Two-stage Stopping Procedures Based on Standardized Time Series

We propose some new two-stage stopping procedures to construct absolute-width and relative-width confidence intervals for a simulation estimator of the steady-state mean of a stochastic process. The procedures are based on the method of standardized time series proposed by Schruben and on Stein’s two-stage sampling scheme. We prove that our two-stage procedures give rise to asymptotically valid confidence intervals (as the prescribed length of the confidence interval approaches zero and the size of the first stage grows to infinity). The sole assumption required is that the stochastic process satisfy a functional central limit theorem. (SIMULATION; OUTPUT ANALYSIS; STOPPING RULES; DIFFUSION APPROXIMATIONS)