Extended Information Matrices for Optimal Designs when the Observations are Correlated

2024/01/18 by Pazman, Andrej, Müller, Werner

paper · doi:10.57938/47c4d841-bc7d-428d-9d5c-0a3c7a9ad581

Abstract

Regression models with correlated errors lead to nonadditivity of the information matrix. This makes the usual approach of design optimization (approximation with a continuous design, application of an equivalence theorem, numerical calculations by a gradient algorithm) impossible. A method is presented that allows the construction of a gradient algorithm by altering the information matrices through adding of supplementary noise. A heuristic is formulated to circumvent the nonconvexity problem and the method is applied to typical examples from the literature. (author's abstract)

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