Optimal designs for model averaging in non-nested models
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Date
2019
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Abstract
In this paper we construct optimal designs for frequentist model averaging estimation.
We derive the asymptotic distribution of the model averaging estimate with fixed weights
in the case where the competing models are non-nested and none of these models is correctly
specified. A Bayesian optimal design minimizes an expectation of the asymptotic
mean squared error of the model averaging estimate calculated with respect to a suitable
prior distribution. We demonstrate that Bayesian optimal designs can improve the
accuracy of model averaging substantially. Moreover, the derived designs also improve
the accuracy of estimation in a model selected by model selection and model averaging
estimates with random weights.
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Keywords
model selection, Bayesian optimal designs, optimal design, model uncertainty, model averaging