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Optimal designs for minimax-criteria in random coefficient regression models

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A Publisher Correction to this article was published on 12 September 2019

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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.

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  • 12 September 2019

    Unfortunately, due to a technical error, the articles published in issues 60:2 and 60:3 received incorrect pagination. Please find here the corrected Tables of Contents. We apologize to the authors of the articles and the readers.

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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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Authors and Affiliations

  1. Institute for Mathematical Stochastics, Otto-von-Guericke University Magdeburg, PF 4120, 39016, Magdeburg, Germany

    Maryna Prus

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  1. Maryna Prus
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Correspondence to Maryna Prus.

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This research has been supported by Grant SCHW 531/16-1 of the German Research Foundation (DFG).

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Prus, M. Optimal designs for minimax-criteria in random coefficient regression models. Stat Papers 60, 465–478 (2019). https://doi.org/10.1007/s00362-018-01072-w

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  • DOI: https://doi.org/10.1007/s00362-018-01072-w

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