论文标题
较高的计算较高维度的引导程序
Bootstrap in High Dimension with Low Computation
论文作者
论文摘要
引导程序是一种量化统计不确定性的流行数据驱动方法，但是对于现代的高维问题，由于需要反复生成重新示例和改装模型，因此可能会遭受巨大的计算成本。我们研究了在具有少量重新样本的高维环境中进行引导程序的使用。特别是，我们表明，通过最近的“廉价”自举透视图，即使尺寸随着样本量紧密增长，许多重新样本也可以实现有效的覆盖范围，从而强烈支持引导程序在大规模问题上的可实现性。我们通过一系列实验来验证理论结果，并将方法的性能与其他基准进行比较。
The bootstrap is a popular data-driven method to quantify statistical uncertainty, but for modern high-dimensional problems, it could suffer from huge computational costs due to the need to repeatedly generate resamples and refit models. We study the use of bootstraps in high-dimensional environments with a small number of resamples. In particular, we show that with a recent "cheap" bootstrap perspective, using a number of resamples as small as one could attain valid coverage even when the dimension grows closely with the sample size, thus strongly supporting the implementability of the bootstrap for large-scale problems. We validate our theoretical results and compare the performance of our approach with other benchmarks via a range of experiments.
