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Optimal design of experiments for implicit models

Duarte, Belmiro P.M., Atkinson, Anthony C., Granjo, Jose F.O and Oliveira, Nuno M.C (2020) Optimal design of experiments for implicit models. Journal of the American Statistical Association. ISSN 0162-1459 (In Press)

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Explicit models representing the response variables as functions of the control variables are standard in virtually all scientific fields. For these models there is a vast literature on the optimal design of experiments to provide good estimates of the parameters with the use of minimal resources. Contrarily, the optimal design of experiments for implicit models is more complex and has not been systematically addressed. Nevertheless, there are practical examples where the models relating the response variables, the parameters and the factors are implicit or hardly convertible into an explicit form. We propose a general formulation for developing the theory of the optimal design of experiments (ODoE) for implicit algebraic models to specifically find continuous local designs. The treatment relies on converting the ODoE problem into an optimization problem of the Nonlinear Programming class which includes the construction of the parameter sensitivities and the Cholesky decomposition of the Fisher Information Matrix. The Nonlinear Programming problem generated has multiple local optima, and we use global solvers, combined with an equivalence theorem from the theory of ODoE, to ensure the global optimality of our continuous optimal designs. We consider D– and A–optimality criteria and apply the approach to five examples of practical interest in chemistry and thermodynamics.

Item Type: Article
Official URL:
Additional Information: © 2020 American Statistical Association
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 04 Dec 2020 17:18
Last Modified: 10 Mar 2021 00:25

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