IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v81y2019i1p101-120.html
   My bibliography  Save this article

Characterization of c‐, L‐ and ϕk‐optimal designs for a class of non‐linear multiple‐regression models

Author

Listed:
  • Dennis Schmidt

Abstract

Optimal designs for multiple‐regression models are determined. We consider a general class of non‐linear models including proportional hazards models with different censoring schemes, the Poisson and the negative binomial model. For these models we provide a complete characterization of c‐optimal designs for all vectors c in the case of a single covariate. For multiple regression with an arbitrary number of covariates, c‐optimal designs for certain vectors c are derived analytically. Using some general results on the structure of optimal designs for multiple regression, we determine L‐ and ϕk‐optimal designs for models with an arbitrary number of covariates.

Suggested Citation

  • Dennis Schmidt, 2019. "Characterization of c‐, L‐ and ϕk‐optimal designs for a class of non‐linear multiple‐regression models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(1), pages 101-120, February.
    • Handle: RePEc:bla:jorssb:v:81:y:2019:i:1:p:101-120
    DOI: 10.1111/rssb.12292
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssb.12292
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssb.12292?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
    ---><---

    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:81:y:2019:i:1:p:101-120. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.