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Bayesian and maximin optimal designs for heteroscedastic regression models

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  • Dette, Holger
  • Haines, Linda M.
  • Imhof, Lorens A.

Abstract

The problem of constructing standardized maximin D-optimal designs for weighted polynomial regression models is addressed. In particular it is shown that, by following the broad approach to the construction of maximin designs introduced recently by Dette, Haines and Imhof (2003), such designs can be obtained as weak limits of the corresponding Bayesian Φq-optimal designs. The approach is illustrated for two specific weighted polynomial models and also for a particular growth model.

Suggested Citation

  • 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.
    • Handle: RePEc:zbw:sfb475:200336
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    1. Biedermann, Stefanie & Dette, Holger, 2003. "A note on maximin and Bayesian D-optimal designs in weighted polynomial regression," Technical Reports 2003,03, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Dette, Holger & Haines, Linda M. & Imhof, Lorens A., 2003. "Maximin and Bayesian optimal designs for regression models," Technical Reports 2003,10, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    3. 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.
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