Mats Just a comment on your comments below:
"All models are wrong and I see no reason why the exponential error model would be different although I think it is better than the proportional error for most situations. " "Why would you not be able to get sensible information from models that don't have an additive error component?" I agree that for estimation purposes a purely proportional or exponential error model often seems to work well and under the principles of "all models are wrong" it may well be appropriately justified. This is probably because estimation processes that we use in standard software are fairly robust to trivial solutions. The theory of optimal design is less forgiving in this light and if you stated that your error was proportional to the observation then it would conclude that there would be no error when there is no observation (which we know is not true due to LOD issues). All designs are optimal when there is zero error since the information matrix would be infinite. Practically, the smallest observation will have least error and hence be in some sense close to optimal. So, a proportional or exponential only error model should be used with caution in anything other than estimation and not used for the purposes of optimal design. Steve --
