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Refinement strategies for stratified sampling methods

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  • Tong, Charles

Abstract

In many computer experiments the adequacy of a given sample to give acceptable statistical estimates cannot be determined a priori, and thus the ability to extend or refine an experimental design may be important. This paper describes refinement strategies for the class of stratified experimental designs such as latin hypercubes, orthogonal arrays, and factorial designs. A few applications are given to demonstrate their usefulness.

Suggested Citation

  • Tong, Charles, 2006. "Refinement strategies for stratified sampling methods," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1257-1265.
    • Handle: RePEc:eee:reensy:v:91:y:2006:i:10:p:1257-1265
    DOI: 10.1016/j.ress.2005.11.027
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    References listed on IDEAS

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    1. L. Ingber, 1989. "Very fast simulated re-annealing," Lester Ingber Papers 89vf, Lester Ingber.
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    Cited by:

    1. A. M. Elsawah & Kai-Tai Fang, 2020. "New foundations for designing U-optimal follow-up experiments with flexible levels," Statistical Papers, Springer, vol. 61(2), pages 823-849, April.
    2. Munoz Zuniga, M. & Garnier, J. & Remy, E. & de Rocquigny, E., 2011. "Adaptive directional stratification for controlled estimation of the probability of a rare event," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1691-1712.
    3. Sui Peng & Huixiang Chen & Yong Lin & Tong Shu & Xingyu Lin & Junjie Tang & Wenyuan Li & Weijie Wu, 2019. "Probabilistic Power Flow for Hybrid AC/DC Grids with Ninth-Order Polynomial Normal Transformation and Inherited Latin Hypercube Sampling," Energies, MDPI, vol. 12(16), pages 1-21, August.
    4. Jin Xu & Jiajie Chen & Peter Z. G. Qian, 2015. "Sequentially Refined Latin Hypercube Designs: Reusing Every Point," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1696-1706, December.
    5. Sallaberry, C.J. & Helton, J.C. & Hora, S.C., 2008. "Extension of Latin hypercube samples with correlated variables," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 1047-1059.
    6. Tong, Charles, 2010. "Self-validated variance-based methods for sensitivity analysis of model outputs," Reliability Engineering and System Safety, Elsevier, vol. 95(3), pages 301-309.
    7. Shields, Michael D. & Teferra, Kirubel & Hapij, Adam & Daddazio, Raymond P., 2015. "Refined Stratified Sampling for efficient Monte Carlo based uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 310-325.
    8. Blatman, Géraud & Sudret, Bruno, 2010. "Efficient computation of global sensitivity indices using sparse polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 95(11), pages 1216-1229.
    9. Sakurahara, Tatsuya & O'Shea, Nicholas & Cheng, Wen-Chi & Zhang, Sai & Reihani, Seyed & Kee, Ernie & Mohaghegh, Zahra, 2019. "Integrating renewal process modeling with Probabilistic Physics-of-Failure: Application to Loss of Coolant Accident (LOCA) frequency estimations in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    10. Qing Deng & Changsen Feng & Fushuan Wen & Chung-Li Tseng & Lei Wang & Bo Zou & Xizhu Zhang, 2019. "Evaluation of Accommodation Capability for Electric Vehicles of a Distribution System Considering Coordinated Charging Strategies," Energies, MDPI, vol. 12(16), pages 1-20, August.

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