Optimality criteria for regression models based on predicted variance

In the context of nonlinear regression models, a new class of optimum desig n criteria is developed and illustrated. This new class, termed I-L-optimal ity, is analogous to Kiefer's Phi(k)-criterion but is based on predicted va riance, whereas Kiefer's class is based on the eigenvalues of the informati on matrix; I-L-optimal designs are invariant with respect to different para meterisations of the model and contain G- and D-optimality as special cases . We provide a general equivalence theorem which is used to obtain and veri fy I-L-optimal designs. The method is illustrated by various examples.