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Optimal Tests for Initialization Bias in Simulation Output

Author

Listed:
  • L. Schruben

    (Cornell University, Ithaca, New York)

  • H. Singh

    (Cornell University, Ithaca, New York)

  • L. Tierney

    (Carnegie-Mellon University, Pittsburgh, Pennsylvania)

Abstract

We present a family of tests for detecting initialization bias in the mean of a simulation output series using a hypothesis testing framework. The null hypothesis is that the output mean does not change throughout the simulation run. The alternative hypothesis specifies a general transient mean function. The tests are asymptotically optimal based on cumulative sums of deviations about the sample mean. A particular test in this family is applied to a variety of simulation models. The test requires very modest computation and appears to be both robust and powerful.

Suggested Citation

  • L. Schruben & H. Singh & L. Tierney, 1983. "Optimal Tests for Initialization Bias in Simulation Output," Operations Research, INFORMS, vol. 31(6), pages 1167-1178, December.
    • Handle: RePEc:inm:oropre:v:31:y:1983:i:6:p:1167-1178
    DOI: 10.1287/opre.31.6.1167
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    Citations

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    Cited by:

    1. Enver Yücesan, 1993. "Randomization tests for initialization bias in simulation output," Naval Research Logistics (NRL), John Wiley & Sons, vol. 40(5), pages 643-663, August.
    2. Robinson, Stewart, 2007. "A statistical process control approach to selecting a warm-up period for a discrete-event simulation," European Journal of Operational Research, Elsevier, vol. 176(1), pages 332-346, January.
    3. David Goldsman & Lee W. Schruben & James J. Swain, 1994. "Tests for transient means in simulated time series," Naval Research Logistics (NRL), John Wiley & Sons, vol. 41(2), pages 171-187, March.
    4. Jacobson, Sheldon H., 1997. "The effect of initial transient on the steady-state simulation harmonic analysis gradient estimators," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 43(2), pages 209-221.
    5. K Hoad & S Robinson & R Davies, 2010. "Automating warm-up length estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(9), pages 1389-1403, September.
    6. Martin Comis & Catherine Cleophas & Christina Büsing, 2021. "Patients, primary care, and policy: Agent-based simulation modeling for health care decision support," Health Care Management Science, Springer, vol. 24(4), pages 799-826, December.
    7. Kannan, V. R. & Ghosh, S., 1995. "Using dynamic cellular manufacturing to simplify scheduling in cell based production systems," Omega, Elsevier, vol. 23(4), pages 443-452, August.
    8. repec:jss:jstsof:21:i11 is not listed on IDEAS

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    Keywords

    767 statistical analysis;

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