Higher-order coverage errors of batching methods via Edgeworth expansions on t-statistics
研究了分批方法在模拟和统计中的高阶覆盖误差，通过Edgeworth展开分析t统计量，发现批数对覆盖误差的影响通常非单调，且当批数较大时，分段刀切法覆盖效果最佳。
While batching methods have been widely used in simulation and statistics, their higher-order coverage behaviors and relative advantages in this regard remain open. We develop techniques to obtain higher-order coverage errors for batching methods by building Edgeworth-type expansions on t-statistics. The coefficients in these expansions are intricate analytically, but we provide algorithms to estimate the coefficients of the n−1 error terms via Monte Carlo simulation. We provide insights on the effect of the number of batches on the coverage error, where we demonstrate generally nonmonotonic relations. We also compare different batching methods both theoretically and numerically, and argue that none of the methods is uniformly better than others in terms of coverage. However, when the number of batches is large, sectioned jackknife has the best coverage among all.