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Computing efficient exact designs of experiments using integer quadratic programming

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  • Harman, Radoslav
  • Filová, Lenka

Abstract

A new method for computing exact experimental designs for linear regression models by integer quadratic programming is proposed. The key idea is to use the criterion of DQ-optimality, which is a quadratic approximation of the criterion of D-optimality in the neighbourhood of the approximate D-optimal information matrix. Several numerical examples are used to demonstrate that the D-efficiency of exact DQ-optimal designs is usually very high. An important advantage of this method is that it can be applied to situations with general linear constraints on permissible designs, including marginal and cost constraints.

Suggested Citation

  • Harman, Radoslav & Filová, Lenka, 2014. "Computing efficient exact designs of experiments using integer quadratic programming," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1159-1167.
    • Handle: RePEc:eee:csdana:v:71:y:2014:i:c:p:1159-1167
    DOI: 10.1016/j.csda.2013.02.021
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    References listed on IDEAS

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    1. Martin-Martin, R. & Torsney, B. & Lopez-Fidalgo, J., 2007. "Construction of marginally and conditionally restricted designs using multiplicative algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5547-5561, August.
    2. Turkington,Darrell A., 2002. "Matrix Calculus and Zero-One Matrices," Cambridge Books, Cambridge University Press, number 9780521807883.
    3. L. Imhof & J. Lopez‐Fidalgo & W. K. Wong, 2001. "Efficiencies of Rounded Optimal Approximate Designs for Small Samples," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 55(3), pages 301-318, November.
    4. Pace, R. Kelley & LeSage, James P., 2004. "Chebyshev approximation of log-determinants of spatial weight matrices," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 179-196, March.
    5. R. A. Bailey, 2007. "Designs for two‐colour microarray experiments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(4), pages 365-394, August.
    6. Grace Montepiedra, 1998. "Application of genetic algorithms to the construction of exact D-optimal designs," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(6), pages 817-826.
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    Cited by:

    1. Harman, Radoslav & Prus, Maryna, 2018. "Computing optimal experimental designs with respect to a compound Bayes risk criterion," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 135-141.
    2. Belmiro P. M. Duarte, 2023. "Exact Optimal Designs of Experiments for Factorial Models via Mixed-Integer Semidefinite Programming," Mathematics, MDPI, vol. 11(4), pages 1-17, February.
    3. Lenka Filová & Radoslav Harman, 2020. "Ascent with quadratic assistance for the construction of exact experimental designs," Computational Statistics, Springer, vol. 35(2), pages 775-801, June.
    4. Radoslav Harman & Eva Benková, 2017. "Barycentric algorithm for computing D-optimal size- and cost-constrained designs of experiments," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(2), pages 201-225, February.

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