Bayesian D-Optimal and Model Robust Designs in Linear Regression Models
作者:
Holger Dette,
期刊:
Statistics
(Taylor Available online 1993)
卷期:
Volume 25,
issue 1
页码: 27-46
ISSN:0233-1888
年代: 1993
DOI:10.1080/02331889308802429
出版商: Gordon & Breach Science Publishers
关键词: Primary 62K04;secondary 62J05;Bayesian optimal designs;model robust designs;D-optimality;c-optimality;Elfving’s Theorem
数据来源: Taylor
摘要:
We consider the Bayesian optimal design problem in the usual linear regression model. A version of Elfving’s Theorem is proved for a model robust Bayesian c-optimality criterion. The optimal design minimizes a weighted product where the factors are proportional to the expected posterior risks of the Bayesian estimators for the linear combinations of the parameters in different models. The geometric characterizations are used to state sufficient conditions which guarantee that the classical and the Bayesian optimal designs are supported at the same set of points or are identical. The Bayesian D-optimal design problem appears as a special case in this setup considering “nested” models and special linear combinations for the paramater of the “highest coefficients” in different models. Thus sufficient conditions on the precision matrices of the prior distribution are found that the Bayesian D-optimal and the classical optimal design are supported at the same set of points or are identical. The results are illustrated in several examples.
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