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Kriging for Interpolation in Random Simulation

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  • van Beers, W.C.M.

    (Tilburg University, School of Economics and Management)

  • Kleijnen, J.P.C.

    (Tilburg University, School of Economics and Management)

Abstract

Whenever simulation requires much computer time, interpolation is needed. Simulationists use different interpolation techniques (eg linear regression), but this paper focuses on Kriging. This technique was originally developed in geostatistics by DG Krige, and has recently been widely applied in deterministic simulation. This paper, however, focuses on random or stochastic simulation. Essentially, Kriging gives more weight to ‘neighbouring’ observations. There are several types of Kriging; this paper discusses—besides Ordinary Kriging—a novel type, which ‘detrends’ data through the use of linear regression. Results are presented for two examples of input/output behaviour of the underlying random simulation model: Ordinary and Detrended Kriging give quite acceptable predictions; traditional linear regression gives the worst results.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • van Beers, W.C.M. & Kleijnen, J.P.C., 2001. "Kriging for Interpolation in Random Simulation," Other publications TiSEM e007110c-e224-484d-92f0-c, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiutis:e007110c-e224-484d-92f0-caae6a13f3e4
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    References listed on IDEAS

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    1. Kleijnen, J.P.C. & van Groenendaal, W.J.H., 1992. "Two-stage versus sequential sample-size determination in regression analysis of simulation experiments," Other publications TiSEM cac61c9f-0967-4f5a-aab1-9, Tilburg University, School of Economics and Management.
    2. van Groenendaal, W.J.H. & Kleijnen, J.P.C., 1997. "On the assessment of economic risk : Factorial design versus Monte Carlo methods," Other publications TiSEM fd2a2307-0812-4543-8151-7, Tilburg University, School of Economics and Management.
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