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Interpolation and Extrapolation in Random Coefficient Regression Models: Optimal Design for Prediction

  • Maryna PrusEmail author
  • Rainer Schwabe
Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Abstract

The problem of optimal design for the prediction of individual parameters in random coefficient regression in the particular case of a given population mean was considered by Gladitz and Pilz (Statistics 13:371–385, 1982). In the more general situation, where the population parameter is unknown, D- and L-optimal designs were discussed in Prus and Schwabe (J R Stat Soc Ser B, 78:175–191). Here we present analytical results for designs which are optimal for prediction in the case of interpolation as well as extrapolation of the individual response.

Keywords

Fixed Effect Model Individual Parameter Error Matrix Dispersion Matrix Linear Unbiased Predictor 
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 International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute for Mathematical StochasticsOtto-von-Guericke UniversityMagdeburgGermany

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