IDEAS home Printed from https://ideas.repec.org/d/dbpkucn.html

Publications

by members of

Peking University → Guanghua School of Management → Department of Business Statistics and Econometrics

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, arXiv.org, number 2204.08283, Apr, revised Aug 2022.
  2. Bohan Zhang & Yanfei Kang & Anastasios Panagiotelis & Feng Li, 2022, "Optimal reconciliation with immutable forecasts," Papers, arXiv.org, number 2204.09231, Apr.

2021

  1. Li Li & Yanfei Kang & Feng Li, 2021, "Bayesian forecast combination using time-varying features," Papers, arXiv.org, number 2108.02082, Aug, 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, arXiv.org, number 2012.03854, Dec, revised Jan 2022.
    • 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, volume 38, issue 3, pages 705-871, DOI: 10.1016/j.ijforecast.2021.11.001.
  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, Monash University, Department of Econometrics and Business Statistics, number 29/20.

2019

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

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, Monash University, Department of Econometrics and Business Statistics, number 15/18.

2014

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

2010

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

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, Sveriges Riksbank (Central Bank of Sweden), number 233, Oct.

2008

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

Journal articles

2023

  1. Zhang, Bohan & Kang, Yanfei & Panagiotelis, Anastasios & Li, Feng, 2023, "Optimal reconciliation with immutable forecasts," European Journal of Operational Research, Elsevier, volume 308, issue 2, pages 650-660, DOI: 10.1016/j.ejor.2022.11.035.
  2. Wang, Xiaoqian & Kang, Yanfei & Hyndman, Rob J. & Li, Feng, 2023, "Distributed ARIMA models for ultra-long time series," International Journal of Forecasting, Elsevier, volume 39, issue 3, pages 1163-1184, DOI: 10.1016/j.ijforecast.2022.05.001.
  3. Li, Li & Kang, Yanfei & Li, Feng, 2023, "Bayesian forecast combination using time-varying features," International Journal of Forecasting, Elsevier, volume 39, issue 3, pages 1287-1302, DOI: 10.1016/j.ijforecast.2022.06.002.
  4. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023, "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, volume 39, issue 4, pages 1518-1547, DOI: 10.1016/j.ijforecast.2022.11.005.
  5. 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, volume 61, issue 22, pages 7557-7572, November, DOI: 10.1080/00207543.2022.2153941.

2022

  1. Kang, Yanfei & Cao, Wei & Petropoulos, Fotios & Li, Feng, 2022, "Forecast with forecasts: Diversity matters," European Journal of Operational Research, Elsevier, volume 301, issue 1, pages 180-190, DOI: 10.1016/j.ejor.2021.10.024.
  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, volume 38, issue 3, pages 705-871, DOI: 10.1016/j.ijforecast.2021.11.001.
    • 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, arXiv.org, number 2012.03854, Dec, revised Jan 2022.
  3. Talagala, Thiyanga S. & Li, Feng & Kang, Yanfei, 2022, "FFORMPP: Feature-based forecast model performance prediction," International Journal of Forecasting, Elsevier, volume 38, issue 3, pages 920-943, DOI: 10.1016/j.ijforecast.2021.07.002.
  4. Anderer, Matthias & Li, Feng, 2022, "Hierarchical forecasting with a top-down alignment of independent-level forecasts," International Journal of Forecasting, Elsevier, volume 38, issue 4, pages 1405-1414, DOI: 10.1016/j.ijforecast.2021.12.015.
  5. 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, volume 40, issue 4, pages 1691-1700, October, DOI: 10.1080/07350015.2021.1961789.
  6. 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, volume 73, issue 5, pages 979-993, May, DOI: 10.1080/01605682.2021.1880297.

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, volume 132, issue C, pages 719-731, DOI: 10.1016/j.jbusres.2020.10.051.

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, volume 14, issue 11, pages 1-18, November, DOI: 10.1371/journal.pone.0225223.
  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, volume 7, issue 1, pages 1632528-163, January, DOI: 10.1080/23322039.2019.1632528.

2018

  1. Li, Feng & Kang, Yanfei, 2018, "Improving forecasting performance using covariate-dependent copula models," International Journal of Forecasting, Elsevier, volume 34, issue 3, pages 456-476, DOI: 10.1016/j.ijforecast.2018.01.007.

2016

  1. Li, Shuo & Tu, Yundong, 2016, "On estimating the nonparametric multiplicative error models," Economics Letters, Elsevier, volume 143, issue C, pages 66-68, DOI: 10.1016/j.econlet.2016.03.023.

2015

  1. Liangjun Su & Yundong Tu & Aman Ullah, 2015, "Testing Additive Separability of Error Term in Nonparametric Structural Models," Econometric Reviews, Taylor & Francis Journals, volume 34, issue 6-10, pages 1057-1088, December, DOI: 10.1080/07474938.2014.956621.
  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, volume 33, issue 3, pages 393-402, July, DOI: 10.1080/07350015.2014.955174.

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, volume 182, issue 1, pages 196-210, DOI: 10.1016/j.jeconom.2014.04.018.

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, volume 40, issue 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, volume 62, issue 1, pages 109-116, February, DOI: 10.1007/s10463-009-0250-4.
  2. Zhang, Hao Helen & Lu, Wenbin & Wang, Hansheng, 2010, "On sparse estimation for semiparametric linear transformation models," Journal of Multivariate Analysis, Elsevier, volume 101, issue 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, volume 71, issue 3, pages 671-683, June, DOI: 10.1111/j.1467-9868.2008.00693.x.
  2. Wang, Hansheng & Xia, Yingcun, 2009, "Shrinkage Estimation of the Varying Coefficient Model," Journal of the American Statistical Association, American Statistical Association, volume 104, issue 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, volume 27, issue 5, pages 409-419.
  2. Da Huang & Hansheng Wang & Qiwei Yao, 2008, "Estimating GARCH models: when to use what?," Econometrics Journal, Royal Economic Society, volume 11, issue 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, volume 103, pages 811-821, June.
  4. Wang, Hansheng & Leng, Chenlei, 2008, "A note on adaptive group lasso," Computational Statistics & Data Analysis, Elsevier, volume 52, issue 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, volume 1, issue 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, volume 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, volume 51, issue 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, volume 69, issue 1, pages 63-78, February, DOI: 10.1111/j.1467-9868.2007.00577.x.
  4. Wang, Hansheng & Leng, Chenlei, 2007, "Unified LASSO Estimation by Least Squares Approximation," Journal of the American Statistical Association, American Statistical Association, volume 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, volume 94, issue 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, volume 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, Palgrave Macmillan, chapter 0, in: Mohsen Hamoudia & Spyros Makridakis & Evangelos Spiliotis, "Forecasting with Artificial Intelligence", DOI: 10.1007/978-3-031-35879-1_10.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.