The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Stochastic Kriging for Simulation Metamodeling
Abstract
We extend the basic theory of kriging, as applied to the design and analysis of deterministic computer experiments, to the stochastic simulation setting. Our goal is to provide flexible, interpolation-based metamodels of simulation output performance measures as functions of the controllable design or decision variables, or uncontrollable environmental variables. To accomplish this, we characterize both the intrinsic uncertainty inherent in a stochastic simulation and the extrinsic uncertainty about the unknown response surface. We use tractable examples to demonstrate why it is critical to characterize both types of uncertainty, derive general results for experiment design and analysis, and present a numerical example that illustrates the stochastic kriging method.
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Design and Analysis of Computer Experiments
Sacks, J., Welch, W. J., Mitchell, T. J., & Wynn, H. P. (1989) · Statistical Science · doi:10.1214/ss/1177012413 · Cited by 6,919
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Simulation modeling and analysis
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The Design and Analysis of Computer Experiments
Santner, T. J., Williams, B. J., & Notz, W. I. (2003) · Springer Series in Statistics · doi:10.1007/978-1-4757-3799-8 · Cited by 2,170
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Interpolation of Spatial Data: Some Theory for Kriging
Rathbun, S. L., & Stein, M. L. (2000) · Journal of the American Statistical Association · doi:10.2307/2669494 · Cited by 1,916
Predicting the output from a complex computer code when fast approximations are available
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Cited by
Bessa, M. A., Bostanabad, R., Liu, Z., Hu, A., Apley, D. W., Brinson, C., Chen, W., & Liu, W. K. (2017) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2017.03.037 · Cited by 573
Sun, Q., Ye, Z.-S., & Zhu, X. (2020) · IISE Transactions · doi:10.1080/24725854.2019.1672908 · Cited by 108
Johnson, L. R., Gramacy, R. B., Cohen, J., Mordecai, E., Murdock, C., Rohr, J., Ryan, S. J., Stewart-Ibarra, A. M., & Weikel, D. (2018) · The Annals of Applied Statistics · doi:10.1214/17-aoas1090 · Cited by 42
Metadata
- Authors
- Ankenman, B., Nelson, B. L., & Staum, J.
- Venue
- Operations Research
- Published
- 2010-04
- Volume
- 58
- Issue
- 2
- Pages
- 371-382
- ISSN
- 0030-364X (ISSN-L)
- 1526-5463
- OpenAlex
- W2163286960
- Cited by
- 622
- References
- 19
- FWCI
- 29.77
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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