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Resampling Methods: A Practical Guide to Data Analysis, by Phillip I. Good
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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, it provides a table-free introduction to data analysis utilizing numerous exercises, practical data sets, and freely available statistical shareware.
New to the third edition are additional program listings and screen shots of C++, CART, Blossom, Box Sampler (an Excel add-in), EViews, MATLAB, R, Resampling Stats, SAS macros, S-Plus, Stata, or StatXact, which accompany each resampling procedure. A glossary and solutions to selected exercises have also been added. 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.
- Sales Rank: #1979383 in Books
- Published on: 2005-09-08
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x .63" w x 6.14" l, 1.01 pounds
- Binding: Hardcover
- 218 pages
Review
“This book provides an introduction to topics ranging from bootstrapping to regression trees, using computer code from a myriad of programs every step of the way. Topics covered include methods for one and two populations, power, experimental design, categorical data, multivariate methods, model building, and decision trees. The third edition restructures these categories into groupings by application rather than by statistical method, making the book far more user-friendly for the practicing statistician.
Good’s passion for making statistics not only palatable but enjoyable for the reader is evident in his enthusiastic tone, his excellent motivating examples, and his clear and nontechnical discussions of the methodologies. At the beginning of the text, a section titled ‘Which Chapter Should I Read?’ helps the reader determine where to find the information desired. Within the chapters, the reader is encouraged to explore data rather than simply plug it into formulas, and at the end of the chapter, a section titled ‘To Learn More’ provides an extensive list of references. Problems at the end of the chapter make the book useful as a textbook.” ―JASA (review of the third edition)
"[The book] has a 309-item bibliography, a glossary, and author and subject indices.… [It] provides much greater depth on the methods [than other books on the same subject]. Software support is broad." ―Pharmaceutical Statistics
"I enjoyed this book. The style of writing suggests that statistics is fun and exploratory (which it often is). The reader is helped and encouraged to understand the problem (how the data were obtained) and how they might analyze it using resampling methods." ―The American Statistician (review of the second edition)
"...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 book is what the title suggests, a practical guide to data analysis…helpful to the beginner…and a good resource…to an advanced user… The first few chapters provide a great pedagogical tool for creating in high school students an early interest in statistics. The book also proves to be a good resource for advanced topics with an extensive bibliography [and] provides in the Appendices C++, GAUSS, SAS, S-Plus and Stata codes for various procedures used in the book, along with resource material on resampling software." ―Technometrics (review of the first edition)
"Readers with a wide variety of backgrounds and interests should find this book illuminating and a valuable introduction." ―Short Book Reviews (Int’l Statistical Institute, review of the first edition)
"There is a list of 409 bibliographical references ranging from extremely theoretical to very applied… The appendices…contain advice for the ad hoc programmer, examples of code in various programming environments, as well as a guide to many resources for resampling software… More than 130 exercises are also included… This book has many very useful features for newcomers to applied statistics who will be able to learn a lot from it about the general principles of the business and about modern computer intensive methods." ―Mathematical Reviews (review of the first edition)
"Most introductory statistics books ignore or give little attention to resampling methods, and thus another generation learns the less than optimal methods of statistical analysis. The author attempts to remedy this situation by writing an introductory text that focuses on resampling methods, and he does it well." ―Ron. C. Fryxell, Albion College (review of the first edition)
"...The wealth of the bibliography covers a wide range of disciplines." ―Dr. Dimitris Karlis, Athens University of Economics (review of the first edition)
From the Back Cover
"…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.
Most helpful customer reviews
33 of 33 people found the following review helpful.
vastly improved over the first edition
By Michael R. Chernick
I wrote a very critical review of the first edition. As the author states in his review, this edition is much improved and has benefitted from past criticism. The treatment of permutation methods is particularly good as that is the author's specialty. The coverage of the bootstrap has been improved and there are now many useful references cited.
Chapter 10 is particularly noteworthy as the author has gone to great pains to point out the many important practical issues in model building and model validation. It does however miss some coverage of bootstrap developments in this area particularly the work of Gong. Also the author has a distain for order determination method like Mallows' Cp or information criteria such as those of Akaike, Swartz and Rissanen. I do not share this distain and think that such methods should not have been omitted from the discussion.
The first edition had a very poorly written chapter on classification and clustering. Some of the important ideas from that chapter were embedded into the new model building chapter. Bootstrap bias adjustment is not treated and seems to be a blatant omission given that a section on classification is presented in Chapter 10. But the author's aim is to touch on many interesting topics where resampling can help and he avoids the complications that would be required from an in-depth treatment. This strategy helps him keep the level of the text elementary. He does provide a large number of references for the reader who wants to learn more.
19 of 22 people found the following review helpful.
Entertaining but at times incomplete in coverage & accuracy
By A Customer
Dr. Good writes a very entertaining and elementary account of resampling methods much like his earlier book on permutation tests. However, unlike permutation tests, Dr. Good is not an expert on bootstrap or cross validation and although his treatment is good in some chapters his coverage is superficial in others and at times misses important aspects of the literature. This is particularly true of the chapter on classification problems. There he fails to make the distinction between clustering and classification. What he calls classification is really cluster analysis. Classification involves constructing algorithms based on training data whereas clustering deals with mixture distributions or other statistical models to define groups in data sets with no preassigned category labels. He also overlooks much of the important literature on linear, quadratic and regularized discriminant functions. He also leaves out the interesting topic of error rate estimation in classification problems where the bootstrap has enjoyed considerable success especially when compared to cross validation. If the reader is interested in a thorough account of resampling methods applied to classification problems he or she should consult the text by McLachlan. A good treatment of the application of bootstrap to error rate estimation can be found in the recent book "Bootstrap Methods A Practitioner's Guide" by Chernick.
13 of 14 people found the following review helpful.
review of a review for this book
By The Pyrrhonist
I really want to write a review of a review. One of the reviews suggest that "Bootstrap Methods A Practitioner's Guide" by Chernick is a better source for the material offered by this book. My response to that is you've got to be kidding. I've got Chernick's book and you'd need a psychic to learn anything from it. If you're trying to decide between Good's book and Chernicks please pick Good's.
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