Resampling Methods: A Practical Guide to Data Analysis

This new book is a practical guide to data analysis using the bootstrap, cross-validation, and permutation tests. It is an essential resource for industrial statisticians, statistical consultants, and researcher professionals in science, engineering, and technology.

Only requiring minimal mathematics beyond algebra, it provides a table-free introduction to data analysis utilizing numerous exercises, practical data sets, and freely available statistical shareware.

Topics and Features:

Uses resampling approach to introduction statistics A practical presentation that covers all three resampling methods - bootstrap, density-estimation, and permutations Includes systematic guide to help one select correct procedure for a particular application Detailed coverage of all three statistical methodologies - classification, estimation, and hypothesis testing Suitable for classroom use and individual, self-study purposes Numerous practical examples in most popular computer programs such as SASTM, StataTM, and StatXactTM Useful appendices with computer programs and code to develop own methods With its accessible style and intuitive topic development, the book is an excellent basic resource and guide to the power, simplicity, and versatility of bootstrap, cross-validation and permutation tests. Students, professionals and researchers will find it a particularly useful guide to modern resampling methods and their applications.

"...the author has packaged an excellent and modern set of topics around the development and use of quantitative models.... If you need to learn about resampling, this book would be a good place to start."

―Technometrics (Review of the Second Edition)

This thoroughly revised and expanded third edition is a practical guide to data analysis using the bootstrap, cross-validation, and permutation tests. Only requiring minimal mathematics beyond algebra, the book provides a table-free introduction to data analysis utilizing numerous exercises, practical data sets, and freely available statistical shareware.

Topics and Features

* Practical presentation covers both the bootstrap and permutations along with the program code necessary to put them to work.

* Includes a systematic guide to selecting the correct procedure for a particular application.

* Detailed coverage of classification, estimation, experimental design, hypothesis testing, and modeling.

* Suitable for both classroom use and individual self-study.

New to the Third Edition

* Procedures are grouped by application; a prefatory chapter guides readers to the appropriate reading matter.

* Program listings and screen shots now accompany each resampling procedure: Whether one programs in C++, CART, Blossom, Box Sampler (an Excel add-in), EViews, MATLAB, R, Resampling Stats, SAS macros, S-PLUS, Stata, or StatXact, readers will find the program listings and screen shots needed to put each resampling procedure into practice.

* To simplify programming, code for readers to download and apply is posted at http://www.springeronline.com/0-8176-4386-9.

* Notation has been simplified and, where possible, eliminated.

* A glossary and answers to selected exercises are included.

With its accessible style and intuitive topic development, the book is an excellent basic resource for the power, simplicity, and versatility of resampling methods. It is an essential resource for statisticians, biostatisticians, statistical consultants, students, and research professionals in the biological, physical, and social sciences, engineering, and technology.