Bootstrap Confidence Intervals for Simulation Output Parameters

Bootstrapping has been used to characterize the impact on discrete-event simulation output arising from input model uncertainty for thirty years. The distribution of simulation output statistics can be very non-normal, especially in simulation of heavily loaded queueing systems, and systems operating at a near optimal value of the output measure. This paper presents issues facing simulationists in using bootstrapping to provide confidence intervals for parameters related to the distribution of simulation output statistics, and identifies appropriate alternatives to the basic and percentile bootstrap methods. Both input uncertainty and ordinary output analysis settings are included.