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Multivariate Versus Univariate Kriging Metamodels for Multi-Response Simulation Models (Revision of 2012-039)

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Author Info

  • Kleijnen, Jack P.C.
  • Mehdad, E.

    (Tilburg University, Center for Economic Research)

Abstract

Abstract: To analyze the input/output behavior of simulation models with multiple responses, we may apply either univariate or multivariate Kriging (Gaussian process) metamodels. In multivariate Kriging we face a major problem: the covariance matrix of all responses should remain positive-de nite; we therefore use the recently proposed "non-separable dependence" model. To evaluate the performance of univariate and multivariate Kriging, we perform several Monte Carlo experiments that simulate Gaussian processes. These Monte Carlo results suggest that the simpler univariate Kriging gives smaller mean square error.

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Bibliographic Info

Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2014-012.

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Date of creation: 2014
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Handle: RePEc:dgr:kubcen:2014012

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

Related research

Keywords: Simulation; Stochastic processes; Multivariate statistics;

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  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. Kleijnen, J.P.C., 1993. "Simulation and optimization in production planning: a case study," Open Access publications from Tilburg University urn:nbn:nl:ui:12-369798, Tilburg University.
  3. 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.
  4. Kleijnen, Jack P.C. & Beers, Wim van & Nieuwenhuyse, Inneke van, 2010. "Constrained optimization in expensive simulation: Novel approach," European Journal of Operational Research, Elsevier, vol. 202(1), pages 164-174, April.
  5. Ham , G. van & Rotmans, J. & Kleijnen, J.P.C., 1992. "Techniques for sensitivity analysis of simulation models: A case study of the CO2 greenhouse effect," Open Access publications from Tilburg University urn:nbn:nl:ui:12-365610, Tilburg University.
  6. Kleijnen, J.P.C., 2008. "Review of the book [Design and Analysis of Simulation Experiments]," Open Access publications from Tilburg University urn:nbn:nl:ui:12-4379049, Tilburg University.
  7. Kleijnen, J.P.C. & Smits, M.T., 2003. "Performance metrics in supply chain management," Open Access publications from Tilburg University urn:nbn:nl:ui:12-111582, Tilburg University.
  8. Gneiting, Tilmann & Kleiber, William & Schlather, Martin, 2010. "Matérn Cross-Covariance Functions for Multivariate Random Fields," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1167-1177.
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