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A review of some exchange algorithms for constructing discrete D-optimal designs

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  • Nguyen, Nam-Ky
  • Miller, Alan J.

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  • Nguyen, Nam-Ky & Miller, Alan J., 1992. "A review of some exchange algorithms for constructing discrete D-optimal designs," Computational Statistics & Data Analysis, Elsevier, vol. 14(4), pages 489-498, November.
    • Handle: RePEc:eee:csdana:v:14:y:1992:i:4:p:489-498
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    Cited by:

    1. Bergtold, Jason S. & Shanoyan, Aleksan & Fewell, Jason E. & Williams, Jeffery R., 2017. "Annual bioenergy crops for biofuels production: Farmers' contractual preferences for producing sweet sorghum," Energy, Elsevier, vol. 119(C), pages 724-731.
    2. 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).
    3. Fewell, Jason E. & Bergtold, Jason S. & Williams, Jeffery R., 2016. "Farmers' willingness to contract switchgrass as a cellulosic bioenergy crop in Kansas," Energy Economics, Elsevier, vol. 55(C), pages 292-302.
    4. Moein Saleh & Ming-Hung Kao & Rong Pan, 2017. "Design D-optimal event-related functional magnetic resonance imaging experiments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 73-91, January.
    5. CUERVO, Daniel Palhazi & GOOS, Peter & SÖRENSEN, Kenneth, 2013. "An iterated local search algorithm for the construction of large scale D-optimal experimental designs," Working Papers 2013006, University of Antwerp, Faculty of Business and Economics.
    6. Sung Jung, Joo & Jin Yum, Bong, 1996. "Construction of exact D-optimal designs by tabu search," Computational Statistics & Data Analysis, Elsevier, vol. 21(2), pages 181-191, February.
    7. Nguyen, Nam-Ky & Liu, Min-Qian, 2008. "An algorithmic approach to constructing mixed-level orthogonal and near-orthogonal arrays," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5269-5276, August.
    8. Arnouts, Heidi & Goos, Peter, 2010. "Update formulas for split-plot and block designs," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3381-3391, December.
    9. Szu Hui Ng & Stephen E. Chick, 2004. "Design of follow‐up experiments for improving model discrimination and parameter estimation," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(8), pages 1129-1148, December.
    10. De Zhang & Mingqiang Li & Feng Zhang & Maojun Fan, 2019. "New gradient methods for sensor selection problems," International Journal of Distributed Sensor Networks, , vol. 15(3), pages 15501477198, March.
    11. Wachowicz, Tomasz & Roszkowska, Ewa, 2022. "Can holistic declaration of preferences improve a negotiation offer scoring system?," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1018-1032.
    12. Dongying Wang & Sumin Wang, 2023. "Constructing Optimal Designs for Order-of-Addition Experiments Using a Hybrid Algorithm," Mathematics, MDPI, vol. 11(11), pages 1-20, May.
    13. Godolphin, J.D. & Warren, H.R., 2014. "An efficient procedure for the avoidance of disconnected incomplete block designs," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1134-1146.

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