论文标题
重新采样的无自举推断的分位数
Resampling-free bootstrap inference for quantiles
论文作者
论文摘要
Bootstrap推断是获得对分位数和Quantiles估计量差异的强大推断的强大工具。自举推断的计算密集型性质使其在大规模实验中变得不可行。在本文中，使用Poisson Bootstrap算法和分位数估计量的理论特性用于推导替代无用的无泊松算法，用于Poisson Bootstrap推断，该算法基本上降低了计算复杂性，而无需其他假设。这些发现与基于订单统计数据的分位数分析置信区间有关现有文献。结果解锁了几乎任意大型样本的量量化差异的自举推断。在Spotify，我们现在可以轻松地计算出具有数亿个观察值的A/B测试中的分位数和Quantiles差异。
Bootstrap inference is a powerful tool for obtaining robust inference for quantiles and difference-in-quantiles estimators. The computationally intensive nature of bootstrap inference has made it infeasible in large-scale experiments. In this paper, the theoretical properties of the Poisson bootstrap algorithm and quantile estimators are used to derive alternative resampling-free algorithms for Poisson bootstrap inference that reduce the computational complexity substantially without additional assumptions. These findings are connected to existing literature on analytical confidence intervals for quantiles based on order statistics. The results unlock bootstrap inference for difference-in-quantiles for almost arbitrarily large samples. At Spotify, we can now easily calculate bootstrap confidence intervals for quantiles and difference-in-quantiles in A/B tests with hundreds of millions of observations.
