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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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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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