Titlebook: Optimum Design 2000; Anthony Atkinson,Barbara Bogacka,Anatoly Zhigljavs Book 2001 Springer-Verlag US 2001 Likelihood.design.model.neural n

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Optimal Characteristic Designs for Polynomial Models(López-Fidalgo and Rodríguez-Díaz, 1998). With a slight modification of the classical algorithms, the gradient expression allows us to find some optimal characteristic designs for polynomial regression. We observe that these designs are a smooth transition from A- to D-optimal designs. Moreover, for

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A Note on Optimal Bounded Designscation locally optimal designs are obtained with bounded intensity for the logistic regression model. Moreover, it is shown that, for additive models, optimal marginally bounded designs can be generated from their optimal counterparts in the corresponding marginal models.

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Construction of Constrained Optimal Designslated but the Lagrange parameter is removed through a substitution, using linear equation theory, in an approach which transforms the constrained optimisation problem to a problem of maximising two functions of the design weights simultaneously. They have a common maximum of zero which is simultaneo

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Block Designs for Comparison of Two Test Treatments with a Controltment is better than (or differs from) the control are required. When an experiment is arranged in . blocks of size ., the optimal allocation of a fixed number of experimental units to the individual test treatments and the control within each block need to be determined. The optimality criteria of

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Sequential Construction of an Experimental Design from an I.I.D. Sequence of Experiments without Repse here a different suboptimal solution, based on a one-step-ahead optimal approach. A simple procedure, derived from an adaptive rule which is asymptotically optimal, Pronzato (1999a), when . = 1 (. → ∞, . fixed), is presented. The performances of these different strategies are compared on a simple example.

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