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Geometric characterization of D-optimal designs for random coefficient regression models

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

Abstract

In this paper we investigate geometric characterization of optimal designs for random coefficient regression models. We establish a generalization of Elving’s theorem for the D-optimality criterion for estimating the population parameters in random coefficient regression models. We also consider the geometric characterization of D-optimal designs for predicting the individual parameters.

Suggested Citation

  • Liu, Xin & Yue, Rong-Xian & Chatterjee, Kashinath, 2020. "Geometric characterization of D-optimal designs for random coefficient regression models," Statistics & Probability Letters, Elsevier, vol. 159(C).
    • Handle: RePEc:eee:stapro:v:159:y:2020:i:c:s0167715219303426
    DOI: 10.1016/j.spl.2019.108696
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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. Tim Holland‐Letz & Holger Dette & Andrey Pepelyshev, 2011. "A geometric characterization of optimal designs for regression models with correlated observations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(2), pages 239-252, March.
    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.
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