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Using Common Random Numbers and Control Variates in Multiple-Comparison Procedures

Author

Listed:
  • Wei-Ning Yang

    (Ohio State University, Columbus, Ohio)

  • Barry L. Nelson

    (Ohio State University, Columbus, Ohio)

Abstract

This paper considers the determination of the relative merits of two or more system designs via stochastic simulation experiments by constructing simultaneous interval estimates of certain differences in expected performance. Tukey's all-pairwise-comparisons procedure, Hsu's multiple-comparisons-with-the-best procedure, and Dunnett's multiple-comparisons-with-a-control procedure are standard methods for making such comparisons. We propose refinements for all three procedures through the use of two variance reduction techniques: common random numbers and control variates. We show that the proposed procedures are better than the standard multiple-comparison procedures in the sense that they have a larger probability of containing the true difference and, at the same time, not containing zero when a difference exists.

Suggested Citation

  • Wei-Ning Yang & Barry L. Nelson, 1991. "Using Common Random Numbers and Control Variates in Multiple-Comparison Procedures," Operations Research, INFORMS, vol. 39(4), pages 583-591, August.
    • Handle: RePEc:inm:oropre:v:39:y:1991:i:4:p:583-591
    DOI: 10.1287/opre.39.4.583
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    Cited by:

    1. Jing Xie & Peter I. Frazier & Stephen E. Chick, 2016. "Bayesian Optimization via Simulation with Pairwise Sampling and Correlated Prior Beliefs," Operations Research, INFORMS, vol. 64(2), pages 542-559, April.
    2. Michael C. Fu & Jian-Qiang Hu & Chun-Hung Chen & Xiaoping Xiong, 2007. "Simulation Allocation for Determining the Best Design in the Presence of Correlated Sampling," INFORMS Journal on Computing, INFORMS, vol. 19(1), pages 101-111, February.
    3. Diana M. Negoescu & Peter I. Frazier & Warren B. Powell, 2011. "The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery," INFORMS Journal on Computing, INFORMS, vol. 23(3), pages 346-363, August.
    4. L. Dai & C. H. Chen, 1997. "Rates of Convergence of Ordinal Comparison for Dependent Discrete Event Dynamic Systems," Journal of Optimization Theory and Applications, Springer, vol. 94(1), pages 29-54, July.
    5. Barry L. Nelson & David Goldsman, 2001. "Comparisons with a Standard in Simulation Experiments," Management Science, INFORMS, vol. 47(3), pages 449-463, March.
    6. Huashuai Qu & Ilya O. Ryzhov & Michael C. Fu & Zi Ding, 2015. "Sequential Selection with Unknown Correlation Structures," Operations Research, INFORMS, vol. 63(4), pages 931-948, August.

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