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Bootstrap
Author(s) -
Hesterberg Tim
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.182
Subject(s) - resampling , statistics , confidence interval , statistic , mathematics , statistical hypothesis testing , statistical inference , bootstrap aggregating , standard error , econometrics , computer science
This article provides an introduction to the bootstrap. The bootstrap provides statistical inferences—standard error and bias estimates, confidence intervals, and hypothesis tests—without assumptions such as Normal distributions or equal variances. As such, bootstrap methods can be remarkably more accurate than classical inferences based on Normal or t distributions. The bootstrap uses the same basic procedure regardless of the statistic being calculated, without requiring the use of application‐specific formulae. This article may provide two big surprises for many readers. The first is that the bootstrap shows that common t confidence intervals are woefully inaccurate when populations are skewed, with one‐sided coverage levels off by factors of two or more, even for very large samples. The second is that the number of bootstrap samples required is much larger than generally realized. WIREs Comp Stat 2011 3 497–526 DOI: 10.1002/wics.182 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bootstrap and Resampling