Efficient designs with minimal aliasing

Technometrics

Volumn 53, Issue 1, 2011, Pages 62-71

  Jones, Bradley ^a,b   Nachtsheim, Christopher J ^c  

^a SAS INSTITUTE INC   (United States)

^b UNIVERSITY OF ANTWERP   (Belgium)

^c UNIVERSITY OF MINNESOTA   (United States)

Author keywords

Alias matrix; Bayesian design; Constrained design; D optimality; Minimum bias design; Response surface design

Indexed keywords

ALIAS MATRIX; BAYESIAN DESIGN; CONSTRAINED DESIGN; D-OPTIMALITY; MINIMUM BIAS DESIGN; RESPONSE SURFACE DESIGNS;

BAYESIAN NETWORKS; OPTIMAL SYSTEMS; OPTIMIZATION; SURFACE PROPERTIES;

DESIGN;

EID: 79251564059     PISSN: 00401706     EISSN: None     Source Type: Journal    
DOI: 10.1198/TECH.2010.09113     Document Type: Article

References (30)

1. Bingham, D. R., and Chipman, H. A. (2007), "Incorporating Prior Information in Optimal Design for Model Selection," Technometrics, 49, 155-163. [62]
2. Box, G. E. P., and Draper, N. R. (1959), "A Basis for the Selection of a Response Surface Design," Journal of the American Statistical Association, 54, 622-654. [63]
3. Box, G. E. P., Hunter, J. S., and Hunter, W. G. (2005), Statistics for Experimenters (2nd ed.), New York: Wiley. [68]
4. Bursztyn, D., and Steinberg, D. (2006), "Comparison of Designs for Computer Experiments," Journal of Statistical Planning and Inference, 136, 1103-1119. [62,64,65]
5. Clyde, M. A., and Chaloner, K. (1996), "The Equivalence of Constrained and Weighted Designs in Multiple Objective Design Problems," Journal of the American Statistical Association, 91, 1236-1244. [64]
6. Cook, R. D., and Fedorov, V. V. (1995), "Constrained Optimization of Experimental Design," Statistics, 26, 129-148. [64]
7. Cook, R. D., and Nachtsheim, C. J. (1982), "Model Robust, Linear-Optimal Designs," Technometrics, 24, 49-54. [62]
8. Cook, R. D., and Wong, W. K. (1994), "On the Equivalence of Constrained and Compound Optimal Designs," Journal of the American Statistical Association, 89, 687-692. [64,65]
9. Draper, N. R., and Guttman, I. (1992), "Treating Bias as Variance for Experimental Design Purposes," Annals of the Institute of Statistical Mathematics, 44, 659-671. [64]
10. Draper, N. R., and Lawrence, W. E. (1965), "Designs Which Minimize Model Inadequacies: Cuboidal Regions of Interest," Biometrika, 52, 111-118. [62]
11. DuMouchel, W., and Jones, B. (1994), "A Simple Bayesian Modification of D-Optimal Designs to Reduce Dependence on an Assumed Model," Technometrics, 36, 37-47. [62,63,68,70]
12. Evans, J. W., and Manson, A. R. (1978), "Optimal Experimental Designs in Two Dimensions Using Minimum Bias Estimation," Journal of the American Statistical Association, 73, 171-176. [62]
13. Fedorov, V. V. (1972), Theory of Optimal Experiments, New York: Academic Press. [64]
14. k-p Designs," Technometrics, 22, 601-608. [70]
15. Hall, M., Jr. (1961), "Hadamard Matrix of Order 16," Jet Propulsion Laboratory Research Summary, 1, 21-26. [65]
16. Jones, B., and Nachtsheim, C. J. (2011), "A Class of Three-Level Designs for Definitive Screening in the Presence of Second-Order Effects," Journal of Quality Technology, 43, 1-15. [66]
17. Jones, B., Lin, D., and Nachtsheim, C. J. (2008), "Bayesian D-Optimal Supersaturated Designs," Journal of Statistical Planning and Inference, 138, 86-92. [62]
18. Jones, B., Li, W., Nachtsheim, C. J., and Ye, K. (2007), "Model Discrimination - Another Perspective on Model-Robust Designs," Journal of Statistical Planning and Inference, 137, 1577-1583. [62]
19. Karson, M. J. (1970), "Design Criterion for Minimum Bias Estimation of Response Surfaces," Journal of the American Statistical Association, 65, 1565-1572. [62]
20. Karson, M. J., and Spruill, M. L. (1975), "Design Criteria and Minimum Bias Estimation," Communications in Statistics, 4, 339-356. [62]
21. Läuter, E. (1974), "Experimental Design in a Class of Models," Mathematische Operationsforschung und Statistik, 5, 379-396. [62]
22. Li, W., and Nachtsheim, C. J. (2000), "Model-Robust Factorial Designs," Technometrics, 42, 379-396. [62]
23. Meyer, R. K., and Nachtsheim, C. J. (1995), "The Coordinate Exchange Algorithm for Constructing Exact Experimental Designs," Technometrics, 37, 60-69. [65,70]
24. Meyer, R. D., Steinberg, D. M., and Box, G. E. P. (1996), "Follow-Up Designs to Resolve Confounding in Multifactor Experiments," Technometrics, 38, 303-313. [62]
25. Montepiedra, G., and Fedorov, V. V. (1997), "Minimum Bias Designs With Constraints," Journal of Statistical Planning and Inference, 63, 97-111. [63,64]
26. Steinberg, D. M. (1985), "Model Robust Response Surface Designs: Scaling Two-Level Factorials," Biometrika, 72, 513-526. [64]
27. Sun, D. X., Li, W., and Ye, K. Q. (2002), "An Algorithm for Sequentially Constructing Non-Isomorphic Orthogonal Designs and Its Applications," Technical Report SUNYSB-AMS-02-13, State University of New York at Stony Brook, Dept. of Applied Mathematics and Statistics. [65]
28. Tsai, P.-W., Gilmour, S. G., and Mead, R. (2000), "Projective Three-Level Main Effects Designs Robust to Model Uncertainty," Biometrika, 87, 467-475. [64]
29. Welch, W. J. (1983), "A Mean Squared Error Criterion for the Design of Experiments," Biometrika, 70, 205-213. [64]
30. Yang, J. Y., and Draper, N. R. (2003), "Two-Level Factorial and Fractional Factorial Designs in Blocks of Size Two," Journal of Quality Technology, 35, 294-305. [66]