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Computing optimal experimental designs with respect to a compound Bayes risk criterion

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  • Harman, Radoslav
  • Prus, Maryna

Abstract

We consider the problem of computing optimal experimental designs with respect to a compound Bayes risk criterion, which includes various specific criteria, such as a linear criterion for prediction in a random coefficient regression model. We prove that this problem can be converted into a problem of constrained A-optimality in an artificial model, which allows us to directly use existing theoretical results and software tools. We demonstrate the application of the proposed method for the optimal design of a random coefficient regression model with respect to an integrated mean squared error criterion.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:stapro:v:137:y:2018:i:c:p:135-141
    DOI: 10.1016/j.spl.2018.01.017
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    References listed on IDEAS

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    1. Harman, Radoslav & Filová, Lenka, 2014. "Computing efficient exact designs of experiments using integer quadratic programming," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1159-1167.
    2. 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.
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    Cited by:

    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. 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).
    3. Maryna Prus, 2020. "Optimal designs in multiple group random coefficient regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 233-254, March.

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