Michael R. Chernick & Robert A. LaBudde 
An Introduction to Bootstrap Methods with Applications to R [PDF ebook] 

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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