Statistical inference using bootstrap confidence intervals

Michael Wood

doi:10.1111/j.1740-9713.2004.00067.x

Statistical inference using bootstrap confidence intervals

Michael Wood

Research output: Contribution to journal › Article › peer-review

Abstract

Bootstrap confidence intervals provide a way of quantifying the uncertainties in the inferences that can be drawn from a sample of data. The idea is to use a simulation, based on the actual data, to estimate the likely extent of sampling error. Michael Wood explains how simple bootstrapping works and explores some of its advantages.

Original language	English
Pages (from-to)	180-182
Number of pages	3
Journal	Significance
Volume	1
Issue number	4
DOIs	https://doi.org/10.1111/j.1740-9713.2004.00067.x
Publication status	Published - Dec 2004

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http://onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2004.00067.x/abstract

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abstract = "Bootstrap confidence intervals provide a way of quantifying the uncertainties in the inferences that can be drawn from a sample of data. The idea is to use a simulation, based on the actual data, to estimate the likely extent of sampling error. Michael Wood explains how simple bootstrapping works and explores some of its advantages.",

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Statistical inference using bootstrap confidence intervals

Abstract

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