Two-Stage Stopping Procedures Based on Standardized Time Series
提出新的两阶段停止程序，为随机过程稳态均值的仿真估计构建绝对宽度和相对宽度置信区间，基于标准化时间序列方法和Stein两阶段抽样，证明渐近有效性。
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.