An algorithm for the computation of optimum designs under a given covariance structure

Regression models with correlated errors lead to nonadditivity of the information matrix. This makes the usual approach of design optimization (approximation by a design measure, application of an equivalence theorem, numerical calculations by a gradient algorithm) impossible. Therefore extended information matrices depending upon design measures have been proposed recently and herein we present a first order iterative design optimization algorithm based upon them. A heuristic is formulated to circumvent the nonconvexity of the problem and the method is applied to typical examples from the literature.