Accessible to medicine- and/or public policy-related audiences, as well as most statisticians.

• Emphasis on outliers is discussed by way of detection and treatment.
• Resampling statistics software is incorporated throughout.
• Motivating applications are presented in light of honest theory.
• Plentiful exercises are sprinkled throughout.

This book provides an introduction to the bootstrap, offering reliable, authoritative coverage of the bootstrap's considerable advantages - as well as its drawbacks. This Second Edition takes great care to draw connections between the more traditional resampling methods and the bootstrap and places even more emphasis on the use of the bootstrap as an exploratory tool.

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....