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Note: On the Value of Function Evaluation Location Information in Monte Carlo Simulation

Author

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  • Thomas J. DiCiccio

    (Department of Statistics, Stanford University, Stanford, California 94305)

  • Peter W. Glynn

    (Department of Operations Research, Stanford University, Stanford, California 94305)

Abstract

The point estimator used in naive Monte Carlo sampling weights all the computed function evaluations equally, and it does not take into account the precise locations at which the function evaluations are made. In this note, we consider one-dimensional integration problems in which the integrand is twice continuously differentiable. It is shown that if the weights are suitably modified to reflect the location information present in the sample, then the convergence rate of the Monte Carlo estimator can be dramatically improved from order n -1/2 to order n -2 , where n is the number of function evaluations computed.

Suggested Citation

  • Thomas J. DiCiccio & Peter W. Glynn, 1995. "Note: On the Value of Function Evaluation Location Information in Monte Carlo Simulation," Management Science, INFORMS, vol. 41(4), pages 733-737, April.
    • Handle: RePEc:inm:ormnsc:v:41:y:1995:i:4:p:733-737
    DOI: 10.1287/mnsc.41.4.733
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    Cited by:

    1. Hatem Ben-Ameur & Pierre L'Ecuyer & Christiane Lemieux, 2004. "Combination of General Antithetic Transformations and Control Variables," Mathematics of Operations Research, INFORMS, vol. 29(4), pages 946-960, November.

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