The bootstrap is a popular and useful tool for estimating the asymptotic variance of complicated estimators. Ironically, the fact that the estimators are complicated can make the standard bootstrap computationally burdensome because it requires repeated re-calculation of the estimator. In Honore and Hu (2015), we propose a computationally simpler bootstrap procedure based on repeated re-calculation of one-dimensional estimators. The applicability of that approach is quite general. In this paper, we propose an alternative method which is specific to extremum estimators based on U-statistics. The contribution here is that rather than repeated re-calculating the U-statistic-based estimator, we can recalculate a related estimator based on single-sums. A simulation study suggests that the approach leads to a good approximation to the standard bootstrap, and that if this is the goal, then our approach is superior to numerical derivative methods.

中文翻译：

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基于U统计量的估计量的渐近方差的更简单的自举估计

引导程序是一种用于估计复杂估计量的渐近方差的流行且有用的工具。具有讽刺意味的是，估算器很复杂的事实会使标准的引导程序在计算上变得繁重，因为它需要反复重新计算估算器。在Honore和Hu（2015）中，我们基于对一维估计量的重复重新计算，提出了一种计算上更简单的引导程序。这种方法的适用性很一般。在本文中，我们提出了另一种基于U统计量的极值估计量的替代方法。这里的贡献在于，我们可以重复计算基于单和的相关估计量，而不是重复重新计算基于U统计量的估计量。一项模拟研究表明，该方法可以很好地逼近标准自举程序，