IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v54y2010i1p219-232.html
   My bibliography  Save this article

Uniform design over general input domains with applications to target region estimation in computer experiments

Author

Listed:
  • Chuang, S.C.
  • Hung, Y.C.

Abstract

The power of uniform design (UD) has received great attention in the area of computer experiments over the last two decades. However, when conducting a typical computer experiment, one finds many non-rectangular types of input domains on which traditional UD methods cannot be adequately applied. In this study, we propose a new UD method that is suitable for any type of design area. For practical implementation, we develop an efficient algorithm to construct a so-called nearly uniform design (NUD) and show that it approximates very well the UD solution for small sizes of experiment. By utilizing the proposed UD method, we also develop a methodology for estimating the target region of computer experiments. The methodology is sequential and aims to (i) provide adaptive models that predict well the output measures related to the experimental target; and (ii) minimize the number of experimental trials. Finally, we illustrate the developed methodology on various examples and show that, given the same experimental budget, it outperforms other approaches in estimating the prespecified target region of computer experiments.

Suggested Citation

  • Chuang, S.C. & Hung, Y.C., 2010. "Uniform design over general input domains with applications to target region estimation in computer experiments," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 219-232, January.
  • Handle: RePEc:eee:csdana:v:54:y:2010:i:1:p:219-232
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(09)00293-X
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Fang, Kai-Tai & Qin, Hong, 2003. "A note on construction of nearly uniform designs with large number of runs," Statistics & Probability Letters, Elsevier, vol. 61(2), pages 215-224, January.
    2. Huang, Chien-Ming & Lee, Yuh-Jye & Lin, Dennis K.J. & Huang, Su-Yun, 2007. "Model selection for support vector machines via uniform design," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 335-346, September.
    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. Lin, D.K.J. & Sharpe, C. & Winker, P., 2010. "Optimized U-type designs on flexible regions," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1505-1515, June.
    2. Chen, Ray-Bing & Hsu, Yen-Wen & Hung, Ying & Wang, Weichung, 2014. "Discrete particle swarm optimization for constructing uniform design on irregular regions," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 282-297.
    3. Zhang, Mei & Zhou, Yong-Dao, 2016. "Spherical discrepancy for designs on hyperspheres," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 226-234.
    4. Astrid Jourdan, 2024. "Space-filling designs with a Dirichlet distribution for mixture experiments," Statistical Papers, Springer, vol. 65(5), pages 2667-2686, July.
    5. Xiong, Zikang & Liu, Liwei & Ning, Jianhui & Qin, Hong, 2020. "Sphere packing design for experiments with mixtures," Statistics & Probability Letters, Elsevier, vol. 164(C).
    6. Ray-Bing Chen & Ying-Chao Hung & Weichung Wang & Sung-Wei Yen, 2013. "Contour estimation via two fidelity computer simulators under limited resources," Computational Statistics, Springer, vol. 28(4), pages 1813-1834, August.

    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. Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2009. "Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(6), pages 512-534.
    2. Talayeh Razzaghi & Oleg Roderick & Ilya Safro & Nicholas Marko, 2016. "Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-18, May.
    3. repec:hum:wpaper:sfb649dp2008-005 is not listed on IDEAS
    4. Yang, YouLong & Che, JinXing & Li, YanYing & Zhao, YanJun & Zhu, SuLing, 2016. "An incremental electric load forecasting model based on support vector regression," Energy, Elsevier, vol. 113(C), pages 796-808.
    5. Orestis P. Panagopoulos & Petros Xanthopoulos & Talayeh Razzaghi & Onur Şeref, 2019. "Relaxed support vector regression," Annals of Operations Research, Springer, vol. 276(1), pages 191-210, May.
    6. Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2007. "The Default Risk of Firms Examined with Smooth Support Vector Machines," Discussion Papers of DIW Berlin 757, DIW Berlin, German Institute for Economic Research.
    7. Zujun Ou & Kashinath Chatterjee & Hong Qin, 2011. "Lower bounds of various discrepancies on combined designs," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 74(1), pages 109-119, July.
    8. De Brabanter, K. & De Brabanter, J. & Suykens, J.A.K. & De Moor, B., 2010. "Optimized fixed-size kernel models for large data sets," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1484-1504, June.
    9. Onur Şeref & Talayeh Razzaghi & Petros Xanthopoulos, 2017. "Weighted relaxed support vector machines," Annals of Operations Research, Springer, vol. 249(1), pages 235-271, February.
    10. Zou, Na & Ren, Ping & Qin, Hong, 2009. "A note on Lee discrepancy," Statistics & Probability Letters, Elsevier, vol. 79(4), pages 496-500, February.
    11. Fasheng Sun & Jie Chen & Min-Qian Liu, 2011. "Connections between uniformity and aberration in general multi-level factorials," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 73(3), pages 305-315, May.
    12. Feng, Zhong-kai & Niu, Wen-jing & Cheng, Chun-tian & Wu, Xin-yu, 2017. "Optimization of hydropower system operation by uniform dynamic programming for dimensionality reduction," Energy, Elsevier, vol. 134(C), pages 718-730.

    More about this item

    Statistics

    Access and download statistics

    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:csdana:v:54:y:2010:i:1:p:219-232. 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: http://www.elsevier.com/locate/csda .

    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.