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arXiv · 2002.08443

Simultaneous Inference for Massive Data: Distributed Bootstrap

Yang Yu·Shih-Kang Chao·Guang Cheng
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Abstract

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.

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Yang Yu, Shih-Kang Chao, Guang Cheng. 2020-02-19. Simultaneous Inference for Massive Data: Distributed Bootstrap. https://arxiv.org/abs/2002.08443

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