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Improved EBLUPs in Mixed-Effects Regression Models

Author

Listed:
  • Sam W

    (Pfizer Inc., New York, USA)

  • Peijin X

    (Hershey, PA, New York, USA)

  • Ching RY

    (1Pfizer Inc., New York, USA)

  • Kelly HZ

    (Pfizer Inc., New York, USA)

Abstract

Mixed models are now heavily employed in business analysis, studies of public health, and in clinical research. However, widely used MLE based methods, the ML and the REML, for making inferences about the Best Linear Unbiased Predictor (BLUP) suffers from a number of drawbacks such as the non-convergence and lack of accuracy with small number of factor levels in a group structure. The BLUP in mixed models is a function of the variance components, which are typically estimated by MLE based method.

Suggested Citation

  • Sam W & Peijin X & Ching RY & Kelly HZ, 2018. "Improved EBLUPs in Mixed-Effects Regression Models," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 4(4), pages 78-86, January.
    • Handle: RePEc:adp:jbboaj:v:4:y:2018:i:4:p:78-86
    DOI: 10.19080/BBOAJ.2018.04.555641
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    References listed on IDEAS

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    1. Gamage, Jinadasa & Mathew, Thomas & Weerahandi, Samaradasa, 2013. "Generalized prediction intervals for BLUPs in mixed models," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 226-233.
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    Cited by:

    1. Hirche, Martin & Greenacre, Luke & Nenycz-Thiel, Magda & Loose, Simone & Lockshin, Larry, 2021. "SKU performance and distribution: A large-scale analysis of the role of product characteristics with store scanner data," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).

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