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Robustness properties of minimally‐supported Bayesian D‐optimal designs for heteroscedastic models
Author(s) -
Dette Holger,
Song Dale,
Wong Weng Kee
Publication year - 2001
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3316012
Subject(s) - robustness (evolution) , optimal design , heteroscedasticity , mathematics , bayesian probability , variance (accounting) , mathematical optimization , computer science , statistics , biochemistry , chemistry , gene , accounting , business
Bayesian D‐optimal designs supported on a fixed number of points were found by Dette & Wong (1998) for estimating parameters in a polynomial model when the error variance depends exponentially on the explanatory variable. The present authors provide optimal designs under a broader class of error variance structures and investigate the robustness properties of these designs to model and prior distribution assumptions. A comparison of the performance of the optimal designs relative to the popular uniform designs is also given. The authors' results suggest that Bayesian D‐optimal designs suported on a fixed number of points are more likely to be globaly optimal among all designs if the prior distribution is symmetric and is concentrated around its mean.