Resampling Methods: A Practical Guide to Data Analysis

by Phillip I. Good

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 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.

269 pages, Hardcover

First published April 1, 1999