Optimal designs for homoscedastic functional polynomial measurement error models

Min-Jue Zhang and Rong-Xian Yue ()
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Min-Jue Zhang: Shanghai Normal University
Rong-Xian Yue: Shanghai Normal University

AStA Advances in Statistical Analysis, 2021, vol. 105, issue 3, No 6, 485-501

Abstract: Abstract This paper considers the construction of optimal designs for homoscedastic functional polynomial measurement error models. The general equivalence theorems are given to check the optimality of a given design, based on the locally and Bayesian D-optimality criteria. The explicit characterizations of the locally and Bayesian D-optimal designs are provided. The results are illustrated by numerical analysis for a quadratic polynomial measurement error model. Numerical results show that the error-variances ratio and the model parameter are the important factors for the both optimal designs. Moreover, it is shown that the Bayesian D-optimal design is more robust and effective compared with the locally D-optimal design, if the error-variances ratio or the model parameter is misspecified.

Keywords: Measurement error; Optimal design; D-optimality; Bayesian optimality; Equivalence theorem (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10182-021-00399-4

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