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Selecting the Best Alternative Based on Its Quantile

Author

Listed:
  • Demet Batur

    (College of Business Administration, University of Nebraska–Lincoln, Lincoln, Nebraska 68588)

  • F. Fred Choobineh

    (Electrical and Computer Engineering, University of Nebraska–Lincoln, Lincoln, Nebraska 68588)

Abstract

A value-at-risk, or quantile, is widely used as an appropriate investment selection measure for risk-conscious decision makers. We present two quantile-based sequential procedures—with and without consideration of equivalency between alternatives—for selecting the best alternative from a set of simulated alternatives. These procedures asymptotically guarantee a user-defined target probability of correct selection within a prespecified indifference zone. Experimental results demonstrate the trade-off between the indifference-zone size and the number of simulation iterations needed to render a correct selection while satisfying a desired probability of correct selection.

Suggested Citation

  • Demet Batur & F. Fred Choobineh, 2021. "Selecting the Best Alternative Based on Its Quantile," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 657-671, May.
    • Handle: RePEc:inm:orijoc:v:33:y:2021:i:2:p:657-671
    DOI: 10.1287/ijoc.2020.0965
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    References listed on IDEAS

    as
    1. Batur, D. & Choobineh, F., 2010. "A quantile-based approach to system selection," European Journal of Operational Research, Elsevier, vol. 202(3), pages 764-772, May.
    2. Hui Dong & Marvin K. Nakayama, 2017. "Quantile Estimation with Latin Hypercube Sampling," Operations Research, INFORMS, vol. 65(6), pages 1678-1695, December.
    3. Chun-Hung Chen & Stephen E. Chick & Loo Hay Lee & Nugroho A. Pujowidianto, 2015. "Ranking and Selection: Efficient Simulation Budget Allocation," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 45-80, Springer.
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    5. Seong-Hee Kim & Barry L. Nelson, 2006. "On the Asymptotic Validity of Fully Sequential Selection Procedures for Steady-State Simulation," Operations Research, INFORMS, vol. 54(3), pages 475-488, June.
    6. Falk, Michael, 1986. "On the estimation of the quantile density function," Statistics & Probability Letters, Elsevier, vol. 4(2), pages 69-73, March.
    7. David Goldsman & Seong-Hee Kim & William S. Marshall & Barry L. Nelson, 2002. "Ranking and Selection for Steady-State Simulation: Procedures and Perspectives," INFORMS Journal on Computing, INFORMS, vol. 14(1), pages 2-19, February.
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    10. Batur, D. & Choobineh, F., 2012. "Stochastic dominance based comparison for system selection," European Journal of Operational Research, Elsevier, vol. 220(3), pages 661-672.
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    13. Weiwei Fan & L. Jeff Hong & Barry L. Nelson, 2016. "Indifference-Zone-Free Selection of the Best," Operations Research, INFORMS, vol. 64(6), pages 1499-1514, December.
    14. Demet Batur & Lina Wang & F. Fred Choobineh, 2018. "Methods for System Selection Based on Sequential Mean–Variance Analysis," INFORMS Journal on Computing, INFORMS, vol. 30(4), pages 724-738, November.
    15. Peter I. Frazier, 2014. "A Fully Sequential Elimination Procedure for Indifference-Zone Ranking and Selection with Tight Bounds on Probability of Correct Selection," Operations Research, INFORMS, vol. 62(4), pages 926-942, August.
    16. L. Jeff Hong & Barry L. Nelson & Jie Xu, 2015. "Discrete Optimization via Simulation," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 9-44, Springer.
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