IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v220y2012i3p661-672.html
   My bibliography  Save this article

Stochastic dominance based comparison for system selection

Author

Listed:
  • Batur, D.
  • Choobineh, F.

Abstract

We present two complementing selection procedures for comparing simulated systems based on the stochastic dominance relationship of a performance metric of interest. The decision maker specifies an output quantile set representing a section of the distribution of the metric, e.g., downside or upside risks or central tendencies, as the basis for comparison. The first procedure compares systems over the quantile set of interest by a first-order stochastic dominance criterion. The systems that are deemed nondominant in the first procedure could be compared by a weaker almost first-order stochastic dominance criterion in the second procedure. Numerical examples illustrate the capabilities of the proposed procedures.

Suggested Citation

  • Batur, D. & Choobineh, F., 2012. "Stochastic dominance based comparison for system selection," European Journal of Operational Research, Elsevier, vol. 220(3), pages 661-672.
  • Handle: RePEc:eee:ejores:v:220:y:2012:i:3:p:661-672
    DOI: 10.1016/j.ejor.2012.02.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037722171200135X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2012.02.018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chen, E. Jack & Kelton, W. David, 2006. "Quantile and tolerance-interval estimation in simulation," European Journal of Operational Research, Elsevier, vol. 168(2), pages 520-540, January.
    2. 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.
    3. Moshe Leshno & Haim Levy, 2002. "Preferred by "All" and Preferred by "Most" Decision Makers: Almost Stochastic Dominance," Management Science, INFORMS, vol. 48(8), pages 1074-1085, August.
    4. P. Heidelberger & P. A. W. Lewis, 1984. "Quantile Estimation in Dependent Sequences," Operations Research, INFORMS, vol. 32(1), pages 185-209, February.
    5. Stephen E. Chick & Koichiro Inoue, 2001. "New Two-Stage and Sequential Procedures for Selecting the Best Simulated System," Operations Research, INFORMS, vol. 49(5), pages 732-743, October.
    6. Pichitlamken, Juta & Nelson, Barry L. & Hong, L. Jeff, 2006. "A sequential procedure for neighborhood selection-of-the-best in optimization via simulation," European Journal of Operational Research, Elsevier, vol. 173(1), pages 283-298, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Ng, Pin & Wong, Wing-Keung & Xiao, Zhijie, 2017. "Stochastic dominance via quantile regression with applications to investigate arbitrage opportunity and market efficiency," European Journal of Operational Research, Elsevier, vol. 261(2), pages 666-678.
    3. Montes, Ignacio & Miranda, Enrique & Montes, Susana, 2014. "Decision making with imprecise probabilities and utilities by means of statistical preference and stochastic dominance," European Journal of Operational Research, Elsevier, vol. 234(1), pages 209-220.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. 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.
    3. Christos Alexopoulos & David Goldsman & Anup C. Mokashi & Kai-Wen Tien & James R. Wilson, 2019. "Sequest: A Sequential Procedure for Estimating Quantiles in Steady-State Simulations," Operations Research, INFORMS, vol. 67(4), pages 1162-1183, July.
    4. Zhongshun Shi & Yijie Peng & Leyuan Shi & Chun-Hung Chen & Michael C. Fu, 2022. "Dynamic Sampling Allocation Under Finite Simulation Budget for Feasibility Determination," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 557-568, January.
    5. 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.
    6. 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.
    7. Tsai, Shing Chih & Chu, I-Hao, 2012. "Controlled multistage selection procedures for comparison with a standard," European Journal of Operational Research, Elsevier, vol. 223(3), pages 709-721.
    8. Hyeong Soo Chang & Jiaqiao Hu, 2012. "On the Probability of Correct Selection in Ordinal Comparison over Dynamic Networks," Journal of Optimization Theory and Applications, Springer, vol. 155(2), pages 594-604, November.
    9. Guangwu Liu & Liu Jeff Hong, 2009. "Kernel estimation of quantile sensitivities," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(6), pages 511-525, September.
    10. Hooi Hooi Lean & Michael McAleer & Wing-Keung Wong, 2013. "Risk-averse and Risk-seeking Investor Preferences for Oil Spot and Futures," Documentos de Trabajo del ICAE 2013-31, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico, revised Aug 2013.
    11. Lee, Loo Hay & Chew, Ek Peng & Manikam, Puvaneswari, 2006. "A general framework on the simulation-based optimization under fixed computing budget," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1828-1841, November.
    12. Lizyayev, Andrey & Ruszczyński, Andrzej, 2012. "Tractable Almost Stochastic Dominance," European Journal of Operational Research, Elsevier, vol. 218(2), pages 448-455.
    13. Mingbin Ben Feng & Eunhye Song, 2020. "Efficient Nested Simulation Experiment Design via the Likelihood Ratio Method," Papers 2008.13087, arXiv.org, revised May 2024.
    14. Moshe Levy, 2020. "Comment on “Aging Population, Retirement, and Risk Taking”," Management Science, INFORMS, vol. 66(6), pages 2787-2791, June.
    15. Abdelbari El Khamlichi & Thi Hong Van Hoang & Wing‐keung Wong, 2016. "Is Gold Different for Islamic and Conventional Portfolios? A Sectorial Analysis," Post-Print hal-02965765, HAL.
    16. Bali, Turan G. & Demirtas, K. Ozgur & Levy, Haim & Wolf, Avner, 2009. "Bonds versus stocks: Investors' age and risk taking," Journal of Monetary Economics, Elsevier, vol. 56(6), pages 817-830, September.
    17. Bi, Hongwei & Huang, Rachel J. & Tzeng, Larry Y. & Zhu, Wei, 2019. "Higher-order Omega: A performance index with a decision-theoretic foundation," Journal of Banking & Finance, Elsevier, vol. 100(C), pages 43-57.
    18. Teng Wah Leo, 2017. "On the asymptotic distribution of (generalized) Lorenz transvariation measures," METRON, Springer;Sapienza Università di Roma, vol. 75(2), pages 195-213, August.
    19. George J Borjas & Ilpo Kauppinen & Panu Poutvaara, 2019. "Self-selection of Emigrants: Theory and Evidence on Stochastic Dominance in Observable and Unobservable Characteristics," The Economic Journal, Royal Economic Society, vol. 129(617), pages 143-171.
    20. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections," JRFM, MDPI, vol. 11(1), pages 1-29, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:220:y:2012:i:3:p:661-672. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

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