Authors: Alhorn, Kira
Schorning, Kirsten
Dette, Holger
Title: Optimal designs for frequentist model averaging
Language (ISO): en
Abstract: We consider the problem of designing experiments for the estimation of a target in regression analysis if there is uncertainty about the parametric form of the regression function. A new optimality criterion is proposed, which minimizes the asymptotic mean squared error of the frequentist model averaging estimate by the choice of an experimental design. 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 up to 45%.
Subject Headings: model selection
Bayesian optimal designs
optimal design
model uncertainty
local misspecification
model averaging
URI: http://hdl.handle.net/2003/37014
http://dx.doi.org/10.17877/DE290R-19011
Issue Date: 2018
Appears in Collections:Sonderforschungsbereich (SFB) 823

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