|Cornelia J. Swanepoel (South Africa)||firstname.lastname@example.org|
The bootstrap is a very general resampling procedure which can be applied to estimate the sampling distribution of a statistic. From the statistical practitioner's point of view it has attractive properties because it requires few assumptions, little modelling or analysis, and can be applied in an automatic way in a wide variety of situations regardless of their theoretical complexity. The bootstrap can provide answers to questions which are too complicated for traditional statistical analyses, which are usually based on central limit theory and asymptotic normal approximations.
The bootstrap method is a computer-intensive method and due to the development and availability of modern computing power, the methodology became an even more powerful, practical tool to the practitioner.
In this talk, a brief discussion of the non-parametric bootstrap is presented, followed by examples and applications in various fields. Furthermore, possible suggestions regarding the teaching of these concepts at various levels are made.
The key requirements for computer implementation of the bootstrap method include a flexible programming language with a collection of reliable quasi-random number generators, a wide range of built-in statistical procedures to bootstrap and a reasonably fast processor. The use of the statistical languages S and Fortran, using the current commercial versions Splus 4.5 and Digital Fortran 6.0, are illustrated, although other statistical computing environments could also be applied.
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