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Optimal designs for prediction of random effects in two-groups models with multivariate response

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  • Prus, Maryna

Abstract

In this work an analytical solution is proposed for optimal designs for the prediction of individual random effects and the group difference in two-groups models with multivariate response. The solution is given by optimality conditions for approximate designs. In particular two-groups models with the same regression function for both groups, Bayesian optimal designs are optimal for the prediction of the group difference. The results are illustrated by examples of linear and bi-linear regression.

Suggested Citation

  • Prus, Maryna, 2023. "Optimal designs for prediction of random effects in two-groups models with multivariate response," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
    • Handle: RePEc:eee:jmvana:v:198:y:2023:i:c:s0047259x23000581
    DOI: 10.1016/j.jmva.2023.105212
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    References listed on IDEAS

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    1. Maryna Prus & Hans-Peter Piepho, 2021. "Optimizing the Allocation of Trials to Sub-regions in Multi-environment Crop Variety Testing," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(2), pages 267-288, June.
    2. Xin Liu & Rong-Xian Yue & Weng Kee Wong, 2019. "D-optimal designs for multi-response linear mixed models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(1), pages 87-98, January.
    3. Maryna Prus & Rainer Schwabe, 2016. "Optimal designs for the prediction of individual parameters in hierarchical models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 175-191, January.
    4. Harman, Radoslav & Prus, Maryna, 2018. "Computing optimal experimental designs with respect to a compound Bayes risk criterion," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 135-141.
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