The influence of correlation functions on stochastic kriging metamodels

Proceedings - Winter Simulation Conference

Volumn , Issue , 2010, Pages 1067-1078

  Xie, Wei ^a   Nelson, Barry ^a   Staum, Jeremy ^a  

^a Northwestern University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CORRELATION FUNCTION; HIGHER ORDER; JACKSON NETWORKS; KRIGING; KRIGING METAMODELS; M/G/1 QUEUE; MULTI-PRODUCTS; NON-DIFFERENTIABLE; PREDICTION ACCURACY; RESPONSE SURFACE; SPATIAL AND FREQUENCY DOMAIN;

INTERPOLATION; MATHEMATICAL MODELS; STOCHASTIC SYSTEMS; SURFACE PROPERTIES;

CORRELATION METHODS;

EID: 79951594258     PISSN: 08917736     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WSC.2010.5679083     Document Type: Conference Paper

References (12)

1. Ankenman, B., B. L. Nelson, and J. Staum. Stochastic kriging for simulation metamodeling. Operations Research 58 (2): 371-382.
2. Borkowski, J. J., and G. F. Piepel. 2009. Uniform designs for highly constrained mixture experiments. Journal of Quality Technology 41 (1): 35-47.
3. Cressie, N. A. C. 1993. Statistics for spatial data. New York: John Wiley & Sons.
4. Currin, C., T. Mitchell, M. Morris, and D. Ylvisaker. 1991. Bayesian prediction of deterministic functions, with applications to the design and analysis of computer experiments. Journal of the American Statistical Association 86 (416): 953-963.
5. Mitchell, T. J., and M. D. Morris. 1992. The spatial correlation function approach to response surface estimation. In Proceedings of the 1992 Winter Simulation Conference, ed. R. C. Crain, J. R. Wilson, J. J. Swain, and D. Goldsman, 565-571: Piscataway, N.J.: Institute of Electrical and Electronics Engineers.
6. Oppenheim, A. V., R. W. Schafer, and J. R. Buck. 2005. Discrete-time signal processing. Upper Saddle River, NJ: Pearson Education.
7. Sacks, J., S. B. Schiller, and W. J. Welch. 1989. Designs for computer experiments. Technometrics 31 (1): 41-47.
8. Santner, T. J., B. J. Williams, and W. I. Notz. 2003. The design and analysis of computer experiments. New York: Springer.
9. Stein, M. L. 1999. Interpolation of spatial data: Some theory for kriging. New York: Springer-Verlag.
10. van Beers, W. C. M., and J. Kleijnen. 2003. Kriging for interpolation in random simulation. Journal of the Operational Research Society 54:255-262.
11. Whitt, W. 1989. Planning queueing simulations. Management Science 35 (11): 1341-1366.
12. Yang, F., J. Liu, M. Tongarlak, B. E. Ankenman, and B. L. Nelson. 2007. Metamodeling for cycle time-throughput-product mix surfaces using progressive model fitting. In Proceedings of the 2007 Winter Simulation Conference, ed. S. G. Henderson, B. Biller, M.-H. Hsieh, J. Shortle, J. D. Tew, and R. R. Barton, 322-330: Piscataway, N.J.: Institute of Electrical and Electronics Engineers.