Stochastic kriging with biased sample estimates

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Stochastic kriging has been studied as an effective metamodeling technique for approximating response surfaces in the context of stochastic simulation. In a simulation experiment, an analyst typically needs to estimate relevant metamodel parameters and further do prediction; therefore, the impact of parameter estimation on the performance of the metamodel-based predictor has drawn some attention in the literature. However, how the standard stochastic kriging predictor is affected by the presence of bias in finite-sample estimates has not yet been fully investigated. In this article, we study the predictive performance and investigate optimal budget allocation rules subject to a fixed computational budget constraint. Furthermore, we extend the analysis to two-level or nested simulation, which has been recently documented in the risk management literature, with biased estimators.
Publisher
ASSOC COMPUTING MACHINERY
Issue Date
2014-02
Language
English
Article Type
Article
Citation

ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION, v.24, no.2

ISSN
1049-3301
DOI
10.1145/2567893
URI
http://hdl.handle.net/10203/188996
Appears in Collection
IE-Journal Papers(저널논문)
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