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2013 | OriginalPaper | Buchkapitel

10. Resampling

verfasst von : Jan Beran, Yuanhua Feng, Sucharita Ghosh, Rafal Kulik

Erschienen in: Long-Memory Processes

Verlag: Springer Berlin Heidelberg

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Abstract

Resampling or bootstrap methods refer to techniques where statistical inference is based on a simulated distribution of a statistic T _n obtained by resampling from an observed sample X ₁,…,X _n. Inference of this type is always conditional on the sample. In the most general version, no model assumptions are used except for global conditions such as stationarity, existence of some moments, etc. In the most restricted version, a parametric model is specified and resampling is used only as a simple way of obtaining an approximate distribution of T _n. Note that different terms such as ‘bootstrap’, ‘resampling’, ‘subsampling’, etc. are used in the literature for different variations of the same general idea. Since there does not seem to be a unified terminology, we use ‘resampling’ and ‘bootstrap’ as synonyms.

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Vorheriges Kapitel Spatial and Space-Time Processes

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Titel: Resampling
verfasst von: Jan Beran
Yuanhua Feng
Sucharita Ghosh
Rafal Kulik
Verlag: Springer Berlin Heidelberg
Buch: Long-Memory Processes
Print ISBN: 978-3-642-35511-0

Electronic ISBN: 978-3-642-35512-7

Copyright-Jahr: 2013
DOI: https://doi.org/10.1007/978-3-642-35512-7_10

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