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A Further Study of Predictions in Linear Mixed Models

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  • Hu Yang
  • Huiliang Ye
  • Kai Xue

Abstract

This article is concerned with the prediction problems in linear mixed models (LMM). Both biased predictors and restricted predictors are introduced. It was found that the mean square error matrix (MSEM) of a predictor strongly depends on the MSEM of corresponding estimator of the fixed effects and precise formulas are obtained. As an application, we propose three new predictors to improve the best linear unbiased predictor (BLUP). The performance of the new predictors can be examined easily with the help of vast literature on the linear regression models (LM). We also illustrate our findings with a Monte Carlo simulation and a numerical example.

Suggested Citation

  • Hu Yang & Huiliang Ye & Kai Xue, 2014. "A Further Study of Predictions in Linear Mixed Models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(20), pages 4241-4252, October.
    • Handle: RePEc:taf:lstaxx:v:43:y:2014:i:20:p:4241-4252
    DOI: 10.1080/03610926.2012.725497
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    Cited by:

    1. M. Revan Özkale & Funda Can, 2017. "An evaluation of ridge estimator in linear mixed models: an example from kidney failure data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(12), pages 2251-2269, September.

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