Optimal designs for frequentist model averaging

• 成果类型: Article
• 署名作者: Alhorn, K.; Schorning, K.; Dette, H.
• 署名单位: Dortmund University of Technology; Ruhr University Bochum
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asz036
• 发表日期: 2019
• 页码: 665682
• 关键词: selection; DISCRIMINATION

摘要

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%.