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.
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Editorial Reviews
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...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
- 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
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Most Helpful Customer Reviews
31 of 32 people found the following review helpful:
5.0 out of 5 stars
Efron's bootstrap text,
By
This review is from: An Introduction to the Bootstrap (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) (Hardcover)
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.
19 of 19 people found the following review helpful:
5.0 out of 5 stars
wonderful, interesting book,
By A Customer
This review is from: An Introduction to the Bootstrap (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) (Hardcover)
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.
7 of 7 people found the following review helpful:
5.0 out of 5 stars
A great book to learn the Bootstrap method from,
By statW "sfbasksg" (CA United States) - See all my reviews
This review is from: An Introduction to the Bootstrap (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) (Hardcover)
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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Inside This Book
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First Sentence:
Statistics is the science of learning from experience, especially experience that arrives a little bit at a time. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
jackknife data sets, infinitesimal jackknife estimate, maximum eigenvalue problem, bca limits, abc intervals, ideal bootstrap estimate, permutation replications, resampling form, simple bootstrap estimate, cell survival data, bootstrap bias estimate, bootstrap standard error estimate, least favorable family, residual squared error, replacement from the data, bootstrap hypothesis test, parametric bootstrap estimate, bootstrap histogram, jackknife values, implied likelihood, bootstrapping pairs, least favorable families, hormone data, bootstrap data, bootstrap replications Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Statistics is the science of learning from experience, especially experience that arrives a little bit at a time. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
jackknife data sets, infinitesimal jackknife estimate, maximum eigenvalue problem, bca limits, abc intervals, ideal bootstrap estimate, permutation replications, resampling form, simple bootstrap estimate, cell survival data, bootstrap bias estimate, bootstrap standard error estimate, least favorable family, residual squared error, replacement from the data, bootstrap hypothesis test, parametric bootstrap estimate, bootstrap histogram, jackknife values, implied likelihood, bootstrapping pairs, least favorable families, hormone data, bootstrap data, bootstrap replications Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 47
books:
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100
books
cite this book:
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- The Analysis of Contingency Tables, Second Edition (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) by Brian S. Everitt in Front Matter (1), and Front Matter (2)
- The Bootstrap and Edgeworth Expansion (Springer Series in Statistics) by Peter Hall on page 8, and Back Matter
- Monte Carlo Methods (CRC Monographs on Statistics & Applied Probability) by J. Hammersley in Front Matter, and Back Matter
- Residuals and Influence in Regression (Monographs on Statistics and Applied Probability, 18) by R. Dennis Cook in Front Matter
- Classification and Regression Trees by Leo Breiman on page 244
See all 47 books this book cites
- Measurement Error in Nonlinear Models by Raymond J. Carroll in Front Matter, and Back Matter
- Distribution-Free Statistical Methods, Second Edition (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) by J.S. Maritz in Front Matter, and Back Matter
- Development of Pedotransfer Functions in Soil Hydrology, Volume 30 (Developments in Soil Science) by Y. Pachepsky on page 31, and page 406
- Statistical Methods in Molecular Evolution (Statistics for Biology and Health) by Rasmus Nielsen on page 43, and page 253
- Quantitative Molecular Pharmacology and Informatics in Drug Discovery by Michael Lutz on page 245, and page 353
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