D-optimal or I-optimal design for minimal prediction variance at the limits of the factors of interest
In the JMP technical details it says that D-optimality focuses on precise estimates of the effects whereas I-optimal designs minimize the average variance of prediction over the design space. Let's assume that I have two factors X1 and X2, vary each of them for example by +/-20% and want to have a precise prediction of my response exactly at the limits of my factor variation, i.e., at -20% and +20...