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What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum
Bootstrapping has enormous potential in statistics education and practice, but there are subtle issues and ways to go wrong. For example, the common combination of nonparametric bootstrapping and bootstrap percentile confidence intervals is less accurate than using t-intervals for small samples, tho...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Taylor & Francis
2015
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4784504/ https://www.ncbi.nlm.nih.gov/pubmed/27019512 http://dx.doi.org/10.1080/00031305.2015.1089789 |
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author | Hesterberg, Tim C. |
author_facet | Hesterberg, Tim C. |
author_sort | Hesterberg, Tim C. |
collection | PubMed |
description | Bootstrapping has enormous potential in statistics education and practice, but there are subtle issues and ways to go wrong. For example, the common combination of nonparametric bootstrapping and bootstrap percentile confidence intervals is less accurate than using t-intervals for small samples, though more accurate for larger samples. My goals in this article are to provide a deeper understanding of bootstrap methods—how they work, when they work or not, and which methods work better—and to highlight pedagogical issues. Supplementary materials for this article are available online. [Received December 2014. Revised August 2015] |
format | Online Article Text |
id | pubmed-4784504 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-47845042016-03-23 What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum Hesterberg, Tim C. Am Stat Articles Bootstrapping has enormous potential in statistics education and practice, but there are subtle issues and ways to go wrong. For example, the common combination of nonparametric bootstrapping and bootstrap percentile confidence intervals is less accurate than using t-intervals for small samples, though more accurate for larger samples. My goals in this article are to provide a deeper understanding of bootstrap methods—how they work, when they work or not, and which methods work better—and to highlight pedagogical issues. Supplementary materials for this article are available online. [Received December 2014. Revised August 2015] Taylor & Francis 2015-10-02 2015-12-29 /pmc/articles/PMC4784504/ /pubmed/27019512 http://dx.doi.org/10.1080/00031305.2015.1089789 Text en © 2015 The Author(s). Published with license by American Fisheries Society This is an Open Access article. Non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly attributed, cited, and is not altered, transformed, or built upon in any way, is permitted. The moral rights of the named author(s) have been asserted. |
spellingShingle | Articles Hesterberg, Tim C. What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum |
title | What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum |
title_full | What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum |
title_fullStr | What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum |
title_full_unstemmed | What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum |
title_short | What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum |
title_sort | what teachers should know about the bootstrap: resampling in the undergraduate statistics curriculum |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4784504/ https://www.ncbi.nlm.nih.gov/pubmed/27019512 http://dx.doi.org/10.1080/00031305.2015.1089789 |
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