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Symmetrizing k-nn and Mutual k-nn Smoothers Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Subsampling for Big Data: Some Recent Advances

Authors : P. Bertail, O. Jelassi, J. Tressou, M. Zetlaoui

Published in: Nonparametric Statistics

Publisher: Springer International Publishing

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Abstract

The goal of this contribution is to develop subsampling methods in the framework of big data and to show their feasibility in a simulation study. We argue that using different subsampling distributions with different subsampling sizes brings a lot of information on the behavior of statistical procedures: subsampling allows to estimate the rate of convergence of different procedures and to construct confidence intervals for general parameters including the generalization error of an algorithm in machine learning.

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previous chapter Extremal Index for a Class of Heavy-Tailed Stochastic Processes in Risk Theory
next chapter Probability Bounds for Active Learning in the Regression Problem
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Metadata
Title
Subsampling for Big Data: Some Recent Advances
Authors
P. Bertail
O. Jelassi
J. Tressou
M. Zetlaoui
Copyright Year
2018
Book
Nonparametric Statistics

Print ISBN: 978-3-319-96940-4

Electronic ISBN: 978-3-319-96941-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-96941-1_13

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