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Publications

by members of

Department of Business Statistics and Econometrics
Guanghua School of Management
Peking University
Beijing, China

These are publications listed in RePEc written by members of the above institution who are registered with the RePEc Author Service. Thus this compiles the works all those currently affiliated with this institution, not those affilated at the time of publication. List of registered members. Register yourself. Citation analysis. Find also a compilation of publications from alumni here.

This page is updated in the first days of each month.

| Working papers | Journal articles | Chapters |

Working papers

2022

  1. Li Li & Yanfei Kang & Fotios Petropoulos & Feng Li, 2022. "Feature-based intermittent demand forecast combinations: bias, accuracy and inventory implications," Papers 2204.08283, arXiv.org, revised Aug 2022.
  2. Bohan Zhang & Yanfei Kang & Anastasios Panagiotelis & Feng Li, 2022. "Optimal reconciliation with immutable forecasts," Papers 2204.09231, arXiv.org.

2021

  1. Li Li & Yanfei Kang & Feng Li, 2021. "Bayesian forecast combination using time-varying features," Papers 2108.02082, arXiv.org, revised Jun 2022.

2020

  1. 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 & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
  2. Xiaoqian Wang & Yanfei Kang & Rob J Hyndman & Feng Li, 2020. "Distributed ARIMA Models for Ultra-long Time Series," Monash Econometrics and Business Statistics Working Papers 29/20, Monash University, Department of Econometrics and Business Statistics.

2019

  1. Thiyanga S. Talagala & Feng Li & Yanfei Kang, 2019. "Feature-based Forecast-Model Performance Prediction," Monash Econometrics and Business Statistics Working Papers 21/19, Monash University, Department of Econometrics and Business Statistics.

2018

  1. Yanfei Kang & Rob J Hyndman & Feng Li, 2018. "Efficient generation of time series with diverse and controllable characteristics," Monash Econometrics and Business Statistics Working Papers 15/18, Monash University, Department of Econometrics and Business Statistics.

2014

  1. Chen, Song Xi & Lei, Lihua & Tu, Yundong, 2014. "Functional Coefficient Moving Average Model with Applications to forecasting Chinese CPI," MPRA Paper 67074, University Library of Munich, Germany, revised 2015.
  2. Tae-Hwy Lee & Yundong Tu & Aman Ullah, 2014. "Nonparametric and Semiparametric Regressions Subject to Monotonicity Constraints: Estimation and Forecasting," Working Papers 201404, University of California at Riverside, Department of Economics.
  3. Tae-Hwy Lee & Yundong Tu & Aman Ullah, 2014. "Forecasting Equity Premium: Global Historical Average versus Local Historical Average and Constraints," Working Papers 201405, University of California at Riverside, Department of Economics.

2010

  1. Li, Feng & Villani, Mattias & Kohn, Robert, 2010. "Modeling Conditional Densities Using Finite Smooth Mixtures," Working Paper Series 245, Sveriges Riksbank (Central Bank of Sweden).

2009

  1. Li, Feng & Villani, Mattias & Kohn, Robert, 2009. "Flexible Modeling of Conditional Distributions Using Smooth Mixtures of Asymmetric Student T Densities," Working Paper Series 233, Sveriges Riksbank (Central Bank of Sweden).

2008

  1. Richard Arnott & Yundong Tu, 2008. "Shopper City," Working Papers 200811, University of California at Riverside, Department of Economics, revised Aug 2008.

Journal articles

2024

  1. Feng Li & Haibo Wang & Canyi Du & Ziqin Lan & Feifei Yu & Ying Rong, 2024. "Probabilistic Prediction Model for Expressway Traffic Noise Based on Short-Term Monitoring Data," Sustainability, MDPI, vol. 16(16), pages 1-17, August.

2023

  1. Zhang, Bohan & Kang, Yanfei & Panagiotelis, Anastasios & Li, Feng, 2023. "Optimal reconciliation with immutable forecasts," European Journal of Operational Research, Elsevier, vol. 308(2), pages 650-660.
  2. Wang, Xiaoqian & Kang, Yanfei & Hyndman, Rob J. & Li, Feng, 2023. "Distributed ARIMA models for ultra-long time series," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1163-1184.
  3. Li, Li & Kang, Yanfei & Li, Feng, 2023. "Bayesian forecast combination using time-varying features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1287-1302.
  4. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
  5. Yijiang Zhao & Yahan Ning & Haodong Li & Zhuhua Liao & Yizhi Liu & Feng Li, 2023. "MSC-DeepFM: OSM Road Type Prediction via Integrating Spatial Context Using DeepFM," Sustainability, MDPI, vol. 15(24), pages 1-17, December.
  6. Li Li & Yanfei Kang & Fotios Petropoulos & Feng Li, 2023. "Feature-based intermittent demand forecast combinations: accuracy and inventory implications," International Journal of Production Research, Taylor & Francis Journals, vol. 61(22), pages 7557-7572, November.

