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• W3008516097 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." @default.
• W3008516097 created "2020-03-06" @default.
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• W3008516097 date "2020-02-19" @default.
• W3008516097 modified "2023-09-27" @default.
• W3008516097 title "Simultaneous Inference for Massive Data: Distributed Bootstrap" @default.
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