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Erschienen in:
How Computational Statistics Became the Backbone of Modern Data Science Kapitel lesen Erstes Kapitel lesen

2012 | OriginalPaper | Buchkapitel

17. Bootstrap and Resampling

verfasst von : Enno Mammen, Swagata Nandi

Erschienen in: Handbook of Computational Statistics

Verlag: Springer Berlin Heidelberg

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Abstract

Thebootstrap is by now a standard method in modern statistics. Its roots go back to a lot ofresampling ideas that were around in the seventies. The seminal work of Efron synthesized some of the earlierresampling ideas and established a new framework for simulation based statistical analysis. The idea of thebootstrap is to develop a setup to generate more (pseudo) data using the information of the original data. True underlying sample properties are reproduced as closely as possible and unknown model characteristics are replaced by sample estimates.

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Vorheriges Kapitel Model Selection
Nächstes Kapitel Design and Analysis of Monte Carlo Experiments
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Metadaten
Titel
Bootstrap and Resampling
verfasst von
Enno Mammen
Swagata Nandi
Copyright-Jahr
2012
Verlag
Springer Berlin Heidelberg
Buch
Handbook of Computational Statistics

Print ISBN: 978-3-642-21550-6

Electronic ISBN: 978-3-642-21551-3

Copyright-Jahr: 2012

DOI
https://doi.org/10.1007/978-3-642-21551-3_17