Mixed-input Gaussian process emulators for computer experiments with a large number of categorical levels

References

• Beck, A., and M. Teboulle. 2009. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences 2 (1):183–202. doi: 10.1137/080716542.
   Web of Science ®Google Scholar
• Bien, J., and R. J. Tibshirani. 2011. Sparse estimation of a covariance matrix. Biometrika 98 (4):807–20. doi: 10.1093/biomet/asr054.
   PubMed Web of Science ®Google Scholar
• Cameron, K. W., A. Anwar, Y. Cheng, L. Xu, B. Li, U. Ananth, J. Bernard, C. Jearls, T. Lux, Y. Hong, et al. 2019. MOANA: Modeling and analyzing I/O variability in parallel system experimental design. IEEE Transactions on Parallel and Distributed Systems 30 (8):1843–1. doi: 10.1109/TPDS.2019.2892129.
   Web of Science ®Google Scholar
• Currin, C., T. Mitchell, M. Morris, and D. Ylvisaker. 1991. Bayesian prediction of deterministic functions, with applications to the design and analysis of computer experiments. Journal of the American Statistical Association 86 (416):953–63. doi: 10.1080/01621459.1991.10475138.
   Web of Science ®Google Scholar
• Deng, X., Y. Hung, and C. D. Lin. 2015. Design for computer experiments with qualitative and quantitative factors. Statistica Sinica 25 (4):1567–81. doi: 10.5705/ss.2013.388.
   Web of Science ®Google Scholar
• Deng, X., C. D. Lin, K.-W. Liu, and R. Rowe. 2017. Additive Gaussian process for computer models with qualitative and quantitative factors. Technometrics 59 (3):283–92. doi: 10.1080/00401706.2016.1211554.
   Web of Science ®Google Scholar
• Fang, K.-T., R. Li, and A. Sudjianto. 2005. Design and modeling for computer experiments. Boca Raton, FL: CRC Press.
   Google Scholar
• Fricker, T. E., J. E. Oakley, and N. M. Urban. 2013. Multivariate Gaussian process emulators with nonseparable covariance structures. Technometrics 55 (1):47–56. doi: 10.1080/00401706.2012.715835.
   Web of Science ®Google Scholar
• Gramacy, R. B., and M. Taddy. 2009. Categorical inputs, sensitivity analysis, optimization and importance tempering with tgp version 2, an R package for treed Gaussian process models. University of Cambridge Statistical Laboratory Tech. Rep. Cambridge, UK.
   Google Scholar
• Han, G., T. J. Santner, W. I. Notz, and D. L. Bartel. 2009. Prediction for computer experiments having quantitative and qualitative input variables. Technometrics 51 (3):278–88. doi: 10.1198/tech.2009.07132.
   Web of Science ®Google Scholar
• Hunter, D. R., and R. Li. 2005. Variable selection using MM algorithms. Annals of Statistics 33 (4):1617–42. doi: 10.1214/009053605000000200.
   PubMed Web of Science ®Google Scholar
• Joseph, V. R., E. Gul, and S. Ba. 2019. Designing computer experiments with multiple types of factors: The MaxPro approach. Journal of Quality Technology, 1–12. doi: 10.1080/00224065.2019.1611351.
   Web of Science ®Google Scholar
• Luo, J., L. J. Hong, B. L. Nelson, and Y. Wu. 2015. Fully sequential procedures for large-scale ranking-and-selection problems in parallel computing environments. Operations Research 63 (5):1177–94. doi: 10.1287/opre.2015.1413.
   Web of Science ®Google Scholar
• McKay, M. D., W. J. Conover, and R. J. Beckman. 1979. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21 (2):239–45. doi: 10.2307/1268522.
   Web of Science ®Google Scholar
• Morris, M., T. Mitchell, and D. Ylvisaker. 1993. Bayesian design and analysis of computer experiments: Use of derivatives in surface prediction. Technometrics 35 (3):243–55. doi: 10.1080/00401706.1993.10485320.
   Web of Science ®Google Scholar
• Norcott, W. D. 2019. IOzone Filesystem Benchmark. http://www.iozone.org/.
   Google Scholar
• Qian, P. Z. G. 2012. Sliced Latin hypercube designs. Journal of the American Statistical Association 107 (497):393–9. doi: 10.1080/01621459.2011.644132.
   Web of Science ®Google Scholar
• Qian, P. Z. G., and C. F. J. Wu. 2009. Sliced space-filling designs. Biometrika 96 (4):945–56. doi: 10.1093/biomet/asp044.
   Web of Science ®Google Scholar
• Qian, P. Z. G., H. Wu, and C. F. J. Wu. 2008. Gaussian process models for computer experiments with qualitative and quantitative factors. Technometrics 50 (3):383–96. doi: 10.1198/004017008000000262.
   Web of Science ®Google Scholar
• Roustant, O., E. Padonou, Y. Deville, A. Clément, G. Perrin, J. Giorla, and H. Wynn. 2018. Group kernels for Gaussian process metamodels with categorical inputs. arXiv Preprint arXiv:1802.02368.
   Google Scholar
• Sacks, J., S. B. Schiller, and W. J. Welch. 1989. Designs for computer experiments. Technometrics 31 (1):41–7. doi: 10.1080/00401706.1989.10488474.
   Web of Science ®Google Scholar
• Santner, T. J., B. J. Williams, and W. I. Notz. 2013. The design and analysis of computer experiments. New York: Springer Science & Business Media.
   Google Scholar
• Wright, S. J., and J. Nocedal. 1999. Numerical optimization, vol. 2. New York: Springer.
   Google Scholar
• Wu, C. F. J. 2015. Post-Fisherian experimentation: From physical to virtual. Journal of the American Statistical Association 110 (510):612–20. doi: 10.1080/01621459.2014.914441.
   Web of Science ®Google Scholar
• Zhang, Y., and W. I. Notz. 2015. Computer experiments with qualitative and quantitative variables: A review and reexamination. Quality Engineering 27 (1):2–13. doi: 10.1080/08982112.2015.968039.
   Web of Science ®Google Scholar
• Zhang, Y., S. Tao, W. Chen, and D. W. Apley. 2019. A latent variable approach to Gaussian process modeling with qualitative and quantitative factors. Technometrics, 1–12. doi: 10.1080/00401706.2019.1638834.
   Web of Science ®Google Scholar
• Zhou, Q., P. Z. G. Qian, and S. Zhou. 2011. A simple approach to emulation for computer models with qualitative and quantitative factors. Technometrics 53 (3):266–73. doi: 10.1198/TECH.2011.10025.
   Web of Science ®Google Scholar