• DocumentCode
    1816725
  • Title

    Better simulation metamodeling: The why, what, and how of stochastic kriging

  • Author

    Staum, Jeremy

  • Author_Institution
    Dept. of Ind. Eng. & Manage. Sci., Northwestern Univ., Evanston, IL, USA
  • fYear
    2009
  • fDate
    13-16 Dec. 2009
  • Firstpage
    119
  • Lastpage
    133
  • Abstract
    Stochastic kriging is a methodology recently developed for metamodeling stochastic simulation. Stochastic kriging can partake of the behavior of kriging and of generalized least squares regression. This advanced tutorial explains regression, kriging, and stochastic kriging as metamodeling methodologies, emphasizing the consequences of misspecified models for global metamodeling. It provides an exposition of how to choose parameters in stochastic kriging and how to build a metamodel with it given simulation output, and discusses future research directions to enhance stochastic kriging.
  • Keywords
    modelling; statistical analysis; stochastic processes; least squares regression; simulation metamodeling; stochastic kriging; Analytical models; Computational modeling; Industrial engineering; Metamodeling; Operations research; Predictive models; Response surface methodology; Steady-state; Stochastic processes; Stochastic systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Simulation Conference (WSC), Proceedings of the 2009 Winter
  • Conference_Location
    Austin, TX
  • Print_ISBN
    978-1-4244-5770-0
  • Type

    conf

  • DOI
    10.1109/WSC.2009.5429320
  • Filename
    5429320