Feng Li, Ph.D.

Assistant Professor, Associate Dean 
School of Statistics and Mathematics 
Central University of Finance and Economics 
100081 Beijing, China

Email: feng.li@cufe.edu.cn 
Phone: +86-(0)10-6228-8548 (Haidian Campus)
       +86-(0)10-6177-6189 (Shahe Campus)

Education 🎓

• Ph.D., 2013, Statistics, Stockholm University, Sweden.
  • Thesis: Bayesian Modeling of Conditional Densities
  • Supervisor: Prof. Mattias Villani
  • Thesis opponent : Prof. Sylvia Frühwirth-Schnatter
• M.S., 2008,  Statistics, Dalarna University, Sweden.
• B.S., 2007, Statistics, Renmin University of China.

Curriculum Vitae

• English version CV (PDF)
• Chinese version CV (PDF)

Research interests👨‍🔬

• Bayesian statistics
• Econometrics and forecasting
• Multivariate statistics
• Complex models and big data

Working Papers⏳

• Least Squares Approximation for a Distributed System (with Xuening Zhu and Hansheng Wang)
  • [Working Paper | Spark Implementation]
• Que será será? The uncertainty estimation of feature-based time series forecasts (with Xiaoqian Wang, Yanfei Kang and Fotios Petropoulos)
  • [Working Paper | Que será será]
• Forecasting with time series imaging (with Xixi Li and Yanfei Kang)
  • [Working Paper ]
• GRATIS: GeneRAtion of TIme Series with diverse and controllable characteristics (with Yanfei Kang and Rob J. Hyndman)
  • [ Working Paper | R Package | Web App]

Research Grants

• Efficient Bayesian Flexible Density Methods with High Dimensional Financial Data funded by National Natural Science Foundation of China, (2016-). Principal investigator.
• Bayesian Multivariate Density Estimation Methods for Complex Data funded by Ministry of Education, China (2014-). Principal investigator
  

Publications 🖺

• Li, F., and He, Z. (2019) Credit Risk Clustering in a Business Group: Which Matters More, Systematic or Idiosyncratic Risk? Cogent Economics and Finance, 7(1632528).
  [Journal version | Working Paper | Appendix ]
• Li, F., and Kang, Y.(2018). Improving forecasting performance using covariate-dependent copula models, International Journal of Forecasting, 34(3), pp. 456-476.
  [Journal’s version | Working Paper | Paper on arXiv]
• Li, F., Kalesan, B., Zuo, Y.  et. al (2018) Cohort Profile: The MULTI sTUdy Diabetes rEsearch (MULTITUDE) Consortium, BMJ Open, 8(5), pp. e020640.
  [Journal’s version]
• Li, F. (2016). Distributed Statistical Computing for Big Data and Case Studies, ISBN：9787300230276
  [Available on JD.com and Amazon.cn]
• Li, F. (2013). Bayesian Modeling of Conditional Densities. Ph.D. thesis, Department of Statistics, Stockholm University. ISBN: 978-91-7447-665-1
  [Link to DiVA | Fulltext]
• Li, F, and Villani, M. (2013). Efficient Bayesian Multivariate Surface Regression, Scandinavian Journal of Statistics, 40(4), pp. 706-723.
  [Journal’s version | Paper on arXiv | Talk | Poster]
• Li, F, Villani, M. and Kohn, R. (2011). Modeling Conditional Densities using Finite Smooth Mixtures, Book chapter in Mixture Estimation and Applications (Mengersen, K.L., Robert, C. P. and Titterington, D.M. eds), Wiley.
  [Publisher’s version | Working Paper]
• Li, F, Villani, M. and Kohn, R. (2010). Flexible Modeling of Conditional Distributions using Smooth Mixtures of Asymmetric Student T Densities, Journal of Statistical Planning and Inference, 140(12), pp. 3638-3654.
  [Journal’s version | Working Paper | Talk | Poster]

Awards 🌟

• The 2014 Cramér Prize, Mar 2014.
• International Society for Bayesian Analysis junior travel award, Jun, 2012.
• Travel grant from The Swedish Knut and Alice Wallenberg Foundation, Aug, 2011.
• Outstanding graduate student, honored by Beijing Municipal Education Commission, Jul, 2007, China.