An Introduction to the Bootstrap

Rating:
Summary: Great introduction by the originator of the bootstrap
Review: Brad Efron wrote the key paper rediscovering the bootstrap and putting it in its proper place with other resampling techniques in his famous 1979 paper in the Annals of Statistics. His work was a breakthrough that has now led to hundreds of other publications and several books on the bootstrap and more general resampling procedures by himself, his students and many other statisticians. In fact I am working on a book with goals similar to what he and Rob Tibshirani achieve in this monograph. It is a concise and accurate presentation of the bootstrap and its wide variety of applications and is very much up to the state-of-the-art in this rapidly growing area of statistics. It is written in an intuitive fashion and avoids much of the mathematics (Edgeworth expansions etc.) which are needed to provide formal proof that the bootstrap does what it is intended to do. Provides most of the important references up through 1993. For a similar treatment that is more current, see Davison and Hinkley (1997). Bootstrap Methods and their Application. Those interested in the theory and formal mathematics should consult Hall (1992). The Bootstrap and Edgeworth Expansion.

Rating:
Summary: wonderful, interesting book
Review: that's a very simple and clear book about bootstrapping They consider bootstrapping as a way to estimate confidence intervals and other properties of a statistical distribution. Its mainadvantages are a) it requires no knolwedge of the actual distribution(e.g., it does not have to be normal) b) in principle, it can be used with any statistics method and any solution.

Rating:
Summary: Not for Engineers
Review: This book provides a good coverage of the very useful bootstrap method. However, post-graduation as an engineer, I find that the method is neither well known nor happily accepted by engineers outside of academia. In the corporate world, bootstrapping is left up to degreed statisticians, as this is what management trusts. As a mechanical engineer, I find that simpler statistical techniques, even if they include broad assumptions, are much more widely accepted. If you are an engineer, leave this up to the statisticians. If you are a statistician, this book is an acceptable source for learning bootstrap.

Rating:
Summary: Not for Engineers
Review: This book provides a good coverage of the very useful bootstrap method. However, post-graduation as an engineer, I find that the method is neither well known nor happily accepted by engineers outside of academia. In the corporate world, bootstrapping is left up to degreed statisticians, as this is what management trusts. As a mechanical engineer, I find that simpler statistical techniques, even if they include broad assumptions, are much more widely accepted. If you are an engineer, leave this up to the statisticians. If you are a statistician, this book is an acceptable source for learning bootstrap.

Rating:
Summary: A great book to learn the Bootstrap method from
Review: This is the best book to learn about the bootstrap. Clear style, no empty verbiage, good problems, excellent examples are some of the qualities that make this exposition of Bootstrap great. The math level is minimal - some basic statistics (perhaps at the level of Wackerly et al's book) - is all that's required.

Rating:
Summary: A great book to learn the Bootstrap method from
Review: This is the best book to learn about the bootstrap. Clear style, no empty verbiage, good problems, excellent examples are some of the qualities that make this exposition of Bootstrap great. The math level is minimal - some basic statistics (perhaps at the level of Wackerly et al's book) - is all that's required.