Enhancing Stochastic Kriging Metamodels with Gradient Estimators

Xi Chen (), Bruce E. Ankenman () and Barry L. Nelson ()
Additional contact information
Xi Chen: Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, Virginia 23284
Bruce E. Ankenman: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208
Barry L. Nelson: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208

Operations Research, 2013, vol. 61, issue 2, 512-528

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.

Keywords: stochastic simulation; metamodeling; gradient estimation (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.1120.1143 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:61:y:2013:i:2:p:512-528

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().