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Computing efficient exact designs of experiments using integer quadratic programming. (English) Zbl 06975454
Summary: A new method for computing exact experimental designs for linear regression models by integer quadratic programming is proposed. The key idea is to use the criterion of \(D Q\)-optimality, which is a quadratic approximation of the criterion of \(D\)-optimality in the neighbourhood of the approximate \(D\)-optimal information matrix. Several numerical examples are used to demonstrate that the \(D\)-efficiency of exact \(D Q\)-optimal designs is usually very high. An important advantage of this method is that it can be applied to situations with general linear constraints on permissible designs, including marginal and cost constraints.

MSC:
62 Statistics
Software:
SeDuMi
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