An Analytical Evaluation of Alternative Strategies in Steady-State Simulation

Abstract

We consider the simple technique of making independent and probabilistically identical simulation replications to estimate the steady-state mean of a stochastic process. For a fixed total sample size, using a first-order autoregressive model with high autocorrelation and time-dependent distributions, we analytically quantify and investigate the effects of the number of replications and of initial deletions of output on several different measures of point estimator and confidence interval quality. This analysis leads to general recommendations on the choice of replications and deletion. In particular, we conclude that deletion of some amount of the initial output in a replication can be an effective and efficient method of dealing with initialization bias; this conclusion differs from several previous studies on the efficacy of deletion.