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Designing Uniform Computer Sequential Experiments with Mixture Levels Using Lee Discrepancy

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Abstract

Computer experiments are constructed to simulate the behavior of complex physical systems. Uniform designs have good performance in computer experiments from several aspects. In practical use, the experimenter needs to choose a small size uniform design at the beginning of an experiment due to a limit of time, budget, resources, and so on, and later conduct a follow up experiment to obtain precious information about the system, that is, a sequential experiment. The Lee distance has been widely used in coding theory and its corresponding discrepancy is an important measure for constructing uniform designs. This paper proves that all the follow up designs of a uniform design are uniform and at least two of them can be used as optimal follow up experimental designs. Thus, it is not necessary that the union of any two uniform designs yields a uniform sequential design. Therefore, this article presents a theoretical justification for choosing the best follow up design of a uniform design to construct a uniform sequential design that involves a mixture of ω ≥ 1 factors with βk ≥ 2, 1 ≤ k ≤ ω levels. For illustration of the usage of the proposed results, a closer look is given at using these results for the most extensively used six particular cases, three symmetric and three asymmetric designs, which are often met in practice.

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References

  1. Fang K T and Li R, Uniform design for computer experiments and its optimal properties, Int. J. Materials and Product Technology, 2006, 25(1/2/3): 198–210.

    Google Scholar 

  2. Simpson T W, Lin D K J, and Chen W, Sampling strategies for computer experiments: Design and analysis, Int. J. Relability and Applications, 2001, 2(3): 209–240.

    Google Scholar 

  3. Fang K T, The uniform design: Application of number-theoretic methods in experimental design, Acta Math. Appl. Sinica, 1980, 3: 363–372.

    MathSciNet  Google Scholar 

  4. Wang Y and Fang K T, A note on uniform distribution and experimental design. Chin. Sci. Bull., 1981, 26: 485–489.

    MATH  Google Scholar 

  5. Hickernell F J, A generalized discrepancy and quadrature error bound, Math. Comp., 1998, 67: 299–322.

    Article  MathSciNet  MATH  Google Scholar 

  6. Hickernell F J, Lattice Rules: How Well Do They Measure Up? Random and Quasi-Random Point Sets, Eds. by Hellekalek P and Larcher G, Springer, New York, 1998.

    MATH  Google Scholar 

  7. Roth R M, Introduction to Coding Theory, Cambridge University Press, Cambridge, UK, 2006.

    Book  MATH  Google Scholar 

  8. Zhou Y D, Ning J H, and Song X B, Lee discrepancy and its applications in experimental designs, Statist. Probab. Lett., 2008, 78: 1933–1942.

    Article  MathSciNet  MATH  Google Scholar 

  9. Ji Y B, Alaerts G, Xu C J, et al., Sequential uniform designs for fingerprints development of Ginkgo biloba extracts by capillary electrophoresis, J. Chromatography, 2006, A1128: 273–281.

    Google Scholar 

  10. Tong C, Refinement strategies for stratified sampling methods, Reliability Engineering and System Safety, 2006, 91: 1257–1265.

    Article  Google Scholar 

  11. Durrieu G and Briollais L, Sequential design for microarray experiments, J. Amer. Statist. Association, 2009, 104: 650–660.

    Article  MathSciNet  MATH  Google Scholar 

  12. Loeppky J L, Moore L M, and Williams B J, Batch sequential designs for computer experiments, J. Statist. Plann. Inference, 2010, 140: 1452–1464.

    Article  MathSciNet  MATH  Google Scholar 

  13. Cheong K T W, Htay K, Tan R H C, et al., Identifying combinatorial growth inhibitory effects of various plant extracts on leukemia cells through systematic experimental design, Amer. J. Plant Sci., 2012, 3: 1390–1398.

    Article  Google Scholar 

  14. Elsawah A M, Constructing optimal router bit life sequential experimental designs: New results with a case study, Commun. Stat. Simulat. Comput., 2017, http://dx.doi.org/10.1080/03610918.2017.1397164.

    Google Scholar 

  15. Bullington K E, Hool J N, and Maghsoodloo S, A simple method for obtaining resolution IV designs for use with Taguchi orthogonal arrays, J. Qual. Technol., 1990, 22(4): 260–264.

    Google Scholar 

  16. Li W and Lin D K J, Optimal foldover plans for two-level fractional factorial designs, Technometrics, 2003, 45: 142–149.

