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Praise for the Second Edition
“This book should be an essential part of the personal library of every practicing statistician.”—Technometrics
Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given situation.
Written by leading statisticians, Nonparametric Statistical Methods, Third Edition provides readers with crucial nonparametric techniques in a variety of settings, emphasizing the assumptions underlying the methods. The book provides an extensive array of examples that clearly illustrate how to use nonparametric approaches for handling one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. In addition, the Third Edition features:
- The use of the freely available R software to aid in computation and simulation, including many new R programs written explicitly for this new edition
- New chapters that address density estimation, wavelets, smoothing, ranked set sampling, and Bayesian nonparametrics
- Problems that illustrate examples from agricultural science, astronomy, biology, criminology, education, engineering, environmental science, geology, home economics, medicine, oceanography, physics, psychology, sociology, and space science
- Sales Rank: #418462 in Books
- Brand: Hollander, Myles/ Wolfe, Douglas A./ Chicken, Eric
- Published on: 2013-12-04
- Original language: English
- Number of items: 1
- Dimensions: 10.30" h x 1.65" w x 7.32" l, 3.45 pounds
- Binding: Hardcover
- 848 pages
From the Back Cover
Praise for the Second Edition
"This book should be an essential part of the personal library of every practicing statistician."
—Technometrics
Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given situation.
Written by leading statisticians, Nonparametric Statistical Methods, Third Edition provides readers with crucial nonparametric techniques in a variety of settings, emphasizing the assumptions underlying the methods. The book provides an extensive array of examples that clearly illustrate how to use nonparametric approaches for handling one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. In addition, the Third Edition features:
- The use of the freely available R software to aid in computation and simulation, including many new R programs written explicitly for this new edition
- New chapters that address density estimation, wavelets, smoothing, ranked set sampling, and Bayesian nonparametrics
- Problems that illustrate examples from agricultural science, astronomy, biology, criminology, education, engineering, environmental science, geology, home economics, medicine, oceanography, physics, psychology, sociology, and space science
Nonparametric Statistical Methods, Third Edition is an excellent reference for applied statisticians and practitioners who seek a review of nonparametric methods and their relevant applications. The book is also an ideal textbook for upper-undergraduate and first-year graduate courses in applied nonparametric statistics.
About the Author
MYLES HOLLANDER is Robert O. Lawton Distinguished Professor of Statistics and Professor Emeritus at the Florida State University in Tallahassee. He served as editor of the Theory and Methods Section of the Journal of the American Statistical Association, 1993–96, and he received the Gottfried E. Noether Senior Scholar Award from the American Statistical Association in 2003.
DOUGLAS A. WOLFE is Professor and Chair Emeritus in the Department of Statistics at Ohio State University in Columbus. He is a two-time recipient of the Ohio State University Alumni Distinguished Teaching Award, in 1973–74 and 1988–89.
ERIC CHICKEN is Associate Professor at the Florida State University in Tallahassee. He is active in modern nonparametric statistics research fields, including functional analysis, sequential methods, and complex system applications.
Most helpful customer reviews
5 of 5 people found the following review helpful.
Very modern and thorough, but somtimes messy notation and typos
By MathsEngineer
Overall, I think this is a great book on modern nonaparametric statstical practice. The "comments" are especially helpful for getting more of the theoretical aspects of nonparametric theory and the R code (and associated packages) make this book very useful for the practitioner. My only issues are with some aspects of coverage and typos:
Coverage: I think that a separate chapter on permutation tests and the bootstrap should have been included (or computational nonparametric approaches in general). The boostrap is a very powerful nonparametric technique but only gets secondary play in this book...same thing for permutation tests. However, it retains the usual rank-based tests, which pre-date the bootstrap and other computational nonparametric approaches.
Typos and confusing notation: The book introduces the Dirichlet process for non-parametric Bayesian inference. Its a very cool idea and was new to me. However, the presentation is rather terse and the sudden onset of measure-theory notation, which is sometimes incorrectly used (most certaintly a typo, not a true misunderstanding on the authors' part, obviously), further muddies the discussion of this very interesting topic.
However, beyond the issues above, this is probably the most comprehensive and usable book on nonparametric statistics you can get.
1 of 1 people found the following review helpful.
Great overall, but could use some touching up.
By Vojnitek
As the other reviews have stated, this has a considerable amount of typos and errors. I found parts of it to be very confusing when trying to read it straight through (probably because of some of these errors).
However, I still find this to be a great reference for nonparametrics. I'm currently taking a class taught by one of the authors, and I will say that they know what they're talking about.
1 of 1 people found the following review helpful.
Pretty Good Book
By Joshua D Guenther
This is a pretty good book, has a few mistakes here and there (especially in it's R code) but overall very solid.
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