通过离散卷积方法的自举
Bootstrapping Through Discrete Convolutional Methods
摘要：
Bootstrapping被设计为使用蒙特卡罗技术从固定样本中随机重新采样数据。然而，原始样本本身定义了一个离散分布。卷积方法非常适合于离散分布，我们展示了利用这些技术的优势，自举。离散卷积方法可以为自举量提供精确的数值解，或者至少提供数学误差界。相比之下，蒙特卡罗bootstrap方法只能提供收敛缓慢的置信区间。另外，对于某些问题，卷积方法的计算时间可以显著地少于蒙特卡罗方法。这篇文章提供了几个使用卷积技术进行自举的例子，并将结果与蒙特卡罗自举和竞争鞍点方法的结果进行了比较。
英文摘要：
Bootstrapping was designed to randomly resample data from a fixed sample using Monte Carlo techniques. However, the original sample itself defines a discrete distribution. Convolutional methods are well suited for discrete distributions, and we show the advantages of utilizing these techniques for bootstrapping. The discrete convolutional approach can provide exact numerical solutions for bootstrap quantities, or at least mathematical error bounds. In contrast, Monte Carlo bootstrap methods can only provide confidence intervals which converge slowly. Additionally, for some problems the computation time of the convolutional approach can be dramatically less than that of Monte Carlo resampling. This article provides several examples of bootstrapping using the proposed convolutional technique and compares the results to those of the Monte Carlo bootstrap, and to those of the competing saddlepoint method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
