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An Introduction to the Bootstrap (Chapman & Hall/CRC Monographs on Statistics & Applied Probability), by Bradley Efron, R.J. Tibshirani
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Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
- Sales Rank: #402038 in Books
- Published on: 1994-05-15
- Released on: 1993-01-01
- Original language: English
- Number of items: 1
- Dimensions: 8.50" h x 1.03" w x 5.51" l, 1.58 pounds
- Binding: Hardcover
- 456 pages
Review
..."an excellent book, and worth a reading by most students and practitioners in statistics... Throughout the book, the authors have spent a lot of effort in introducing difficult ideas in a simple, easy-to-understand manner..." - Hong Kong Statistical Society Newsletter ..." written in a style that makes difficult statistical concepts easy to understand ...a wonderful text for the engineer who would like to apply and understand the many different bootstrap techniques that have appeared in the literature in the last fifteen years. It makes an excellent reference text that should grace the shelves of both statisticians and non-statisticians." - Journal of Quality Technology
From the Back Cover
This book provides the first general introduction to the bootstrap and related methods for assessing statistical accuracy.
About the Author
Bradley Efron is Max H. Stein Professor of Statistics and Biostatistics at the Stanford University School of Humanities and Sciences, and the Department of Health Research and Policy with the School of Medicine.
Tibshirani, University of Toronto, Canada.
Tibshirani, University of Toronto, Canada.
Most helpful customer reviews
40 of 43 people found the following review helpful.
Efron's bootstrap text
By Michael R. Chernick
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.
22 of 22 people found the following review helpful.
wonderful, interesting book
By A Customer
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.
9 of 9 people found the following review helpful.
A great book to learn the Bootstrap method from
By statW
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.
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