IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v56y2002i4p425-430.html
   My bibliography  Save this article

The BLUPs are not "best" when it comes to bootstrapping

Author

Listed:
  • Morris, Jeffrey S.

Abstract

In the setting of mixed models, some researchers may construct a semiparametric bootstrap by sampling from the best linear unbiased predictor residuals. This paper demonstrates both mathematically and by simulation that such a bootstrap will consistently underestimate the variation in the data in finite samples.

Suggested Citation

  • Morris, Jeffrey S., 2002. "The BLUPs are not "best" when it comes to bootstrapping," Statistics & Probability Letters, Elsevier, vol. 56(4), pages 425-430, February.
    • Handle: RePEc:eee:stapro:v:56:y:2002:i:4:p:425-430
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(02)00041-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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Paul Anand & Jere R. Behrman & Hai‐Anh H. Dang & Sam Jones, 2019. "Does sorting matter for learning inequality?: Evidence from East Africa," WIDER Working Paper Series wp-2019-110, World Institute for Development Economic Research (UNU-WIDER).
    2. Shang, Junfeng & Cavanaugh, Joseph E., 2008. "Bootstrap variants of the Akaike information criterion for mixed model selection," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2004-2021, January.
    3. Flores-Agreda, Daniel & Cantoni, Eva, 2019. "Bootstrap estimation of uncertainty in prediction for generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 130(C), pages 1-17.
    4. Brian Gray & Vyacheslav Lyubchich & Yulia Gel & James Rogala & Dale Robertson & Xiaoqiao Wei, 2016. "Estimation of river and stream temperature trends under haphazard sampling," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 89-105, March.
    5. Shang, Junfeng & Cavanaugh, Joseph E., 2008. "An assumption for the development of bootstrap variants of the Akaike information criterion in mixed models," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1422-1429, September.

    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:56:y:2002:i:4:p:425-430. 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: 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.