Extended Information Matrices for Optimal Designs when the Observations are Correlated

Andrej Pazman, Werner Müller

Publication: Working/Discussion PaperWU Working Paper

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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)

Publication series

SeriesForschungsberichte / Institut für Statistik
Number53

WU Working Paper Series

  • Forschungsberichte / Institut für Statistik

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