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Poster
Simultaneous Inference for Massive Data: Distributed Bootstrap
Yang Yu · Shih-Kang Chao · Guang Cheng

Thu Jul 16 07:00 AM -- 07:45 AM & Thu Jul 16 06:00 PM -- 06:45 PM (PDT) @ Virtual #None

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.

Author Information

Yang Yu (Purdue University)
Shih-Kang Chao (University of Missouri)
Guang Cheng (Purdue University)

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