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Efficient Sampling Allocation Procedures for Optimal Quantile Selection

Author

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  • Yijie Peng

    (Department of Management Science and Information Systems, Guanghua School of Management, Peking University, Beijing 100871, China)

  • Chun-Hung Chen

    (Department of Systems Engineering and Operations Research, George Mason University, Fairfax, Virginia 22030)

  • Michael C. Fu

    (Institute for Systems Research, Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742)

  • Jian-Qiang Hu

    (Department of Management Science, Fudan University, Shanghai 200433, China)

  • Ilya O. Ryzhov

    (Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742)

Abstract

We propose a dynamic sampling allocation and selection paradigm for finding the alternative with the optimal quantile in a Bayesian framework. Myopic allocation policies (MAPs), analogous to existing methods in classic ranking and selection for selecting the alternative with the optimal mean, and computationally efficient selection policies are derived for selecting the alternative with the optimal quantile. Under certain conditions, we prove that the proposed MAPs and selection procedures are consistent, which means that the best quantile would be eventually correctly selected as the sample size goes to infinity. Numerical experiments demonstrate that the proposed schemes can significantly improve the performance.

Suggested Citation

  • Yijie Peng & Chun-Hung Chen & Michael C. Fu & Jian-Qiang Hu & Ilya O. Ryzhov, 2021. "Efficient Sampling Allocation Procedures for Optimal Quantile Selection," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 230-245, January.
    • Handle: RePEc:inm:orijoc:v:33:y:2021:i:1:p:230-245
    DOI: 10.1287/ijoc.2019.0946
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    References listed on IDEAS

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    Cited by:

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    2. Cheng, Zhenxia & Luo, Jun & Wu, Ruijing, 2023. "On the finite-sample statistical validity of adaptive fully sequential procedures," European Journal of Operational Research, Elsevier, vol. 307(1), pages 266-278.
    3. Dongwook Shin & Mark Broadie & Assaf Zeevi, 2022. "Practical Nonparametric Sampling Strategies for Quantile-Based Ordinal Optimization," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 752-768, March.

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