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Ranking Model Averaging: Ranking Based on Model Averaging

Author

Listed:
  • Ziheng Feng

    (School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing 100124, China)

  • Baihua He

    (International Institute of Finance, School of Management, University of Science and Technology of China, Hefei 230026, China)

  • Tianfa Xie

    (School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing 100124, China)

  • Xinyu Zhang

    (International Institute of Finance, School of Management, University of Science and Technology of China, Hefei 230026, China; and Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China)

  • Xianpeng Zong

    (School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing 100124, China)

Abstract

Ranking problems are commonly encountered in practical applications, including order priority ranking, wine quality ranking, and piston slap noise performance ranking. The responses of these ranking applications are often considered as continuous responses, and there is uncertainty on which scoring function is used to model the responses. In this paper, we address the scoring function uncertainty of continuous response ranking problems by proposing a ranking model averaging (RMA) method. With a set of candidate models varied by scoring functions, RMA assigns weights for each model determined by a K -fold crossvalidation criterion based on pairwise loss. We provide two main theoretical properties for RMA. First, we prove that the averaging ranking predictions of RMA are asymptotically optimal in achieving the lowest possible ranking risk. Second, we provide a bound on the difference between the empirical RMA weights and theoretical optimal ones, and we show that RMA weights are consistent. Simulation results validate RMA superiority over competing methods in reducing ranking risk. Moreover, when applied to empirical examples—order priority, wine quality, and piston slap noise—RMA shows its effectiveness in building accurate ranking systems.

Suggested Citation

  • Ziheng Feng & Baihua He & Tianfa Xie & Xinyu Zhang & Xianpeng Zong, 2025. "Ranking Model Averaging: Ranking Based on Model Averaging," INFORMS Journal on Computing, INFORMS, vol. 37(3), pages 703-722, May.
    • Handle: RePEc:inm:orijoc:v:37:y:2025:i:3:p:703-722
    DOI: 10.1287/ijoc.2023.0257
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    References listed on IDEAS

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    1. Phillips, Robert F, 1987. "Composite Forecasting: An Integrated Approach and Optimality Reconsidered," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(3), pages 389-395, July.
    2. Tomohiro Ando & Ker-Chau Li, 2014. "A Model-Averaging Approach for High-Dimensional Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 254-265, March.
    3. Zhang, Xinyu & Wan, Alan T.K. & Zou, Guohua, 2013. "Model averaging by jackknife criterion in models with dependent data," Journal of Econometrics, Elsevier, vol. 174(2), pages 82-94.
    4. Baihua He & Shuangge Ma & Xinyu Zhang & Li-Xing Zhu, 2023. "Rank-Based Greedy Model Averaging for High-Dimensional Survival Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(544), pages 2658-2670, October.
    5. Xinyu Zhang & Yanyuan Ma & Raymond J. Carroll, 2019. "MALMEM: model averaging in linear measurement error models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(4), pages 763-779, September.
    6. Wan, Alan T.K. & Zhang, Xinyu & Zou, Guohua, 2010. "Least squares model averaging by Mallows criterion," Journal of Econometrics, Elsevier, vol. 156(2), pages 277-283, June.
    7. Yang Feng & Qingfeng Liu, 2020. "Nested Model Averaging on Solution Path for High-dimensional Linear Regression," Papers 2005.08057, arXiv.org.
    8. Ethan X. Fang & Yang Ning & Han Liu, 2017. "Testing and confidence intervals for high dimensional proportional hazards models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1415-1437, November.
    9. Xinyu Zhang & Huihang Liu & Yizheng Wei & Yanyuan Ma, 2024. "Prediction Using Many Samples with Models Possibly Containing Partially Shared Parameters," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(1), pages 187-196, January.
    10. Bruce E. Hansen, 2014. "Model averaging, asymptotic risk, and regressor groups," Quantitative Economics, Econometric Society, vol. 5(3), pages 495-530, November.
    11. Xinyu Zhang & Dalei Yu & Guohua Zou & Hua Liang, 2016. "Optimal Model Averaging Estimation for Generalized Linear Models and Generalized Linear Mixed-Effects Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1775-1790, October.
    12. Zhang, Xinyu & Liu, Chu-An, 2023. "Model averaging prediction by K-fold cross-validation," Journal of Econometrics, Elsevier, vol. 235(1), pages 280-301.
    13. Andrew Dickerson & Gurleen K. Popli, 2016. "Persistent poverty and children's cognitive development: evidence from the UK Millennium Cohort Study," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 535-558, February.
    14. Bruce E. Hansen, 2007. "Least Squares Model Averaging," Econometrica, Econometric Society, vol. 75(4), pages 1175-1189, July.
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