What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum

被引：196

作者：

Hesterberg, Tim C. ^{[1
]}

机构：

[1] Google, Menlo Pk, CA 94025 USA

来源：

AMERICAN STATISTICIAN | 2015年 / 69卷 / 04期

关键词：

Bias; Confidence intervals; Sampling distribution; Standard error; Statistical concepts; Teaching;

D O I：

10.1080/00031305.2015.1089789

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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 methodshow they work, when they work or not, and which methods work betterand to highlight pedagogical issues. Supplementary materials for this article are available online.[Received December 2014. Revised August 2015]

引用

页码：371 / 386

页数：16