Titlebook: MODA4 — Advances in Model-Oriented Data Analysis; Proceedings of the 4 Christos P. Kitsos,Werner G. Müller Conference proceedings 1995 Spri

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Bayesian Designs for Approximate Normalityimality criterion. The assumption that parameter estimates are approximately normally distributed is often used to find optimal designs, as well as simplify data analysis. How well this approximation holds for small to moderate sample sizes depends on the intrinsic and parameter-effects curvatures.

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Simulation Approach to One-Stage and Sequential Optimal Design Problemsnte Carlo sample of simulated experiments. Denote with ., . and . the design parameters, the parameter vector and the data, respectively. The Bayesian optimal design problem is to find the design .* which maximizes the pre-posterior expected utility .(.) = ..(., ., .)..(., .), where ..(., y) is the

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MV-optimization in Simple Linear Regressionan be easily determined. In this paper MV-optimum designs for simple linear regression are found. The equivalence theorem of and the directional derivative of the MV-criterion derived by Ford I., (3), have been used for this purpose. It turns out that for simple linear regression there exist an MV-o

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D-Optimal Designs for Generalized Linear Modelst cases the numerical complexity can be reduced to that of the two parameter case regardless of the original dimension. The form and properties of the obtained .-optimal designs are illustrated and discussed through a few interesting examples.

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Further Results on Optimal Designs for Generalized Tic Polynomials on the Simplex an appropriate factor space in mixture experiments, where the response also depends on the total amount of the components. These results are extended in two different aspects. Firstly the change of the optimal design under variation of the optimality criterion and secondly the exclusion of some of

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