IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v71y2009i4p805-829.html
   My bibliography  Save this article

Robust discrimination designs

Author

Listed:
  • Douglas P. Wiens

Abstract

Summary. We study the construction of experimental designs, the purpose of which is to aid in the discrimination between two possibly non‐linear regression models, each of which might be only approximately specified. A rough description of our approach is that we impose neighbourhood structures on each regression response and determine the members of these neighbourhoods which are least favourable in the sense of minimizing the Kullback–Leibler divergence. Designs are obtained which maximize this minimum divergence. Both static and sequential approaches are studied. We then consider sequential designs whose purpose is initially to discriminate, but which move their emphasis towards efficient estimation or prediction as one model becomes favoured over the other.

Suggested Citation

  • Douglas P. Wiens, 2009. "Robust discrimination designs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(4), pages 805-829, September.
    • Handle: RePEc:bla:jorssb:v:71:y:2009:i:4:p:805-829
    DOI: 10.1111/j.1467-9868.2009.00711.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9868.2009.00711.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9868.2009.00711.x?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
    ---><---

    References listed on IDEAS

    as
    1. Biedermann, Stefanie & Dette, Holger, 2001. "Optimal designs for testing the functional form of a regression via nonparametric estimation techniques," Statistics & Probability Letters, Elsevier, vol. 52(2), pages 215-224, April.
    2. Holger Dette, 2004. "A comparison of sequential and non-sequential designs for discrimination between nested regression models," Biometrika, Biometrika Trust, vol. 91(1), pages 165-176, March.
    3. Wiens, Douglas P., 1991. "Designs for approximately linear regression: two optimality properties of uniform designs," Statistics & Probability Letters, Elsevier, vol. 12(3), pages 217-221, September.
    4. Dette, Holger & Titoff, Stefanie, 2008. "Optimal discrimination designs," Technical Reports 2008,06, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    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. Ghosh, Subir & Dutta, Santanu, 2013. "Robustness of designs for model discrimination," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 193-203.
    2. Dette, Holger & Melas, Viatcheslav B. & Shpilev, Petr, 2017. "T-optimal discriminating designs for Fourier regression models," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 196-206.
    3. Duarte, Belmiro P.M. & Wong, Weng Kee & Atkinson, Anthony C., 2015. "A Semi-Infinite Programming based algorithm for determining T-optimum designs for model discrimination," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 11-24.

    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. Renata Eirini Tsirpitzi & Frank Miller & Carl-Fredrik Burman, 2023. "Robust optimal designs using a model misspecification term," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(7), pages 781-804, October.
    2. Wiens, Douglas P., 2010. "Robustness of design for the testing of lack of fit and for estimation in binary response models," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3371-3378, December.
    3. Bischoff, Wolfgang & Miller, Frank, 2006. "Lack-of-fit-efficiently optimal designs to estimate the highest coefficient of a polynomial with large degree," Statistics & Probability Letters, Elsevier, vol. 76(15), pages 1701-1704, September.
    4. Dette, Holger & Wiens, Douglas P., 2007. "Robust designs for 3D shape analysis with spherical harmonic descriptors," Technical Reports 2007,12, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    5. Linglong Kong & Douglas P. Wiens, 2015. "Model-Robust Designs for Quantile Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 233-245, March.
    6. Mandal, Nripes Kumar & Pal, Manisha, 2013. "Maximin designs for the detection of synergistic effects," Statistics & Probability Letters, Elsevier, vol. 83(7), pages 1632-1637.
    7. Miller, Frank & Dette, Holger & Guilbaud, Olivier, 2007. "Optimal designs for estimating the interesting part of a dose-effect curve," Technical Reports 2007,21, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    8. Xiaojian Xu & Xiaoli Shang, 2017. "D-optimal designs for full and reduced Fourier regression models," Statistical Papers, Springer, vol. 58(3), pages 811-829, September.
    9. Biedermann, Stefanie & Dette, Holger, 2000. "Optimal designs for testing the functional form of a regression via nonparametric estimation techniques," Technical Reports 2000,41, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    10. Hengzhen Huang & Hong†Bin Fang & Ming T. Tan, 2018. "Experimental design for multi†drug combination studies using signaling networks," Biometrics, The International Biometric Society, vol. 74(2), pages 538-547, June.
    11. Ramón Ardanuy & J. López-Fidalgo & Patrick Laycock & Weng Wong, 1999. "When is an Equally-Weighted Design D-optimal?," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 51(3), pages 531-540, September.
    12. Huang, Hengzhen & Chen, Xueping, 2021. "Compromise design for combination experiment of two drugs," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    13. Biedermann, Stefanie & Dette, Holger, 2001. "Optimal designs for testing the functional form of a regression via nonparametric estimation techniques," Statistics & Probability Letters, Elsevier, vol. 52(2), pages 215-224, April.

    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:bla:jorssb:v:71:y:2009:i:4:p:805-829. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.