Optimal designs for frequentist model averaging.

ABSTRACT: 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 new optimality 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%.

SUBMITTER: Alhorn K 

PROVIDER: S-EPMC6690170 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature