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

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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.
Original languageEnglish
Place of PublicationTilburg
PublisherOperations research
Number of pages42
Volume2014-012
Publication statusPublished - 2014

Publication series

NameCentER Discussion Paper
Volume2014-012

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Keywords

  • Simulation
  • Stochastic processes
  • Multivariate statistics

Cite this

Kleijnen, J. P. C., & Mehdad, E. (2014). Multivariate Versus Univariate Kriging Metamodels for Multi-Response Simulation Models (Revision of 2012-039). (CentER Discussion Paper; Vol. 2014-012). Tilburg: Operations research.