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Bayesian and maximin optimal designs for heteroscedastic regression models
Authors:Holger Dette  Linda M Haines  Lorens A Imhof
Abstract:The authors consider the problem of constructing standardized maximin D‐optimal designs for weighted polynomial regression models. In particular they show that by following the approach to the construction of maximin designs introduced recently by Dette, Haines & Imhof (2003), such designs can be obtained as weak limits of the corresponding Bayesian q‐optimal designs. They further demonstrate that the results are more broadly applicable to certain families of nonlinear models. The authors examine two specific weighted polynomial models in some detail and illustrate their results by means of a weighted quadratic regression model and the Bleasdale–Nelder model. They also present a capstone example involving a generalized exponential growth model.
Keywords:Bayesian design  D‐optimal design  maximin design  polynomial regression  standardized criterion  
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