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Optimal designs in multiple group random coefficient regression models

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  • Maryna Prus

    (Otto von Guericke University)

Abstract

The subject of this work is multiple group random coefficients regression models with several treatments and one control group. Such models are often used for studies with cluster randomized trials. We investigate A-, D- and E-optimal designs for estimation and prediction of fixed and random treatment effects, respectively, and illustrate the obtained results by numerical examples.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:testjl:v:29:y:2020:i:1:d:10.1007_s11749-019-00654-6
    DOI: 10.1007/s11749-019-00654-6
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    References listed on IDEAS

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    1. Patton, G.C. & Bond, L. & Carlin, J.B. & Thomas, L. & Butler, H. & Glover, S. & Catalano, R. & Bowes, G., 2006. "Promoting social inclusion in schools: A group-randomized trial of effects on student health risk behavior and well-being," American Journal of Public Health, American Public Health Association, vol. 96(9), pages 1582-1587.
    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.
    3. 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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    Cited by:

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    2. Liu, Xin & Ye, Min & Yue, Rong-Xian, 2021. "Optimal designs for comparing population curves in hierarchical models," Statistics & Probability Letters, Elsevier, vol. 178(C).

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