Using Common Random Numbers for Indifference-Zone Selection and Multiple Comparisons in Simulation
提出一种通用方法，在模拟中选择最优系统时同时进行无差异区间选择和多重比较推断，并利用公共随机数方差缩减技术减少所需样本量，给出两种易于应用的具体程序。
We present a general recipe for constructing experiment design and analysis procedures that simultaneously provide indifference-zone selection and multiple-comparison inference for choosing the best among k simulated systems. We then exhibit two such procedures that exploit the variance-reduction technique of common random numbers to reduce the sample size required to attain a fixed precision. One procedure is based on the Bonferroni inequality and is guaranteed to be statistically conservative. The other procedure is exact under a specific dependence structure, but may be slightly liberal otherwise. Both are easy to apply, requiring only simple calculations and tabled constants. We illustrate the procedures with a numerical example.