UNIFORM ERROR BOUNDS FOR STOCHASTIC KRIGING

In this paper, we propose an approach to construct uniform error bounds (or confidence intervals) for stochastic kriging with a prescribed confidence level. The theoretical development sheds some light on the impact of simulation experimental designs and budget allocation schemes as well as their relative importance on the large-sample properties of stochastic kriging. Through numerical evaluations, we demonstrate the superiority of the uniform error bounds to the simultaneous confidence intervals obtained by applying Bonferroni correction under various experimental settings.