21统计之都COS 访谈第 22 期: 李丰老师
2014-10-25Stockholm UniversityInterview with Feng Li, PhD
2014-06Qvintensen ArticleComplex Model for Complex Data via the Bayesian Approach
2014-03-22The Swedish Cramér Society 2014 Annual MeetingBayesian Modeling of Conditional Densities

期刊审稿

担任 Journal of Business and Economic Statistics, International Journal of Forecasting, Computational Statistics and Data Analysis, Pattern Recognition, Neurocomputing 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。