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Finding Near-Optimal Bayesian Experimental Designs via Genetic Algorithms

Author

Listed:
  • Hamada M.
  • Martz H. F.
  • Reese C. S.
  • Wilson A. G.

Abstract

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Suggested Citation

  • Hamada M. & Martz H. F. & Reese C. S. & Wilson A. G., 2001. "Finding Near-Optimal Bayesian Experimental Designs via Genetic Algorithms," The American Statistician, American Statistical Association, vol. 55, pages 175-181, August.
    • Handle: RePEc:bes:amstat:v:55:y:2001:m:august:p:175-181
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    Citations

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    Cited by:

    1. Zsolt Sándor & Michel Wedel, 2002. "Profile Construction in Experimental Choice Designs for Mixed Logit Models," Marketing Science, INFORMS, vol. 21(4), pages 455-475, February.
    2. repec:jss:jstsof:25:i06 is not listed on IDEAS
    3. Carlos de la Calle-Arroyo & Miguel A. González-Fernández & Licesio J. Rodríguez-Aragón, 2023. "Optimal Designs for Antoine’s Equation: Compound Criteria and Multi-Objective Designs via Genetic Algorithms," Mathematics, MDPI, vol. 11(3), pages 1-16, January.
    4. García-Ródenas, Ricardo & García-García, José Carlos & López-Fidalgo, Jesús & Martín-Baos, José Ángel & Wong, Weng Kee, 2020. "A comparison of general-purpose optimization algorithms for finding optimal approximate experimental designs," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    5. Gray, J. Brian & Fan, Guangzhe, 2008. "Classification tree analysis using TARGET," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1362-1372, January.
    6. Masoudi, Ehsan & Holling, Heinz & Wong, Weng Kee, 2017. "Application of imperialist competitive algorithm to find minimax and standardized maximin optimal designs," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 330-345.
    7. Sessions, David N. & Stevans, Lonnie K., 2006. "Investigating omitted variable bias in regression parameter estimation: A genetic algorithm approach," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2835-2854, June.

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