A construction method for orthogonal Latin hypercube designs

Biometrika

Volumn 93, Issue 2, 2006, Pages 279-288

  Steinberg, David M ^a   Lin, Dennis K J ^b  

^a TEL AVIV UNIVERSITY   (Israel)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Computer experiment; Factorial design; Rotation design; U design

Indexed keywords

EID: 33745628754     PISSN: 00063444     EISSN: 14643510     Source Type: Journal    
DOI: 10.1093/biomet/93.2.279     Document Type: Article

References (17)

1. BEATTIE, S. D. & LIN, D. K. J. (1997), Rotated factorial design for computer experiments, In Proc. Phys. Eng. Sci. Sect. Am. Statist. Assoc. Washington DC: American Statistical Association.
2. BEATTIE, S. D. & LIN, D. K. J. (2004). Rotated factorial designs for computer experiments. J. Chin. Statist. Assoc. 42, 289-308.
3. BEATTIE, S. D. & LIN, D. K. J. (2005). A new class of Latin hypercube for computer experiments. In Contemporary Multivariate Analysis and Experimental Designs In Celebration of Professor Kai-Tai Fang's 65th Birthday, Ed. J. Fan and G. Li, pp. 206-66. Singapore: World Scientific.
4. Box, G. E. P. & WILSON, K. B. (1951). On the experimental attainment of optimum conditions (with Discussion). J. R. Statist. Soc. B 13, 1-34.
5. BURSZTYN, D. & STEINBERG, D. M. (2001). Rotation designs for experiments in high bias situations. J. Statist. Plan. Infer. 97, 399-414.
6. BURSZTYN, D. & STEINBERG, D. M. (2002). Rotation designs: Orthogonal first-order designs with higher order projectivity. J. Appl. Stoch. Models Bus. Indust. 18, 197-206.
7. BUTLER, N. A. (2001). Optimal and orthogonal Latin hypercube designs for computer experiments. Biometrika 88, 847-57.
8. HAMADA, M. & Wu, C. F. J. (1992). Analysis of designed experiments with complex aliasing. J. Qual. Technol. 24, 130-7.
9. HEDAYAT, A. S., SLOANE, N. J. A. & STUFKEN, J. (1999). Orthogonal Arrays: Theory and Applications. New York: Springer.
10. HELTON, J. C. & DAVIS, F. J. (2000). Sampling-based methods. In Sensitivity Analysis, Ed. A. Saltelli, K. Chan and E. M. Scott, pp. 101-53. Chichester: John Wiley & Sons.
11. MCKAY, M. D., BECKMAN, R. J. & CONOVER, W. J. (1979). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21, 239-45.
12. OWEN, A. B. (1992). Orthogonal arrays for computer experiments, integration and visualization. Statist. Sinica 2, 439-52.
13. OWEN, A. B. (1994). Controlling correlations in Latin hypercube samples. J. Am. Statist. Assoc. 89, 1517-22.
14. SANTNER, T. J., WILLIAMS, B. J. & NOTZ, W. I. (2003). The Design and Analysis of Computer Experiments. New York: Springer.
15. TANG, B. (1993). Orthogonal array-based Latin hypercubes. J. Am. Statist. Assoc. 88, 1392-7.
16. TANG, B. (1998). Selecting Latin hypercubes using correlation criteria. Statist. Sinica 8, 965-77.
17. YE, K. Q. (1998). Orthogonal column Latin hypercubes and their application in computer experiments. J. Am. Statist. Assoc. 93, 1430-9.