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Customized Sequential Designs for Random Simulation Experiments: Kriging Metamodelling and Bootstrapping

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Author Info

  • Beers, W.C.M. van
  • Kleijnen, J.P.C.

    (Tilburg University, Center for Economic Research)

Abstract

This paper proposes a novel method to select an experimental design for interpolation in random simulation.(Though the paper focuses on Kriging, this method may also apply to other types of metamodels such as linear regression models.)Assuming that simulation requires much computer time, it is important to select a design with a small number of observations (or simulation runs).The proposed method is therefore sequential.Its novelty is that it accounts for the specific input/output behavior (or response function) of the particular simulation at hand; i.e., the method is customized or application-driven.A tool for this customization is bootstrapping, which enables the estimation of the variances of predictions for inputs not yet simulated.The new method is tested through the classic M/M/1 queueing simulation.For this simulation the novel design indeed gives better results than a Latin Hypercube Sampling (LHS) with a prefixed sample of the same size.

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Bibliographic Info

Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2004-63.

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Date of creation: 2004
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Handle: RePEc:dgr:kubcen:200463

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Web page: http://center.uvt.nl

Related research

Keywords: simulation; statistical methods; bootstrap;

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Cited by:
  1. Kleijnen, Jack P.C. & Beers, W.C.M. van & Nieuwenhuyse, I. van, 2010. "Constrained optimization in simulation: A novel approach," Open Access publications from Tilburg University urn:nbn:nl:ui:12-3583585, Tilburg University.
  2. Hernandez, Andres F. & Grover, Martha A., 2013. "Error estimation properties of Gaussian process models in stochastic simulations," European Journal of Operational Research, Elsevier, vol. 228(1), pages 131-140.
  3. Kleijnen, J.P.C., 2007. "Simulation Experiments in Practice: Statistical Design and Regression Analysis," Discussion Paper 2007-30, Tilburg University, Center for Economic Research.
  4. Plischke, Elmar & Borgonovo, Emanuele & Smith, Curtis L., 2013. "Global sensitivity measures from given data," European Journal of Operational Research, Elsevier, vol. 226(3), pages 536-550.
  5. Arreola-Risa, Antonio & Giménez-García, Víctor M. & Martínez-Parra, José Luis, 2011. "Optimizing stochastic production-inventory systems: A heuristic based on simulation and regression analysis," European Journal of Operational Research, Elsevier, vol. 213(1), pages 107-118, August.

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