论文标题
同时推断大量数据：分布式引导程序
Simultaneous Inference for Massive Data: Distributed Bootstrap
论文作者
论文摘要
在本文中，我们提出了一种用于大量机器中分布的大量数据处理的自举方法。这种新方法在计算上是有效的，因为我们在主机上启动了无需过度绘制，通常由现有方法\ cite {kleiner2014Scalable，sengupta2016subspsmpled}所要求，而可证明具有最小通信的最佳统计效率。我们的方法不需要重复重新安装模型，而仅在工具机上收到的梯度上应用主机中的乘数引导程序。模拟验证我们的理论。
In this paper, we propose a bootstrap method applied to massive data processed distributedly in a large number of machines. This new method is computationally efficient in that we bootstrap on the master machine without over-resampling, typically required by existing methods \cite{kleiner2014scalable,sengupta2016subsampled}, while provably achieving optimal statistical efficiency with minimal communication. Our method does not require repeatedly re-fitting the model but only applies multiplier bootstrap in the master machine on the gradients received from the worker machines. Simulations validate our theory.
