Enhancing stochastic kriging metamodels with gradient estimators

Chen, X. and Ankenman, B. E. and Nelson, B. L. (2013) Enhancing stochastic kriging metamodels with gradient estimators. Operations Research, 61 (2). pp. 512-528. ISSN 0030-364X

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Abstract

Stochastic kriging is a new metamodeling technique for effectively representing the mean response surface implied by a stochastic simulation; it takes into account both stochastic simulation noise and uncertainty about the underlying response surface of interest. We show theoretically, through some simplified models, that incorporating gradient estimators into stochastic kriging tends to significantly improve surface prediction. To address the issue of which type of gradient estimator to use, when there is a choice, we briefly review stochastic gradient estimation techniques; we then focus on the properties of infinitesimal perturbation analysis and likelihood ratio/score function gradient estimators and make recommendations. To conclude, we use simulation experiments with no simplifying assumptions to demonstrate that the use of stochastic kriging with gradient estimators provides more reliable prediction results than stochastic kriging alone.

Item Type:
Journal Article
Journal or Publication Title:
Operations Research
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1706
Subjects:
ID Code:
65032
Deposited By:
Deposited On:
06 Jun 2013 15:46
Refereed?:
Yes
Published?:
Published
Last Modified:
31 May 2020 02:09