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D-optimal designs for hierarchical linear models with intraclass covariance structure

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Listed:
  • Lei He

    (Anhui Normal University)

  • Rong-Xian Yue

    (Shanghai Normal University)

Abstract

Intraclass correlation, used for measuring the degree of intrafamily resemblance, arises typically in psychology, education and genetics. In this paper, we extend the popular intraclass correlation model to the framework of hierarchical linear mixed models and consider D-optimal designs for the estimation of the fixed effects as well as the prediction of random effects in such settings. Moreover, characterizations of optimal designs are derived for determining the optimality of designs, and several examples are presented for illustration.

Suggested Citation

  • Lei He & Rong-Xian Yue, 2021. "D-optimal designs for hierarchical linear models with intraclass covariance structure," Statistical Papers, Springer, vol. 62(3), pages 1349-1361, June.
    • Handle: RePEc:spr:stpapr:v:62:y:2021:i:3:d:10.1007_s00362-019-01139-2
    DOI: 10.1007/s00362-019-01139-2
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    References listed on IDEAS

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    1. Maryna Prus, 2019. "Optimal designs for minimax-criteria in random coefficient regression models," Statistical Papers, Springer, vol. 60(2), pages 465-478, April.
    2. Zhang, Duo & Wang, Min, 2018. "Objective Bayesian inference for the intraclass correlation coefficient in linear models," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 292-296.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

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