Skip to main content
Article thumbnail
Location of Repository

Kriging for Interpolation in Random Simulation

By C. M. Beers and Jack P. C. KleijnenWim C. M. Van Beers and Jack P. C. Kleijnen


1 Kriging in Simulation Whenever simulation requires much computer time, interpolation is needed. There are several interpolation techniques in use (for example, linear regression), but this paper focuses on Kriging. This technique was originally developed in geostatistics by D. G. 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: A perfectly specified detrending function gives the best predictions, but Ordinary Kriging gives quite acceptable results; traditional linear regression gives the worst predictions

Year: 2001
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • (external link)
  • (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.