Estimating the variance of the predictor in stochastic Kriging

J.P.C. Kleijnen, Ehsan Mehdad

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)

Abstract

We study the estimation of the true variance of the predictor in stochastic Kriging (SK). First, we obtain macroreplications for a SK metamodel that approximates a single-server simulation model; these macroreplications give independently and identically distributed predictions. This simulation may use common random numbers (CRN). From these macroreplications we conclude that the usual plug-in estimator of the variance significantly underestimates the true variance. Because macroreplications of practical simulation models are computationally expensive, we next formulate two bootstrap methods that use a single macroreplication: (i) a distribution-free method that resamples simulation replications (within the single macroreplication), and (ii) a parametric method that assumes a Gaussian distribution for the SK predictor, and estimates the (hyper)parameters of that distribution from the single macroreplication. Altogether we recommend distribution-free bootstrapping for the estimation of the SK predictor variance in practical simulation experiments.
Original languageEnglish
Pages (from-to)166-173
JournalSimulation Modelling Practice and Theory
Volume66
DOIs
Publication statusPublished - Aug 2016

Keywords

  • Kriging
  • Gaussian process
  • predictor variance
  • plug-in
  • Bootstrap

Fingerprint Dive into the research topics of 'Estimating the variance of the predictor in stochastic Kriging'. Together they form a unique fingerprint.

  • Cite this