    Article  MathSciNet  Google Scholar 

  17. Box, G E P, Hunter W G, and Hunter J S, Statistics for Experiments, John Wiley and Sons, New York, 1978.

    MATH  Google Scholar 

  18. Montgomery D C and Runger G C, Foldover of 2k−p resolution IV experimental designs, J. Qual. Technol., 1996, 28: 446–450.

    Article  Google Scholar 

  19. Li W, Lin D K J, and Ye K Q, Optimal foldover plans for non-regular orthogonal designs, Technometrics, 2003, 45: 347–351.

    Article  MathSciNet  Google Scholar 

  20. Li P F, Liu M Q, and Zhang R C, Choice of optimal initial designs in sequential experiments, Metrika, 2005, 61(2): 127–135.

    Article  MathSciNet  MATH  Google Scholar 

  21. Wang B, Robert G M, and John F B, A note on the selection of optimal foldover plans for 16- and 32-run fractional factorial designs, J. Stat. Plan. Inference, 2010, 140: 1497–1500.

    Article  MATH  Google Scholar 

  22. Fang K T, Lin D K J, and Qin H, A note on optimal foldover design, Statist. Probab. Lett., 2003, 62: 245–250.

    MathSciNet  MATH  Google Scholar 

  23. Elsawah A M and Qin H, An efficient methodology for constructing optimal foldover designs in terms of mixture discrepancy, J. Korean Statist. Soc., 2016, 45: 77–88.

    Article  MathSciNet  MATH  Google Scholar 

  24. Elsawah A M and Qin H, Optimum mechanism for breaking the confounding effects of mixed-level designs, Comput. Stat., 2017, 32(2): 781–802.

    Article  MathSciNet  MATH  Google Scholar 

  25. Ou Z and Qin H, Optimal foldover plans of asymmetric factorials with minimum wrap-around L2-discrepancy, Stat. Papers, 2017, DOI: 10.1007/s00362-017-0892-x.

    Google Scholar 

  26. Elsawah A M, A closer look at de-aliasing effects using an efficient foldover technique, Statistics, 2017, 51(3): 532–557.

    Article  MathSciNet  MATH  Google Scholar 

  27. Elsawah A M, A powerful and efficient algorithm for breaking the links between aliased effects in asymmetric designs, Aust. N. Z. J. Stat., 2017, 59(1): 17–41.

    MathSciNet  MATH  Google Scholar 

  28. Elsawah A M, Choice of optimal second stage designs in two-stage experiments, Comput. Stat., 2018, 33(2): 933–965

    Article  MathSciNet  MATH  Google Scholar 

  29. Elsawah A M and Qin H, A new strategy for optimal foldover two-level designs, Statist. Probab. Lett., 2015, 103: 116–126.

    MathSciNet  MATH  Google Scholar 

  30. Elsawah A M and Qin H, New lower bound for centered L2-discrepancy of four-level U-type designs, Statist. Probab. Lett., 2014, 93: 65–71.

    Article  MathSciNet  MATH  Google Scholar 

  31. Elsawah A M and Qin H, A new look on optimal foldover plans in terms of uniformity criteria, Commun. Stat. Theory Methods, 2017, 46(4): 1621–1635.

    Article  MathSciNet  MATH  Google Scholar 

  32. Elsawah A M, Constructing optimal asymmetric combined designs via Lee discrepancy, Statist. Probab. Lett., 2016, 118: 24–31.

    Article  MathSciNet  MATH  Google Scholar 

  33. Fang K T, Ke X, and Elsawah A M, Construction of uniform designs via an adjusted threshold accepting algorithm, J. Complexity, 2017, 43: 28–37.

    Article  MathSciNet  MATH  Google Scholar 

Download references

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Correspondence to A. M. Elsawah.

Additional information

This research was supported by the Beijing Normal University-Hong Kong Baptist University United International College under Grant Nos. R201409, R201712, and R201810 and the Zhuhai Premier Discipline Grant.

This paper was recommended for publication by Editor XU Jin.

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Elsawah, A.M. Designing Uniform Computer Sequential Experiments with Mixture Levels Using Lee Discrepancy. J Syst Sci Complex 32, 681–708 (2019). https://doi.org/10.1007/s11424-018-7173-1

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  • DOI: https://doi.org/10.1007/s11424-018-7173-1

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