Tailor-Made Split-Plot Designs for Mixture and Process Variables

References

• Bingham, D. R.; Schoen, E. D.; and Sitter, R. R. (2004). “Designing Fractional Factorial Split-Plot Experiments with Few Whole-Plot Factors”. Journal of the Royal Statistical Society, Series C 53, pp. 325–339; Corrigendum, 54, pp. 955–958.
   Web of Science ®Google Scholar
• Bingham, D. and Sitter, R. R. (1999a). “Some Theoretical Results for Fractional Factorial Split-Plot Designs”. Annals of Statistics 27, pp. 1240–1255.
   Web of Science ®Google Scholar
• Bingham, D. and Sitter, R. R. (2001). “Design Issues in Fractional Factorial Split-Plot Designs”. Journal of Quality Technology 33, pp. 2–15.
   Web of Science ®Google Scholar
• Bingham, D. and Sitter, R. R. (2003). “Fractional Factorial Split-Plot Designs for Robust Parameter Experiments”. Technometrics 45, pp. 80–89.
   Web of Science ®Google Scholar
• Bingham, D. and Sitter, R. R. (1999b). “Minimum-Aberration Two-Level Fractional Factorial Split-Plot Designs”. Technometrics 41, pp. 62–70.
   Web of Science ®Google Scholar
• Bisgaard, S. (2000). “The Design and Analysis of 2k–p × 2q–r Split Plot Experiments”. Journal of Quality Technology 32, pp. 39–56.
   Web of Science ®Google Scholar
• Bisgaard, S. and Steinberg, D. M. (1997). “The Design and Analysis of 2k–p × s Prototype Experiments”. Technometrics 39, pp. 52–62.
   Web of Science ®Google Scholar
• Chipman, H.; Hamada, M.; and Wu, C. F J. (1997). “A Bayesian Variable Selection Approach for Analyzing Designed Experiments with Complex Aliasing”. Technometrics 39, pp. 372–381.
   Web of Science ®Google Scholar
• Cornell, J. A. (1988). “Analyzing Data from Mixture Experiments Containing Process Variables: A Split-Plot Approach”. Journal of Quality Technology 20, pp. 2–23.
   Web of Science ®Google Scholar
• Cornell, J. A. (2002). Experiments with Mixtures: Designs, Models, and the Analysis of Mixture Data. New York, NY: Wiley.
   Google Scholar
• Cornell, J. A. and Gorman, J. W. (1994). “Fractional Design Plans for Process Variables in Mixture Experiments”. Journal of Quality Technology 16, pp. 20–38.
   Google Scholar
• Czitrom, V. (1989). “Mixture Experiments with Process Variables: D-Optimal Orthogonal Experimental Plans”. Communications in Statistics: Theory and Methods 17, pp. 105–121.
   Google Scholar
• Czitrom, V. (1989). “Experimental Designs for Four Mixture Components with Process Variables”. Communications in Statistics: Theory and Methods 18, pp. 4561–4581.
   Web of Science ®Google Scholar
• Ganju, J. and Lucas, J. M. (1997). “Bias in Test Statistics When Restrictions in Randomization Are Caused by Factors”. Communications in Statistics: Theory and Methods 26, pp. 47–63.
   Web of Science ®Google Scholar
• Gilmour, S. G. and Goos, P. (2006). “Bayesian Analysis of Data from Multi-Stratum Response Surface Designs”. Working Paper 2006/005, Faculty of Applied Economics, Universiteit Antwerpen.
   Google Scholar
• Goos, P. (2002). The Optimal Design of Blocked and Split-plot Experiments. New York, NY: Springer.
   Google Scholar
• Goos, P. (2006). “Optimal Versus Orthogonal and Equivalent-Estimation Design of Blocked and Split-Plot Experiments”. Statistica Neerlandica 60, pp. 361–378.
   Web of Science ®Google Scholar
• Goos, P. and Donev, A. N. (2006). “The D-Optimal Design of Blocked Experiments with Mixture Components”. Journal of Quality Technology 38, pp. 319–332.
   Web of Science ®Google Scholar
• Goos, P. and Vandebroek, M. (2001). “Optimal Split-Plot Designs”. Journal of Quality Technology 33, pp. 436–450.
   Web of Science ®Google Scholar
• Goos, P. and Vandebroek, M. (2003). “D-Optimal Split-Plot Designs with Given Numbers and Sizes of Whole Plots”. Technometrics 45, pp. 235–245.
   Web of Science ®Google Scholar
• Goos, P. and Vandebroek, M. (2004). “Outperforming Completely Randomized Designs”. Journal of Quality Technology 36, pp. 12–26.
   Web of Science ®Google Scholar
• Huang, P.; Chen, D.; and Voelkel, J. (1998). “Minimum-Aberration Two-Level Split-Plot Designs”. Technometrics 40, pp. 314–326.
   Web of Science ®Google Scholar
• Jones, B. and Goos, P. (2007). “A Candidate-Set-Free Algorithm for Generating D-Optimal Split-Plot Designs”. Journal of Royal Statistical Society, Series C: Applied Statistics 56, pp. 347–364.
   PubMed Web of Science ®Google Scholar
• Kowalski, S.; Cornell, J. A.; and Vining, G. G. (2000). “A New Model and Class of Designs for Mixture Experiments with Process Variables”. Communications in Statistics: Theory and Methods 29, pp. 2255–2280.
   Web of Science ®Google Scholar
• Kowalski, S. M. (2002). “24 Run Split-Plot Experiments for Robust Parameter Design”. Journal of Quality Technology 34, pp. 399–410.
   Web of Science ®Google Scholar
• Kowalski, S. M.; Cornell, J. A.; and Vining, G. G. (2002). “Split-Plot Designs and Estimation Methods for Mixture Experiments with Process Variables”. Technometrics 44, pp. 72–79.
   Web of Science ®Google Scholar
• Letsinger, J. D.; Myers, R. H.; and Lentner, M. (1996). “Response Surface Methods for Bi-Randomization Structures”. Journal of Quality Technology 28, pp. 381–397.
   Web of Science ®Google Scholar
• Littell, R. C.; Milliken, G. A.; Stroup, W. W.; and Wolfinger, R. D. (1996). SAS System for Mixed Models. Cary, NC: SAS Institute.
   Google Scholar
• Snee, R. D. (1975). “Experimental Designs for Quadratic Models in Constrained Mixture Spaces”. Technometrics 17, pp. 149–159.
   Web of Science ®Google Scholar
• Trinca, L. A. and Gilmour, S. G. (2001). “Multi-Stratum Response Surface Designs”. Technometrics 43, pp. 25–33.
   Web of Science ®Google Scholar
• Vining, G. G.; Kowalski, S. M.; and Montgomery, D. C. (2005). “Response Surface Designs Within a Split-Plot Structure”. Journal of Quality Technology 37, pp. 115–128.
   Web of Science ®Google Scholar
• Webb, D.; Lucas, J. M.; and Borkowski, J. J. (2004). “Factorial Experiments When Factor Levels Are Not Necessarily Reset”. Journal of Quality Technology 36, pp. 1–11.
   Web of Science ®Google Scholar