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A Coverage Function for Interval Estimators of Simulation Response

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  • Lee W. Schruben

    (Cornell University)

Abstract

The coverage function presented here measures confidence interval robustness. It is suggested that this function be used in the analysis of empirical interval estimator studies. Some approaches for determining appropriate sample sizes in such experiments are also discussed. A short study of two procedures for constructing confidence intervals for a simulation response is offered as an example.

Suggested Citation

  • Lee W. Schruben, 1980. "A Coverage Function for Interval Estimators of Simulation Response," Management Science, INFORMS, vol. 26(1), pages 18-27, January.
    • Handle: RePEc:inm:ormnsc:v:26:y:1980:i:1:p:18-27
    DOI: 10.1287/mnsc.26.1.18
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    File URL: http://dx.doi.org/10.1287/mnsc.26.1.18
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    Citations

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    Cited by:

    1. Kleijnen, J.P.C., 1984. "Superefficient estimation of power functions in simulation experiments," Other publications TiSEM 559fbcc7-e1d5-44c5-a658-a, Tilburg University, School of Economics and Management.
    2. Marrel, Amandine & Iooss, Bertrand, 2024. "Probabilistic surrogate modeling by Gaussian process: A new estimation algorithm for more robust prediction," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    3. Barry L. Nelson, 2004. "50th Anniversary Article: Stochastic Simulation Research in Management Science," Management Science, INFORMS, vol. 50(7), pages 855-868, July.
    4. Marrel, Amandine & Iooss, Bertrand, 2024. "Probabilistic surrogate modeling by Gaussian process: A review on recent insights in estimation and validation," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    5. Dashi I. Singham & Lee W. Schruben, 2012. "Finite-Sample Performance of Absolute Precision Stopping Rules," INFORMS Journal on Computing, INFORMS, vol. 24(4), pages 624-635, November.

    More about this item

    Keywords

    simulation; confidence intervals;

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