Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Simultaneous Inference for Massive Data: Distributed Bootstrap   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.

4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg