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Designing for Parameter Subsets in Gaussian Nonlinear Regression Models

This article presents and illustrates several important subset design approaches for Gaussian nonlinear regression models and for linear models where interest lies in a nonlinear function of the model parameters. These design strategies are particularly useful in situations where currently- used subset design procedures fail to provide designs which can be used to fit the model function. Our original design technique is illustrated in conjuction with D-optimality, Bayesian D-optimality and Kiefer'sk-optimality, and is extended to yield subset designs which take account of curvature.