An introduction to the bootstrap with application in R

Abstract

Bootstrap methods are resampling techniques for assessing uncertainity. They are useful when inference is to be based on a complex procedure for which theoretical results are unavailable or not useful for the sample sizes met in practice, where a standard model is suspect but it is unclear with what to replace it, or where a 'quick and dirty' answer is required. They can also be used to verify the usefulness of standard approximations for parametric methods, and to improve them if they seem to give inadequate references. This article, a brief introduction on their use, is based soley on parts of Davison and Hinkley (1997), where further details and many examples and practicals can be found. A different point of view is given by Efron and Tibshirani (1993) and a more mathematical survey by Shao and Tu (1995), while Hall (1992) describes the underlying theory.