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"…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.
Content Level » Graduate
Keywords » Applied Science - Data Analysis - Industrial Statistics - Nonparametric Statistics
Related subjects » Birkhäuser Applied Probability and Statistics - Birkhäuser Mathematics
Table of contents
Preface to the Third Edition
Preface to the Second Edition
Preface to the First Edition
Which Chapter Should I Read?
Software for Resampling
Estimating Population Parameters
Comparing Two Populations
Choosing the Best Procedure
Experimental Design and Analysis
Categorical Data
Multiple Variables and Multiple Hypotheses
Model Building
Decision Trees
Answers to Selected Exercises
Bibliography
Glossary
Author Index
Subject Index
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