Mixed Models and Variance Components

  • Ronald Christensen
Part of the Springer Texts in Statistics book series (STS)


Traditionally, linear models have been divided into three categories: fixed effects models, random effects models, and mixed models. The categorization depends on whether the β vector in Y = + e is fixed, random, or has both fixed and random elements. Random effects models always assume that there is a fixed overall mean for observations, so random effects models are actually mixed.


Variance Component Orthogonal Complement Balance Incomplete Block Design Good Linear Unbiased Predictor Linear Unbiased Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • Ronald Christensen
    • 1
  1. 1.Department of Mathematics and StatisticsUniversity of New MexicoAlbuquerqueUSA

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