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An Introduction to Bootstrap Methods with Applications to R [Hardcover]

Michael R. Chernick , Robert A. LaBudde
2.0 out of 5 stars  See all reviews (2 customer reviews)

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Book Description

November 1, 2011 0470467045 978-0470467046 1
A comprehensive introduction to bootstrap methods in the R programming environment

Bootstrap methods provide a powerful approach to statistical data analysis, as they have more general applications than standard parametric methods. An Introduction to Bootstrap Methods with Applications to R explores the practicality of this approach and successfully utilizes R to illustrate applications for the bootstrap and other resampling methods. This book provides a modern introduction to bootstrap methods for readers who do not have an extensive background in advanced mathematics. Emphasis throughout is on the use of bootstrap methods as an exploratory tool, including its value in variable selection and other modeling environments.

The authors begin with a description of bootstrap methods and its relationship to other resampling methods, along with an overview of the wide variety of applications of the approach. Subsequent chapters offer coverage of improved confidence set estimation, estimation of error rates in discriminant analysis, and applications to a wide variety of hypothesis testing and estimation problems, including pharmaceutical, genomics, and economics. To inform readers on the limitations of the method, the book also exhibits counterexamples to the consistency of bootstrap methods.

An introduction to R programming provides the needed preparation to work with the numerous exercises and applications presented throughout the book. A related website houses the book's R subroutines, and an extensive listing of references provides resources for further study.

Discussing the topic at a remarkably practical and accessible level, An Introduction to Bootstrap Methods with Applications to R is an excellent book for introductory courses on bootstrap and resampling methods at the upper-undergraduate and graduate levels. It also serves as an insightful reference for practitioners working with data in engineering, medicine, and the social sciences who would like to acquire a basic understanding of bootstrap methods.


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Editorial Reviews

Review

“I recommend this text to anyone wishing to apply computationally intensive methods and if you only purchase one book on bootstrap methods then this could be the book for you!.”  (International Statistical Review, 2012)

 

From the Back Cover

A comprehensive introduction to bootstrap methods in the R programming environment

Bootstrap methods provide a powerful approach to statistical data analysis, as they have more general applications than standard parametric methods. An Introduction to Bootstrap Methods with Applications to R explores the practicality of this approach and successfully utilizes R to illustrate applications for the bootstrap and other resampling methods. This book provides a modern introduction to bootstrap methods for readers who do not have an extensive background in advanced mathematics. Emphasis throughout is on the use of bootstrap methods as an exploratory tool, including its value in variable selection and other modeling environments.

The authors begin with a description of bootstrap methods and its relationship to other resampling methods, along with an overview of the wide variety of applications of the approach. Subsequent chapters offer coverage of improved confidence set estimation, estimation of error rates in discriminant analysis, and applications to a wide variety of hypothesis testing and estimation problems, including pharmaceutical, genomics, and economics. To inform readers on the limitations of the method, the book also exhibits counterexamples to the consistency of bootstrap methods.

An introduction to R programming provides the needed preparation to work with the numerous exercises and applications presented throughout the book. A related website houses the book's R subroutines, and an extensive listing of references provides resources for further study.

Discussing the topic at a remarkably practical and accessible level, An Introduction to Bootstrap Methods with Applications to R is an excellent book for introductory courses on bootstrap and resampling methods at the upper-undergraduate and graduate levels. It also serves as an insightful reference for practitioners working with data in engineering, medicine, and the social sciences who would like to acquire a basic understanding of bootstrap methods.


Product Details

  • Hardcover: 240 pages
  • Publisher: Wiley; 1 edition (November 1, 2011)
  • Language: English
  • ISBN-10: 0470467045
  • ISBN-13: 978-0470467046
  • Product Dimensions: 9.3 x 6.2 x 0.7 inches
  • Shipping Weight: 1 pounds (View shipping rates and policies)
  • Average Customer Review: 2.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Best Sellers Rank: #354,309 in Books (See Top 100 in Books)

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18 of 19 people found the following review helpful
2.0 out of 5 stars Poor May 6, 2012
Format:Hardcover
My goodwill died on page 23, the last of the fourteen (!) pages - $6-worth of your money, by page count - filled with a dump of R output for "??bootstrap". Asking for a thoughtful discussion of R's related functionality would have been a tall order - the authors didn't even bother to adjust line width, so that lines break mid-page-width, and the print-out takes up twice as much space.

The following Section 2.1.2 is representative. It is ten pages long, and I would say that two or three are really necessary. The most substantial one, page 36, mentions four bootstrap estimators: in order of appearance, Efron's 632, Chatterjee and Chatterjee's e0, Efron's bootstrap (?), and Efron and Tibshirani's 632+. Two are described - on closer inspection, only e0's implementation is clear - one with a couple of lines, the other with a few lines; no math, no discussion, no nuthin'. Readers eager to find out more about an estimator proposed by Efron are referred to ... Chernick's book. Indeed, if one counts references on page 36, it's five to Chernick and two to Efron.

(A special "for shame" for Section 7.8, discussing missing data - on inspection, about a page of text, half of it focusing on .... why the last-observation-carried-forward heuristic is bad, and three lines (!) about bootstrap).

Let's leave the delusions of possible classroom use at the door - this is a literature survey, not a textbook - and then recognize that such a survey, too, has value. I can see this book re-edited and de-padded into a pamphlet like those published by SAGE. (SAGE does have a pamphlet on bootstrap, but one that is too skimpy). At $85, it's an easy "pass".
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11 of 11 people found the following review helpful
By Justin
Format:Hardcover
This book is very bad. I can't imagine it is useful to any audience as it does not provide the detail an expert would like, and it glosses over things a novice would need to know. It's filled with other problems: the examples in the book are poor, the text refers to examples that are not provided, and almost no topic is explained to any useful degree. I do genuinely try to explain the good sides of things I review, even if I dislike the thing overall, but I have nothing positive to say about this book.

Instead of providing information themselves, throughout the book the authors suggest that the reader refer to other sources for further information. Take their own advice and just look for other books about using bootstrap methods.
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