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A new monotonic algorithm for the E-optimal experiment design problem

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  • Sahu, Nitesh
  • Babu, Prabhu

Abstract

In this paper, we develop a new monotonic algorithm for the E-optimal design problem, for which no simple monotonic algorithm is known to exist, using the idea of majorization–minimization. The available algorithms in the literature have no simple closed update equations whereas the proposed new algorithm has simple closed form update equations. The new algorithm is illustrated through numerical examples, and is shown to be competitive compared with the interior point method and existing state-of-the-art algorithm.

Suggested Citation

  • Sahu, Nitesh & Babu, Prabhu, 2021. "A new monotonic algorithm for the E-optimal experiment design problem," Statistics & Probability Letters, Elsevier, vol. 174(C).
    • Handle: RePEc:eee:stapro:v:174:y:2021:i:c:s0167715221000596
    DOI: 10.1016/j.spl.2021.109097
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    References listed on IDEAS

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    1. Duarte, Belmiro P.M. & Sagnol, Guillaume & Wong, Weng Kee, 2018. "An algorithm based on semidefinite programming for finding minimax optimal designs," Computational Statistics & Data Analysis, Elsevier, vol. 119(C), pages 99-117.
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    4. Dette, Holger & Pepelyshev, Andrey & Zhigljavsky, Anatoly, 2008. "Improving updating rules in multiplicative algorithms for computing D-optimal designs," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 312-320, December.
    5. Versyck, K.J. & Claes, J.E. & Van Impe, J.F., 1998. "Optimal experimental design for practical identification of unstructured growth models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 46(5), pages 621-629.
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