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

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  • 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
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    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.

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