Optimal designs for frequentist model averaging

Alhorn, K. and Schorning, K. and Dette, H.

Volume: 106 Pages: 665-682
DOI: 10.1093/biomet/asz036
Published: 2019

We consider the problem of designing experiments for estimating a target parameter in regression analysis when there is uncertainty about the parametric form of the regression function. A newoptimality criterion is proposed that chooses the experimental design to minimize the asymptotic mean squared error of the frequentist model averaging estimate. Necessary conditions for the optimal solution of a locally and Bayesian optimal design problem are established. The results are illustrated in several examples, and it is demonstrated that Bayesian optimal designs can yield a reduction of the mean squared error of the model averaging estimator by up to 45%. © 2019 Biometrika Trust.

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