DeepAI AI Chat
Log In Sign Up

Simultaneous Inference for Massive Data: Distributed Bootstrap

02/19/2020
by   Yang Yu, et al.
0

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 <cit.>, 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.

READ FULL TEXT
02/19/2021

Distributed Bootstrap for Simultaneous Inference Under High Dimensionality

We propose a distributed bootstrap method for simultaneous inference on ...
12/21/2011

A Scalable Bootstrap for Massive Data

The bootstrap provides a simple and powerful means of assessing the qual...
01/31/2022

A Cheap Bootstrap Method for Fast Inference

The bootstrap is a versatile inference method that has proven powerful i...
04/09/2015

Robust, scalable and fast bootstrap method for analyzing large scale data

In this paper we address the problem of performing statistical inference...
07/13/2023

Scalable Resampling in Massive Generalized Linear Models via Subsampled Residual Bootstrap

Residual bootstrap is a classical method for statistical inference in re...
02/15/2023

Optimal Subsampling Bootstrap for Massive Data

The bootstrap is a widely used procedure for statistical inference becau...
07/04/2021

A Comparison of the Delta Method and the Bootstrap in Deep Learning Classification

We validate the recently introduced deep learning classification adapted...