2022

  1. Kang, Yanfei & Cao, Wei & Petropoulos, Fotios & Li, Feng, 2022. "Forecast with forecasts: Diversity matters," European Journal of Operational Research, Elsevier, vol. 301(1), pages 180-190.
  2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
    • 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 & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
  3. Talagala, Thiyanga S. & Li, Feng & Kang, Yanfei, 2022. "FFORMPP: Feature-based forecast model performance prediction," International Journal of Forecasting, Elsevier, vol. 38(3), pages 920-943.
  4. Anderer, Matthias & Li, Feng, 2022. "Hierarchical forecasting with a top-down alignment of independent-level forecasts," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1405-1414.
  5. Chengjiang Han & Feng Li & Bizhen Lian & Tomas Vencúrik & Wei Liang, 2022. "Relationships between Perfectionism, Extra Training and Academic Performance in Chinese Collegiate Athletes: Mediating Role of Achievement Motivation," IJERPH, MDPI, vol. 19(17), pages 1-14, August.
  6. Rui Pan & Tunan Ren & Baishan Guo & Feng Li & Guodong Li & Hansheng Wang, 2022. "A Note on Distributed Quantile Regression by Pilot Sampling and One-Step Updating," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1691-1700, October.
  7. Xiaoqian Wang & Yanfei Kang & Fotios Petropoulos & Feng Li, 2022. "The uncertainty estimation of feature-based forecast combinations," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(5), pages 979-993, May.

2021

  1. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.

2019

  1. 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, Public Library of Science, vol. 14(11), pages 1-18, November.
  2. Feng Li & Zhuojing He, 2019. "Credit risk clustering in a business group: Which matters more, systematic or idiosyncratic risk?," Cogent Economics & Finance, Taylor & Francis Journals, vol. 7(1), pages 1632528-163, January.

2018

  1. Li, Feng & Kang, Yanfei, 2018. "Improving forecasting performance using covariate-dependent copula models," International Journal of Forecasting, Elsevier, vol. 34(3), pages 456-476.

2016

  1. Li, Shuo & Tu, Yundong, 2016. "On estimating the nonparametric multiplicative error models," Economics Letters, Elsevier, vol. 143(C), pages 66-68.

2015

  1. Liangjun Su & Yundong Tu & Aman Ullah, 2015. "Testing Additive Separability of Error Term in Nonparametric Structural Models," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 1057-1088, December.
  2. Tae-Hwy Lee & Yundong Tu & Aman Ullah, 2015. "Forecasting Equity Premium: Global Historical Average Versus Local Historical Average and Constraints," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 393-402, July.

2014

  1. Lee, Tae-Hwy & Tu, Yundong & Ullah, Aman, 2014. "Nonparametric and semiparametric regressions subject to monotonicity constraints: Estimation and forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 196-210.

2013

  1. Feng Li & Mattias Villani, 2013. "Efficient Bayesian Multivariate Surface Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 706-723, December.

2010

  1. Chih-Ling Tsai & Hansheng Wang & Ning Zhu, 2010. "Does a Bayesian approach generate robust forecasts? Evidence from applications in portfolio investment decisions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 109-116, February.
  2. Zhang, Hao Helen & Lu, Wenbin & Wang, Hansheng, 2010. "On sparse estimation for semiparametric linear transformation models," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1594-1606, August.

2009

  1. Hansheng Wang & Bo Li & Chenlei Leng, 2009. "Shrinkage tuning parameter selection with a diverging number of parameters," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 671-683, June.
  2. Wang, Hansheng & Xia, Yingcun, 2009. "Shrinkage Estimation of the Varying Coefficient Model," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 747-757.

2008

  1. Jiang, Guohua & Wang, Hansheng, 2008. "Should earnings thresholds be used as delisting criteria in stock market?," Journal of Accounting and Public Policy, Elsevier, vol. 27(5), pages 409-419.
  2. Da Huang & Hansheng Wang & Qiwei Yao, 2008. "Estimating GARCH models: when to use what?," Econometrics Journal, Royal Economic Society, vol. 11(1), pages 27-38, March.
  3. Wang, Hansheng & Xia, Yingcun, 2008. "Sliced Regression for Dimension Reduction," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 811-821, June.
  4. Wang, Hansheng & Leng, Chenlei, 2008. "A note on adaptive group lasso," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5277-5286, August.
  5. Ronghua Luo & Hansheng Wang, 2008. "A composite logistic regression approach for ordinal panel data regression," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 1(1), pages 29-43.

2007

  1. Wang, Hansheng & Li, Guodong & Jiang, Guohua, 2007. "Robust Regression Shrinkage and Consistent Variable Selection Through the LAD-Lasso," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 347-355, July.
  2. Wang, Hansheng, 2007. "A note on iterative marginal optimization: a simple algorithm for maximum rank correlation estimation," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2803-2812, March.
  3. Hansheng Wang & Guodong Li & Chih‐Ling Tsai, 2007. "Regression coefficient and autoregressive order shrinkage and selection via the lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(1), pages 63-78, February.
  4. Wang, Hansheng & Leng, Chenlei, 2007. "Unified LASSO Estimation by Least Squares Approximation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1039-1048, September.
  5. Hansheng Wang & Runze Li & Chih-Ling Tsai, 2007. "Tuning parameter selectors for the smoothly clipped absolute deviation method," Biometrika, Biometrika Trust, vol. 94(3), pages 553-568.

2002

  1. Shao J. & Wang H., 2002. "Sample Correlation Coefficients Based on Survey Data Under Regression Imputation," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 544-552, June.

Chapters

2023

  1. Li Li & Feng Li & Yanfei Kang, 2023. "Forecasting Large Collections of Time Series: Feature-Based Methods," Palgrave Advances in Economics of Innovation and Technology, in: Mohsen Hamoudia & Spyros Makridakis & Evangelos Spiliotis (ed.), Forecasting with Artificial Intelligence, chapter 0, pages 251-276, Palgrave Macmillan.

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