Dr. Feng Li is an Associate Professor of Statistics in the School of Statistics and Mathematics at Central University of Finance and Economics in Beijing, China. He earned his Ph.D. degree in Statistics from Stockholm University, Sweden in 2013. Feng Li also served as the Associate Dean for the School of Statistics and Mathematics at Central University of Finance and Economics since 2016.
- Ph.D., 2013, Statistics, Stockholm University, Sweden.
- Thesis: Bayesian Modeling of Conditional Densities (The 2014 Cramér Prize, for the best Ph.D. thesis in Statistics and Mathematical Statistics, awarded by the Swedish Statistical Society)
- M.S., 2008, Statistics, Dalarna University, Sweden.
- B.S., 2007, Statistics, Renmin University of China (Outstanding graduate student, honored by Beijing Municipal Education Commission)
Dr. Feng Li’s research interests include Bayesian Statistics, Econometrics and Forecasting, Distributed Learning. He develops highly scalable algorithms and software for solving real business problems. His recent research output appeared in top-tier journals like the Journal of Computational and Graphical Statistics, International Journal of Forecasting, Journal of Business and Economic Statistics, European Journal of Operational Research and Journal of the Operational Research Society. Feng Li has presented at the world meeting of the International Society for Bayesian Analysis (ISBA), and International Symposium on Forecasting.
Grants and Projects⚙️
- Development of the Methodologies of Objective Performance Criteria Based Single-Armed Trials for The Clinical Evaluation of Traditional Chinese Medicine. funded by National Natural Science Foundation of China, (2020-). Major Investigator.
- Efficient Bayesian Flexible Density Methods with High Dimensional Financial Data funded by National Natural Science Foundation of China, (2016-2019). Principal investigator.
- Bayesian Multivariate Density Estimation Methods for Complex Data funded by Ministry of Education, China (2014-2016). Principal investigator
- Bayesian forecast combination using time-varying features. (with Li Li and Yanfei Kang)
- Forecasting reconciliation with a top-down alignment of independent level forecasts. (with Matthias Anderer)
- [ Working Paper ]
- Distributed Forecasting for Ultra-long Time Series. (with Xiaoqian Wang, Yanfei Kang and Rob J Hyndman)
- 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 practice. International Journal of Forecasting (In Press)
- [ Working Paper ]
- Yanfei Kang, Wei Cao, Fotios Petropoulos & Feng Li* (2021). Forecast with Forecasts: Diversity Matters. European Journal of Operational Research. (In Press)
- [ Working Paper ]
- 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 Updating. Journal of Business and Economic Statistics. (In Press).
- Thiyanga S. Talagala, Feng Li, Yanfei Kang (2021). FFORMPP: Feature-based forecast model performance prediction. International Journal of Forecasting. (In Press)
- Xuening Zhu, Feng Li*, & Hansheng Wang (2021). Least-square approximation for a distributed system. Journal of Computational and Graphical Statistics. (In Press).
- 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).
- Yanfei Kang, Evangelos Spiliotis, Fotios Petropoulos, Nikolaos Athiniotis, Feng Li*, & Vassilios Assimakopoulo (2021). Déjà vu: A data-centric forecasting approach through time series cross-similarity, Journal of Business Research. 132(2021):719-731.
- 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 injury, Journal of Affective Disorders 278(1):172-180.
- Xixi Li, Yanfei Kang, & Feng Li* (2020). Forecasting with time series imaging, Expert Systems with Applications 160: 113680.
- Yanfei Kang, Rob J Hyndman, & Feng Li* (2020). GRATIS: GeneRAting TIme Series with diverse and controllable characteristics, Statistical Analysis and Data Mining 13(4): 354-376.
- Chengcheng Hao, Feng Li, & Dietrich von Rosen (2020). A Bilinear Reduced Rank Model, In Jianqing Fan and Jianxin Pan (eds.), Contemporary Experimental Design, Multivariate Analysis and Data Mining, Springer.
- 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 America, Journal of Surgical Research. 256, pp 96-102. Journal’s Cover Paper.
- 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/
- 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 analysis. PLoS One, 14(11).
- Feng Li & Zhuojing He (2019). Credit risk clustering in a business group: which matters more, systematic or idiosyncratic risk? Cogent Economics & Finance, page 1632528.
- [ Working Paper ]
- Feng Li & Yanfei Kang (2018). Improving forecasting performance using covariate-dependent copula models. International Journal of Forecasting, 34(3):456–476.
- 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) consortium. BMJ Open, 8(5):e020640.
- 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.
- [ Online version ]
- Feng Li (2013). Bayesian Modeling of Conditional Densities. Ph.D. thesis, Department of Statistics, Stockholm University. ISBN: 978-91-7447-665-1.
- Feng Li & Mattias Villani (2013). Efficient Bayesian multivariate surface regression. Scandinavian Journal of Statistics, 40(4):706–723.
- 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.
- Feng Li, Mattias Villani & Robert Kohn (2010). Flexible modeling of conditional distributions using smooth mixtures of asymmetric student t densities. Journal of Statistical Planning and Inference,140(12):3638–3654