Confronting the ironies of optimal design: Nonoptimal sampling designs with desirable properties

Two sampling designs are developed for the improvement of parameter estimate precision in nonlinear regression, one for when there is uncertainty in the parameter values, and the other for when the correct model formulation is unknown. Although based on concepts of optimal design theory, the design criteria emphasize efficiency rather than optimality. The development is illustrated using a Streeter-Phelps dissolved oxygen-biochemical oxygen demand model.

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