IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v83y2013i6p1479-1483.html
   My bibliography  Save this article

On the optimality of extended maximal length linear feedback shift register sequences

Author

Listed:
  • Kao, Ming-Hung

Abstract

Maximal length linear feedback shift register sequences (or m-sequences) are widely used as experimental designs for functional magnetic resonance imaging (fMRI) experiments. However, the statistical optimality of these designs has not been proven. Here, we present and study an extended version of m-sequences and show that these designs are optimal.

Suggested Citation

  • Kao, Ming-Hung, 2013. "On the optimality of extended maximal length linear feedback shift register sequences," Statistics & Probability Letters, Elsevier, vol. 83(6), pages 1479-1483.
    • Handle: RePEc:eee:stapro:v:83:y:2013:i:6:p:1479-1483
    DOI: 10.1016/j.spl.2013.02.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715213000552
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2013.02.012?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. Ming-Hung Kao & Abhyuday Mandal & John Stufken, 2012. "Constrained multiobjective designs for functional magnetic resonance imaging experiments via a modified non-dominated sorting genetic algorithm," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(4), pages 515-534, August.
    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. Kao, Ming-Hung, 2014. "A new type of experimental designs for event-related fMRI via Hadamard matrices," Statistics & Probability Letters, Elsevier, vol. 84(C), pages 108-112.

    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. Moein Saleh & Ming-Hung Kao & Rong Pan, 2017. "Design D-optimal event-related functional magnetic resonance imaging experiments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 73-91, January.

    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:83:y:2013:i:6:p:1479-1483. 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.