Curve Fitting and Optimal Design for Prediction

Abstract

Summary The optimal design problem is tackled in the framework of a new model and new objectives. A regression model is proposed in which the regression function is permitted to take any form over the space X of independent variables. The design objective is based on fitting a simplified function for prediction. The approach is Bayesian throughout. The new designs are more robust than conventional ones. They also avoid the need to limit artificially design points to a predetermined subset of X. New solutions are also offered for the problems of smoothing, curve fitting and the selection of regressor variables.