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Control-Variate Models of Common Random Numbers for Multiple Comparisons with the Best

Author

Listed:
  • Barry L. Nelson

    (Department of Industrial and Systems Engineering, Ohio State University, Columbus, Ohio 43210)

  • Jason C. Hsu

    (Department of Statistics, Ohio State University, Columbus, Ohio 43210)

Abstract

Using common random numbers (CRN) in simulation experiment design is known to reduce the variance of estimators of differences in system performance. However, when more than two systems are compared, exact simultaneous statistical inference in conjunction with CRN is typically impossible. We introduce control-variate models of CRN that permit exact statistical inference, specifically multiple comparisons with the best. These models explain the effect of CRN via a linear regression of the simulation output on "control variates" that are functions of the simulation inputs. We establish theoretically, and illustrate empirically, that the control-variate models lead to sharper statistical inference in the sense that the probability of detecting differences in systems' performance is increased.

Suggested Citation

  • Barry L. Nelson & Jason C. Hsu, 1993. "Control-Variate Models of Common Random Numbers for Multiple Comparisons with the Best," Management Science, INFORMS, vol. 39(8), pages 989-1001, August.
    • Handle: RePEc:inm:ormnsc:v:39:y:1993:i:8:p:989-1001
    DOI: 10.1287/mnsc.39.8.989
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    Cited by:

    1. Tsai, Shing Chih, 2011. "Selecting the best simulated system with weighted control-variate estimators," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(4), pages 705-717.

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