D-Optimal split-plot designs with given numbers and sizes of whole plots

Technometrics

Volumn 45, Issue 3, 2003, Pages 235-245

  Goos, Peter ^a   Vandebroek, Martina ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

2m and 2m f designs; Correlated observations; D optimal design; Equivalence of ordinary least squares and generalized least squares; Hard to change factors; Protein extraction experiment; Split plot experiment

Indexed keywords

ALGORITHMS; COMPUTATIONAL METHODS; OPTIMAL SYSTEMS; PROTEINS;

CORRELATED OBSERVATIONS;

STATISTICAL METHODS;

EID: 0043126959     PISSN: 00401706     EISSN: 15372723     Source Type: Journal    
DOI: 10.1198/004017003000000050     Document Type: Article

References (22)

1. Atkinson, A. C., and Donev, A. N. (1989), “The Construction of Exact D-Optimum Experimental Designs With Application to Blocking Response Surface Designs,” Biometrika, 76, 515-526.
2. Atkinson, A. C., and Donev, A. N. (1992), Optimum Experimental Designs, Oxford, U.K.: Clarendon Press.
3. Bingham, D., and Sitter, R. R. (1999), “Minimum-Aberration Two-Level Fractional Factorial Split-Plot Designs,” Technometrics, 41, 62-70.
4. Bingham, D., and Sitter, R. R. (2001), “Design Issues in Fractional Factorial Split-Plot Designs,” Journal of Quality Technology, 33, 2-15.
5. x s PrototypeExperiments,” Technometrics, 39, 52-62.
6. Box, G. E. P., and Jones, S. P. (1992), “Split-Plot Designs for Robust Product Experimentation,” Journal of Applied Statistics, 19, 3-26.
7. Cook, R. D., and Nachtsheim, C. J. (1989), “Computer-Aided Blocking of Factorial and Response-Surface Designs,” Technometrics, 31, 339-346.
8. Cornell, J. A. (1988), “Analyzing Data From Mixture Experiments Containing Process Variables: A Split-Plot Approach,” Journal of Quality Technology, 20, 2-23.
9. Davison, J. J. (1995), “Response Surface Designs and Analysis for BiRandomization Error Structures,” Ph.D. thesis, Virginia Polytechnic Institute and State University.
10. Donev, A. N., and Atkinson, A. C. (1988), “An Adjustment Algorithm for the Construction of Exact D-optimum Experimental Designs,” Technometrics, 30, 429-433.
11. Fedorov, V. V. (1972), Theory of Optimal Experiments, New York: Academic Press.
12. 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, 47-63.
13. Goos, P., and Vandebroek, M. (2001), “Optimal Split-Plot Designs,” Journal of Quality Technology, 33, 436-450.
14. Goos, P., and Vandebroek, M. (2002), “Tailor-Made Split-Plot Experiments,” Research Report 0211, Katholieke Universiteit Leuven, Dept. of Applied Economics.
15. Harville, D. A. (1997), Matrix Algebra From a Statistician’s Perspective, New York: Springer.
16. Huang, P., Chen, D., and Voelkel, J. (1998), “Minimum-Aberration Two-Level Split-Plot Designs,” Technometrics, 40, 314-326.
17. Kempthorne, O. (1952), The Design and Analysis of Experiments, New York: Wiley.
18. Letsinger, J. D., Myers, R. H., and Lentner, M. (1996), “Response Surface Methods for Bi-Randomization Structures,” Journal of Quality Technology, 28, 381-397.
19. Mitchell, T. J. (1974), “An Algorithm for the Construction of D-Optimal Experimental Designs,” Technometrics, 16, 203-210.
20. Nelson, L. S. (1985), “What Do Low F Ratios Tell You?,” Journal ofQuality Technology, 17, 237-238.
21. Trinca, L. A., and Gilmour, S. G. (2000), “An Algorithm for Arranging Response Surface Designs in Small Blocks,” Computational Statistics and Data Analysis, 33, 25-43.
22. Trinca, L. A., and Gilmour, S. G. (2001), “Multi-Stratum Response Surface Designs,” Technometrics, 43, 25-33.