Restricted minimax robust designs for misspecified regression models

The authors propose and explore new regression designs. Within a particular parametric class, these designs are minimax robust against bias caused by model misspecification while attaining reasonable levels of efficiency as well. The introduction of this restricted class of designs is motivated by a desire to avoid the mathematical and numerical intractability found in the unrestricted minimax theory. Robustness is provided against a family of model departures sufficiently broad that the minimax design measures are necessarily absolutely continuous. Examples of implementation involve approximate polynomial and second order multiple regression. Les auteurs proposent et explorent de nouveaux plans experimentaux pour la regression. Ces plans sont minimax par rapport a une classe parametrique restreinte et s'averent a la fois robustes au biais dǔ a un mauvais choix de modele et raisonnablement efficaces. L'introduction de cette classe restreinte de plans est motivee par le desir d'eviter les problemes mathematiques et numeriques lies a la theorie minimax generale. Les plans sont robustes a des familles de modeles suffisamment larges pour que les mesures des plans minimax soient absolument continues. Les exemples d'implantation concernent l'approximation polynomiale et la regression multiple du second ordre.