References
- Anderson-Cook, C. M., and L. Lu. 2018. Graphics to facilitate informative discussion and team decision-making (with discussions). Applied Stochastic Models in Business and Industry 34 (6):963–80. doi: 10.1002/asmb.2325.
- Anderson-Cook, C. M., L. Lu, K. L. Myers, K. R. Quinlan, and N. Pawley. 2019. Improved learning from data competitions through strategic design of training and test data sets. Quality Engineering 31 (4):564–80. doi: 10.1080/08982112.2019.1572186.
- Anderson-Cook, C. M., K. L. Myers, L. Lu, and M. L. Fugate. 2019. Data competitions: Getting more from a strategic design and post-competition analysis. Statistical Analysis and Data Mining 10.1002/sam.11404.
- Anderson-Cook, C. M., K. L. Myers, L. Lu, M. L. Fugate, K. R. Quinlan, and N. Pawley. 2019. How to host an effective data competition: Statistical advice for competition design and analysis. Statistical Analysis and Data Mining 12 (4):271–289.
- Bowman, V. E., and D. C. Woods. 2013. Weighted space-filling designs. Journal of Simulation 7 (4):249–63. doi: 10.1057/jos.2013.8.
- Cook, R. D., and C. J. Nachtsheim. 1980. A comparison of algorithms for constructing exact D-optimal designs. Technometrics 22 (3):315–24. doi: 10.1080/00401706.1980.10486162.
- Johnson, M. E., L. M. Moore, and D. Ylvisaker. 1990. Minimax and maximin distance designs. Journal of Statistical Planning and Inference 26 (2):131–48. doi: 10.1016/0378-3758(90)90122-B.
- Joseph, R., T. Dasgupta, R. Tuo, and C. F. J. Wu. 2015. Sequential exploration of complex surfaces using minimum energy designs. Technometrics 57 (1):64–126. doi: 10.1080/00401706.2014.881749.
- McKay, M., R. Beckman, and W. Conover. 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.
- Mease, D., and D. Bingham. 2006. Latin hyperrectangle sampling for computer experiments. Technometrics 48 (4):467–77. doi: 10.1198/004017006000000101.
- Morgan, J. C., D. Bhattacharyya, C. Tong, and D. C. Miller. 2015. Uncertainty quantification of property models: Methodology and its applications to CO2-loaded aqueous MEA solutions. AIChE Journal 61 (6):1822–39. doi: 10.1002/aic.14762.
- Morris, M., and T. Mitchell. 1995. Exploratory designs for computational experiments. Journal of Statistical Planning and Inference 43 (3):381–402. doi: 10.1016/0378-3758(94)00035-T.
- Myers, R. H., D. C. Montgomery, and C. M. Anderson-Cook. 2016. Response surface methodology: Process and product optimization using designed experiments. 4th ed. New York: Wiley.
- Nguyen, N. K., and A. J. Miller. 1992. A review of some exchange algorithms for constructing discrete d-optimal designs. Computational Statistics & Data Analysis 14 (4):489–92. doi: 10.1016/0167-9473(92)90064-M.
- Pronzato, L., and W. G. Muller. 2012. Design of computer experiments: Space filling and beyond. Statistics and Computing 22 (3):681–701. doi: 10.1007/s11222-011-9242-3.
- Santner, T. J., B. J. Williams, and W. I. Notz. 2003. The Design and Analysis of Computer Experiments. New York: Springer.
- Soepya, F. B., C. M. Anderson-Cook, D. Bhattacharyya, J. C. Morgan, B. P. Omell, M. A. Zamarripa, J. C. Eslick, M. S. Matuszewski, D. C. Miller, J. R. Gattiker, et al. 2018. Sequential design of experiments to maximize learning from carbon capture pilot scale testing. In Proceedings of the 13th International Symposium on Process Systems Engineering, eds. M. R. Eden, M. Ierapetritou, and G. T. Towler, 283–288. London: Elsevier.
- Weaver, B. P., B. J. Williams, C. M. Anderson-Cook, and D. M. Higdon. 2016. Computational enhancements to Bayesian design of experiments using Gaussian processes. Bayesian Analysis 11:191–213.