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- W2275354951 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." @default.
- W2275354951 created "2016-06-24" @default.
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- W2275354951 date "2016-08-01" @default.
- W2275354951 modified "2023-09-26" @default.
- W2275354951 title "Estimating the variance of the predictor in stochastic Kriging" @default.
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- W2275354951 doi "https://doi.org/10.1016/j.simpat.2016.03.008" @default.
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