IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v91y2006i10p1257-1265.html
   My bibliography  Save this article

Refinement strategies for stratified sampling methods

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832005002358
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2005.11.027?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. L. Ingber, 1989. "Very fast simulated re-annealing," Lester Ingber Papers 89vf, Lester Ingber.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Olympia Roeva & Gergana Roeva & Elena Chorukova, 2024. "Crow Search Algorithm for Modelling an Anaerobic Digestion Process: Algorithm Parameter Influence," Mathematics, MDPI, vol. 12(15), pages 1-20, July.
    11. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. L. Ingber, 2015. "Synergy among multiple scales of neocortical interactions," Lester Ingber Papers 15sc, Lester Ingber.
    2. L. Ingber & B. Rosen, 1992. "Genetic algorithms and very fast simulated reannealing: A comparison," Lester Ingber Papers 92ga, Lester Ingber.
    3. Sakata, Shinichi & White, Halbert, 2001. "S-estimation of nonlinear regression models with dependent and heterogeneous observations," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 5-72, July.
    4. Amir Atiya & Steve Wall, 2009. "An analytic approximation of the likelihood function for the Heston model volatility estimation problem," Quantitative Finance, Taylor & Francis Journals, vol. 9(3), pages 289-296.
    5. Moriguchi, Kai & Ueki, Tatsuhito & Saito, Masashi, 2020. "Establishing optimal forest harvesting regulation with continuous approximation," Operations Research Perspectives, Elsevier, vol. 7(C).
    6. Chang-Yong Lee & Dongju Lee, 2014. "Determination of initial temperature in fast simulated annealing," Computational Optimization and Applications, Springer, vol. 58(2), pages 503-522, June.
    7. Gerber, Mathieu & Bornn, Luke, 2018. "Convergence results for a class of time-varying simulated annealing algorithms," Stochastic Processes and their Applications, Elsevier, vol. 128(4), pages 1073-1094.
    8. Preminger, Arie & Franck, Raphael, 2007. "Forecasting exchange rates: A robust regression approach," International Journal of Forecasting, Elsevier, vol. 23(1), pages 71-84.
    9. L. Ingber, 2018. "Quantum Variables in Finance and Neuroscience," Lester Ingber Papers 18qv, Lester Ingber.
    10. Sha Lin & Xin-Jiang He, 2022. "Analytically Pricing European Options under a New Two-Factor Heston Model with Regime Switching," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1069-1085, March.
    11. L. Ingber & H. Fujio & M.F. Wehner, 1991. "Mathematical comparison of combat computer models to exercise data," Lester Ingber Papers 91mc, Lester Ingber.
    12. L. Ingber, 2022. "Quantum Variables in Finance," Lester Ingber Papers 22qv, Lester Ingber.
    13. L. Ingber, 2018. "Model of Models (MOM)," Lester Ingber Papers 18mo, Lester Ingber.
    14. L. Ingber & D.D. Sworder, 1991. "Statistical mechanics of combat with human factors," Lester Ingber Papers 91ch, Lester Ingber.
    15. L. Ingber, 1994. "Statistical mechanics of neocortical interactions: Path-integral evolution of short-term memory," Lester Ingber Papers 94ni, Lester Ingber.
    16. Ingber, Lester, 2000. "High-resolution path-integral development of financial options," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 283(3), pages 529-558.
    17. L. Ingber, 2018. "Quantum calcium-ion interactions with EEG," Lester Ingber Papers 18qc, Lester Ingber.
    18. Michael Saah Hayford & Bithin Datta, 2021. "Source Characterization of Multiple Reactive Species at an Abandoned Mine Site Using a Groundwater Numerical Simulation Model and Optimization Models," IJERPH, MDPI, vol. 18(9), pages 1-42, April.
    19. Dimitris Bertsimas & Omid Nohadani, 2010. "Robust optimization with simulated annealing," Journal of Global Optimization, Springer, vol. 48(2), pages 323-334, October.
    20. Sakata, Shinichi, 2007. "Instrumental variable estimation based on conditional median restriction," Journal of Econometrics, Elsevier, vol. 141(2), pages 350-382, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:91:y:2006:i:10:p:1257-1265. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.