What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum.
What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum.
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DOI：
10.1080/00031305.2015.1089789
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发表时间：
2015-10-02
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影响因子：
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通讯作者：
Hesterberg TC
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文献类型：
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作者：
Hesterberg TC
自助法在统计教育和实践中具有巨大的潜力，但也存在一些微妙的问题和出错的方式。例如，非参数自举法和自举百分位数置信区间的常见组合在小样本情况下不如使用t区间准确，但在大样本情况下更准确。我在这篇文章中的目标是提供对引导方法的更深入的理解它们如何工作，何时工作或不工作，以及哪些方法工作得更好，并强调教学问题。本文的补充材料可在网上查阅。[2014年12月收到。2015年8月修订]
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]