IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v170y2021ics0167715220302959.html
   My bibliography  Save this article

A note on optimal designs for estimating the slope of a polynomial regression

Author

Listed:
  • Dette, Holger
  • Melas, Viatcheslav B.
  • Shpilev, Petr

Abstract

In this note we consider the optimal design problem for estimating the slope of a polynomial regression with no intercept at a given point, say z. In contrast to previous work, we investigate the model on the non-symmetric interval.

Suggested Citation

  • Dette, Holger & Melas, Viatcheslav B. & Shpilev, Petr, 2021. "A note on optimal designs for estimating the slope of a polynomial regression," Statistics & Probability Letters, Elsevier, vol. 170(C).
    • Handle: RePEc:eee:stapro:v:170:y:2021:i:c:s0167715220302959
    DOI: 10.1016/j.spl.2020.108992
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715220302959
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2020.108992?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. Kim-Hung Li & Tai-Shing Lau & Chongqi Zhang, 2005. "A note on D-optimal designs for models with and without an intercept," Statistical Papers, Springer, vol. 46(3), pages 451-458, July.
    Full references (including those not matched with items on IDEAS)

    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. Nripes Mandal & Manisha Pal & Bikas Sinha & Premadhis Das, 2015. "Optimum mixture designs in a restricted region," Statistical Papers, Springer, vol. 56(1), pages 105-119, February.
    2. Dette, Holger & Melas, Viatcheslav B. & Shpilev, Petr, 2020. "Optimal designs for estimating individual coefficients in polynomial regression with no intercept," Statistics & Probability Letters, Elsevier, vol. 158(C).
    3. Idais, Osama, 2020. "A note on locally optimal designs for generalized linear models with restricted support," Statistics & Probability Letters, Elsevier, vol. 159(C).
    4. Hanen Hanna & Walter Tinsson, 2015. "A new class of designs for mixture-of-mixture experiments," Statistical Papers, Springer, vol. 56(2), pages 311-331, May.

    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:stapro:v:170:y:2021:i:c:s0167715220302959. 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/wps/find/journaldescription.cws_home/622892/description#description .

    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.