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Robust sequential designs for nonlinear regression. (English) Zbl 1018.62057
Summary: The authors introduce the formal notion of an approximately specified nonlinear regression model and investigate sequential design methodologies when the fitted model is possibly of an incorrect parametric form. They present small-sample simulation studies which indicate that their new designs can be very successful, relative to some common competitors, in reducing mean squared error due to model misspecification and to heteroscedastic variation. Their simulations also suggest that standard normal-theory inference procedures remain approximately valid under the sequential sampling schemes. The methods are illustrated both by simulation and in an example using data from an experiment described in the chemical engineering literature.

MSC:
62L05 Sequential statistical design
62K05 Optimal statistical designs
62F35 Robustness and adaptive procedures (parametric inference)
62J02 General nonlinear regression
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