Multivariate Batch Means and Control Variates
研究将非重叠批次均值输出分析法与控制变量方差缩减技术结合，用于估计稳态多元均值向量，并分析批次数量和控制变量数量对估计量的影响，为仿真实验提供批次选择指南。
We consider applying the nonoverlapping batch means output analysis method in conjunction with the control-variate variance-reduction technique to estimate a steady-state multivariate mean vector. The effects of the number of batches and the number of control variates on the multivariate point and region estimators and the univariate point and interval estimators are considered. The results are experiment analysis guidelines in terms of an appropriate range of the number of batches to choose as a function of the number of responses and control variates. The results have implications for terminating simulations as well.