Confidence Intervals for Steady-State Simulations: I. A Survey of Fixed Sample Size Procedures

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Abstract
We consider the problem of constructing a confidence interval for the steady-state mean of a stochastic process by means of simulation, and study the five main methods which have been proposed (replication, batch means, autoregressive representation, spectrum analysis, and regeneration cycles) for the case when the length of the simulation is fixed in advance. Comparing the performances of these methods on stochastic models with known steady-state means, we find that the simulator should exercise caution in interpreting the results from a simulation of fixed length, and that the length of a simulation adequate for acceptable performance is highly model-dependent. We also investigate possible sources of error for the methods, and conclude that variance estimator bias is more important than point estimator bias in confidence interval coverage degradation.

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