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Optimal designs for minimax-criteria in random coefficient regression models

  • Maryna Prus
Regular Article
  • 6 Downloads

Abstract

We consider minimax-optimal designs for the prediction of individual parameters in random coefficient regression models. We focus on the minimax-criterion, which minimizes the “worst case” for the basic criterion with respect to the covariance matrix of random effects. We discuss particular models: linear and quadratic regression, in detail.

Keywords

Random coefficient regression Optimal designs Prediction Integrated mean squarer error Minimax-criterion 

Notes

Acknowledgements

The author is grateful to two anonymous referees and the guest editor for helpful comments which improved the presentation of the results.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute for Mathematical StochasticsOtto-von-Guericke University MagdeburgMagdeburgGermany

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