The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Choosing the Sample Size of a Computer Experiment: A Practical Guide
Abstract
We provide reasons and evidence supporting the informal rule that the number of runs for an effective initial computer experiment should be about 10 times the input dimension. Our arguments quantify two key characteristics of computer codes that affect the sample size required for a desired level of accuracy when approximating the code via a Gaussian process (GP). The first characteristic is the total sensitivity of a code output variable to all input variables; the second corresponds to the way this total sensitivity is distributed across the input variables, specifically the possible presence of a few prominent input factors and many impotent ones (i.e., effect sparsity). Both measures relate directly to the correlation structure in the GP approximation of the code. In this way, the article moves toward a more formal treatment of sample size for a computer experiment. The evidence supporting these arguments stems primarily from a simulation study and via specific codes modeling climate and ligand activation of G-protein.
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Cites
Efficient Global Optimization of Expensive Black-Box Functions
Jones, D. R., Schonlau, M., & Welch, W. J. (1998) · Journal of Global Optimization · doi:10.1023/a:1008306431147 · Cited by 8,070
Self-citationMcKay, M. D., Beckman, R. J., & Conover, W. J. (2000) · Technometrics · doi:10.2307/1271432 · Cited by 7,415
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
Self-citationMcKay, M. D., Beckman, R. J., & Conover, W. J. (1979) · Technometrics · doi:10.2307/1268522 · Cited by 3,757
Exploratory designs for computational experiments
Morris, M. D., & Mitchell, T. J. (1995) · Journal of Statistical Planning and Inference · doi:10.1016/0378-3758(94)00035-t · Cited by 1,197
Designs for Computer Experiments
Sacks, J., Schiller, S. B., & Welch, W. J. (1989) · Technometrics · doi:10.1080/00401706.1989.10488474 · Cited by 970
Self-citationComputer Model Calibration Using High-Dimensional Output
Higdon, D., Gattiker, J., Williams, B., & Rightley, M. (2008) · Journal of the American Statistical Association · doi:10.1198/016214507000000888 · Cited by 867
Currin, C., Mitchell, T., Morris, M., & Ylvisaker, D. (1991) · Journal of the American Statistical Association · doi:10.1080/01621459.1991.10475138 · Cited by 753
Bayesian Treed Gaussian Process Models With an Application to Computer Modeling
Gramacy, R. B., & Lee, H. K. H. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000689 · Cited by 650
Screening, Predicting, and Computer Experiments
Welch, W. J., Buck, R. J., Sacks, J., Wynn, H. P., Mitchell, T. J., & Morris, M. D. (1992) · Technometrics · doi:10.2307/1269548 · Cited by 630
Self-citationCombining Field Data and Computer Simulations for Calibration and Prediction
Higdon, D., Kennedy, M., Cavendish, J. C., Cafeo, J. A., & Ryne, R. D. (2004) · SIAM Journal on Scientific Computing · doi:10.1137/s1064827503426693 · Cited by 623
Computer model validation with functional output
Bayarri, M. J., Walsh, D., Berger, J. O., Cafeo, J., Garcia-Donato, G., Liu, F., Palomo, J., Parthasarathy, R. J., Paulo, R., & Sacks, J. (2007) · The Annals of Statistics · doi:10.1214/009053607000000163 · Cited by 238
Self-citationComputer Model Validation with Functional Output
(2015) · Cited by 204
Variable Selection for Gaussian Process Models in Computer Experiments
Linkletter, C., Bingham, D., Hengartner, N., Higdon, D., & Ye, K. Q. (2006) · Technometrics · doi:10.1198/004017006000000228 · Cited by 167
Circuit Optimization via Sequential Computer Experiments: Design of an Output Buffer
Aslett, R., Buck, R. J., Duvall, S. G., Sacks, J., & Welch, W. J. (1998) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/1467-9876.00096 · Cited by 45
Self-citationWelch, W. J., Buck, R. J., Sacks, J., Wynn, H. P., Morris, M. D., & Schonlau, M. (1996) · Technometrics · doi:10.1080/00401706.1996.10484496 · Cited by 7
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Cited by
Lindgren, F., Rue, H., & Lindström, J. (2011) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2011.00777.x · Cited by 2,708
Yondo, R., Andrés, E., & Valero, E. (2018) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2017.11.003 · Cited by 311
Couvreux, F., Hourdin, F., Williamson, D., Roehrig, R., Volodina, V., Villefranque, N., Rio, C., Audouin, O., Salter, J., Bazile, E., et al. (2021) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2020ms002217 · Cited by 121
Optimization of formulations using robotic experiments driven by machine learning DoE
Cao, L., Russo, D., Felton, K., Salley, D., Sharma, A., Keenan, G., Mauer, W., Gao, H., Cronin, L., & Lapkin, A. A. (2021) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2020.100295 · Cited by 75
Metadata
- Authors
- Loeppky, J. L., Sacks, J., & Welch, W. J.
- Venue
- Technometrics
- Publisher
- Informa UK Limited
- Published
- 2009-11
- Volume
- 51
- Issue
- 4
- Pages
- 366-376
- ISSN
- 0040-1706 (ISSN-L)
- 1537-2723
- OpenAlex
- W2098720801
- Cited by
- 758
- References
- 16
- FWCI
- 20.75
- Type
- journal article
- Language
- en
- Open access
- closed
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