Feng Li - Google Scholar Citations

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	All	Since 2015
Citations	53	29
h-index	3	3
i10-index	1	1

201020112012201320142015201620172018201920202 4 9 3 6 6 4 3 13 3

Co-authors

Mattias VillaniProfessor of Statistics, Stockholm University and Linköping UniversityVerified email at stat.su.se
robert kohnProfessor of Economics University of New South WalesVerified email at unsw.edu.au
Yanfei KangSchool of Economics and Management, Beihang UniversityVerified email at buaa.edu.cn
Rob J HyndmanProfessor of Statistics, Monash UniversityVerified email at monash.edu
Bindu KalesanBoston UniversityVerified email at BU.EDU
Yi ZuoDepartment of Biostatistics, Vanderbilt UniversityVerified email at bu.edu
Fotios PetropoulosUniversity of BathVerified email at bath.ac.uk
Xixi LiThe University of ManchesterVerified email at postgrad.manchester.ac.uk
Xuening ZhuSchool of Data Science, Fudan UniversityVerified email at fudan.edu.cn
Hansheng WangGuanghua School of Management, Peking UniversityVerified email at gsm.pku.edu.cn
Evangelos SpiliotisForecasting & Strategy Unit, School of Electrical and Computer Engineering, National TechnicalVerified email at fsu.gr
Thiyanga TalagalaLecturer, University of Sri JayewardenepuraVerified email at sjp.ac.lk

Feng Li

School of Statistics and Mathematics, Central University of Finance and Economics

Verified email at cufe.edu.cn - Homepage

Bayesian Inference Statistical Computing Forecasting Econometrics


Title	Cited by	Year
Flexible modeling of conditional distributions using smooth mixtures of asymmetric student t densities F Li, M Villani, R Kohn Journal of Statistical Planning and Inference 140 (12), 3638-3654, 2010	35	2010
GRATIS: GeneRAting TIme Series with diverse and controllable characteristics Y Kang, RJ Hyndman, F Li arXiv preprint arXiv:1903.02787, 2019	6*	2019
Efficient Bayesian Multivariate Surface Regression F Li, M Villani Scandinavian Journal of Statistics, 2013	5	2013
Improving forecasting performance using covariate-dependent copula models F Li, Y Kang International Journal of Forecasting 34 (3), 456-476, 2018	3*	2018
Modelling conditional densities using finite smooth mixtures F Li, M Villani, R Kohn Mixture: Estimation and Applications (Wiley Series in Probability and …, 2011	3	2011
Cohort profile: The MULTI sTUdy Diabetes rEsearch (MULTITUDE) consortium EC Pino, Y Zuo, CM De Olivera, S Mahalingaiah, O Keiser, LL Moore, F Li, ... BMJ open 8 (5), e020640, 2018	1	2018
Déjà vu: forecasting with similarity Y Kang, E Spiliotis, F Petropoulos, N Athiniotis, F Li, V Assimakopoulos arXiv preprint arXiv:1909.00221, 2019		2019
FFORMPP: Feature-based forecast model performance prediction TS Talagala, F Li, Y Kang arXiv preprint arXiv:1908.11500, 2019		2019
Least Squares Approximation for a Distributed System X Zhu, F Li, H Wang arXiv preprint arXiv:1908.04904, 2019		2019
Que será será? The uncertainty estimation of feature-based time series forecasts X Wang, Y Kang, F Petropoulos, F Li arXiv preprint arXiv:1908.02891, 2019		2019
Forecasting with time series imaging X Li, Y Kang, F Li arXiv preprint arXiv:1904.08064, 2019		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. HM Bailey, Y Zuo, F Li, J Min, K Vaddiparti, M Prosperi, J Fagan, S Galea, ... PloS one 14 (11), e0225223-e0225223, 2019		2019
Credit risk clustering in a business group: which matters more, systematic or idiosyncratic risk? F Li, Z He Cogent Economics & Finance 7 (1), 1632528, 2019		2019
Distributed Statistical Computing for Big Data and Case Studies F Li Renmin University Press, 2016		2016
Bayesian Modeling of Conditional Densities F Li Department of Statistics, Stockholm University, 2013		2013

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