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Minimax D-Optimal Designs for the Logistic Model

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  • Joy King
  • Weng-Kee Wong

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  • Joy King & Weng-Kee Wong, 2000. "Minimax D-Optimal Designs for the Logistic Model," Biometrics, The International Biometric Society, vol. 56(4), pages 1263-1267, December.
    • Handle: RePEc:bla:biomet:v:56:y:2000:i:4:p:1263-1267
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    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2000.01263.x
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    References listed on IDEAS

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    1. Salomon Minkin & Kiran Kundhal, 1999. "Likelihood-Based Experimental Design for Estimation of Ed 50," Biometrics, The International Biometric Society, vol. 55(4), pages 1030-1037, December.
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    Cited by:

    1. 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).
    2. Sheng Wu & Weng Kee Wong & Catherine M. Crespi, 2017. "Maximin optimal designs for cluster randomized trials," Biometrics, The International Biometric Society, vol. 73(3), pages 916-926, September.
    3. Chang Li & Daniel C. Coster, 2022. "Improved Particle Swarm Optimization Algorithms for Optimal Designs with Various Decision Criteria," Mathematics, MDPI, vol. 10(13), pages 1-16, July.
    4. J. López Fidalgo & I. M. Ortiz Rodr�guez & Weng Kee Wong, 2011. "Design issues for population growth models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(3), pages 501-512, November.
    5. Belmiro P. M. Duarte & Weng Kee Wong, 2015. "Finding Bayesian Optimal Designs for Nonlinear Models: A Semidefinite Programming-Based Approach," International Statistical Review, International Statistical Institute, vol. 83(2), pages 239-262, August.
    6. Dette, Holger & Haines, Linda M. & Imhof, Lorens A., 2003. "Bayesian and maximin optimal designs for heteroscedastic regression models," Technical Reports 2003,36, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    7. Tekle, Fetene B. & Tan, Frans E.S. & Berger, Martijn P.F., 2008. "Maximin D-optimal designs for binary longitudinal responses," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5253-5262, August.
    8. Maryam Safarkhani & Mirjam Moerbeek, 2016. "D-optimal designs for a continuous predictor in longitudinal trials with discrete-time survival endpoints," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(2), pages 146-171, May.
    9. Karvanen, Juha & Kulathinal, Sangita & Gasbarra, Dario, 2009. "Optimal designs to select individuals for genotyping conditional on observed binary or survival outcomes and non-genetic covariates," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1782-1793, March.
    10. 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.
    11. Adewale, Adeniyi J. & Xu, Xiaojian, 2010. "Robust designs for generalized linear models with possible overdispersion and misspecified link functions," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 875-890, April.

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