Minimax Robust Designs for M-Estimated Models

Experimental designs that are minimax in the presence of model misspecifications have been constructed so as to minimize the maximum, over classes of alternate response models, of the integrated mean squared error of the predicted values.

The theory to date has focussed almost exclusively on Least Squares estimates.

Here we extend this theory to designs tailored for M-estimation of parameters, thus obtaining additional robustness against outlying responses.

We show that, subject to a minor change in a tuning constant, designs optimal for Least Squares remain so asymptotically for M-estimation.

We argue that even this minor change should be ignored, and the tuning constant chosen in an ad hoc but sensible manner which does not depend on which M-estimate is being employed.

A rather surprising additional result is that our designs and estimates, derived under an assumption of i.i.d. errors, are also robust, in a minimax sense, against broad classes of correlation structures.