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R-optimal designs in random coefficient regression models

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  • Liu, Xin
  • Yue, Rong-Xian
  • Chatterjee, Kashinath

Abstract

This paper considers R-optimal approximate designs for random coefficient regression models. An equivalence theorem for the R-optimality is provided for random coefficient regression models. Explicit expressions for R-optimal designs under linear regression models are presented for illustration.

Suggested Citation

  • Liu, Xin & Yue, Rong-Xian & Chatterjee, Kashinath, 2014. "R-optimal designs in random coefficient regression models," Statistics & Probability Letters, Elsevier, vol. 88(C), pages 127-132.
    • Handle: RePEc:eee:stapro:v:88:y:2014:i:c:p:127-132
    DOI: 10.1016/j.spl.2014.02.005
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    References listed on IDEAS

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    1. Thomas Schmelter, 2007. "The Optimality of Single-group Designs for Certain Mixed Models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 65(2), pages 183-193, February.
    2. Yu, Sheng-Hua, 2007. "The linear minimax estimator of stochastic regression coefficients and parameters under quadratic loss function," Statistics & Probability Letters, Elsevier, vol. 77(1), pages 54-62, January.
    3. Holger Dette, 1997. "Designing Experiments with Respect to ‘Standardized’ Optimality Criteria," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(1), pages 97-110.
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    Cited by:

    1. Cheng, Jing & Ai, Mingyao, 2020. "Optimal designs for panel data linear regressions," Statistics & Probability Letters, Elsevier, vol. 163(C).
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    3. He, Lei & He, Daojiang, 2020. "R-optimal designs for individual prediction in random coefficient regression models," Statistics & Probability Letters, Elsevier, vol. 159(C).

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