  A meta-model of the input-output data of a computationally expensive
simulation is often employed for prediction, optimization, or sensitivity
analysis purposes. Fitting is enabled by a designed experiment, and for
computationally expensive simulations, the design efficiency is of importance.
Heteroscedasticity in simulation output is common, and it is potentially
beneficial to induce dependence through the reuse of pseudo-random number
streams to reduce the variance of the meta-model parameter estimators. In this
paper, we develop a computational approach to robust design for computer
experiments without the need to assume independence or identical distribution
of errors. Through explicit inclusion of the variance or correlation structures
into the meta-model distribution, either maximum likelihood estimation or
generalized estimating equations can be employed to obtain an appropriate
Fisher information matrix. Robust designs can then be computationally sought
which maximize some relevant summary measure of this matrix, averaged across a
prior distribution of any unknown parameters.
