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Nonparametric Statistical Inference, Fifth Edition (Statistics: Textbooks and Monographs) 5th Edition

4.6 out of 5 stars 5 customer reviews
ISBN-13: 978-1420077612
ISBN-10: 1420077619
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Editorial Reviews

Review

"… a classic with a long history. Now in its fifth edition, many students have learned the basics of nonparametric statistics from one of the previous versions. The latest edition is updated with material from recent research and a new summarizing chapter with instructions for implementation of the imparted knowledge. Additional problems pose new challenges and better, readable figures improve the textbook’s ease of use. … a comprehensive compilation and should be useful as supplemental material for any course on nonparametrics …"
Statistical Papers (2014) 55

"Overall, this remains a very fine book suitable for a graduate-level course in nonparametric statistics. I recommend it for all people interested in learning the basic ideas of nonparametric statistical inference."
―Eugenia Stoimenova, Journal of Applied Statistics, June 2012

"… one of the best books available for a graduate (or advanced undergraduate) text for a theory course on nonparametric statistics. … a very well-written and organized book on nonparametric statistics, especially useful and recommended for teachers and graduate students."
Biometrics, 67, September 2011

"This excellently presented book achieves its aim of seeding the fundamentals of non-parametric inference. The theoretical concepts are illustrated with numerical examples and use of statistical software is illustrated, wherever possible. The book is undoubtedly well written and presents a good balance of theory and applications. It is suitable for teaching as well as self-learning. There are exercises in each chapter which will be helpful in teaching a course. … I would strongly recommend this book to university libraries, teachers and undergraduate students who want to learn non-parametric inference in theory and practice."
Journal of the Royal Statistical Society, Series A, April 2011

Praise for the Fourth Edition:
The facts that the first edition of this book was published in 1971 and that it is now in its fourth and revised edition are testimony to the book’s success over a long period. … The book is readable and clearly written and would be a valuable addition to every statistician’s library.
ISI Short Book Reviews

I learned nonparametric statistics … from the first author’s original version of the book. Having enjoyed that experience, I have unabashedly promoted this book ever since. The 4E is another very impressive updating of a classic text that should be part of every statistician’s library. … More than 100 pages have been added to the book. … the authors have generally rewritten and enhanced a lot of the material. Now, in its fourth edition, this book offers a very comprehensive and integrated presentation on nonparametric inference. … There is no competitor for this book and its comprehensive development and application of nonparametric methods. Users of one of the earlier editions should certainly consider upgrading to this new edition.
Technometrics, Vol. 46, No. 2, May 2004

The fourth edition includes new materials on quantiles, power and sample size, goodness-of-fit tests, multiple comparisons, and count data, as well as material on computing using SAS, Minitab, SPSS, and StatXact … The authors have … put a lot of effort to make the book more user-friendly by … adding tabular guides for tests and confidence intervals, more figures … and more exercises.
The American Statistician, May 2004

… Useful to students and research workers …This edition will be a good textbook for a beginning graduate-level course in nonparametric statistics.
Journal of the American Statistical Association

… a good mix of nonparametric theory and methodology focused on traditional rank-based methods … a good introduction to rank-based methods with a moderate amount of mathematical detail.
Journal of Quality Technology, Vol. 37, No. 2, April 2005

About the Author

Jean Dickinson Gibbons is Russell Professor Emerita of Statistics at the University of Alabama.

Subhabrata Chakraborti is a Robert C. and Rosa P. Morrow Faculty Excellence Fellow and professor of statistics at the University of Alabama.

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Product Details

  • Series: Statistics: Textbooks and Monographs
  • Hardcover: 650 pages
  • Publisher: Chapman and Hall/CRC; 5 edition (July 26, 2010)
  • Language: English
  • ISBN-10: 1420077619
  • ISBN-13: 978-1420077612
  • Product Dimensions: 6.3 x 1.5 x 9.4 inches
  • Shipping Weight: 2.4 pounds (View shipping rates and policies)
  • Average Customer Review: 4.6 out of 5 stars  See all reviews (5 customer reviews)
  • Amazon Best Sellers Rank: #195,175 in Books (See Top 100 in Books)

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Top Customer Reviews

By Justin Z. Smith on October 4, 2010
Format: Hardcover Verified Purchase
This book is a great "go to" resource on nonparametric statistics. In most MS programs in Statistics, nonparametric statistics are mentioned as an aside, and possibly rigorous proofs are not given. This book covers the proofs, and some more questions potential users of nonparametric methods have been wondering about, like:

-just what is meant by "nonparametric"?
-what is the efficiency lost by using ranks instead of the values of the data?
-is there a correlation between the ranks and the data?
-why is the probability integral transformation important?
-what are some practical applications of these methods?
-and more.

The updates to the 5th edition are significant.
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Format: Hardcover Verified Purchase
Is one of the best book in the subject I ever read (I read the 4 editions).....besides de "rationale of each test" (that almost all books in Nonparametric have) this book include all the intricate calculus that the subject need ...and they are made very carefully and with all detail....I love this book (....besides is much modern than Lehmann`s)
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Note: not for people who aren't looking for a mathematical nonparametric stats book focusing on proofs.

This book is one of a kind! I have searched all over for a book like this. I learned from Conover's Practical Nonparametric Statistics back in my undergraduate years, which has a primary focus on the applications of the tests and a VERY limited amount of theory. I was not satisfied with this approach and was left hanging with some - not much - motivation to some of these disgusting test statistic formulas. This book covers it all in one text (with very few exceptions, from what I noticed). Highly recommended!
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This book covers a lot of useful material and has pretty good prose. But my progress slowed to a crawl, and as a working data scientist hoping to spike his statistics knowledge, I didn't make it far before putting it down and deciding to focus my reading time on other reading.

Gibbons and Chakraborti focuses very heavily on deriving formulas for things. Unless you have exquisite speed in banging out derivations involving calculus and combinatorial formulas, you'll be simultaneously exhausted by sweating out the umpteenth n-fold integral and bored to tears by the consequently slow pace of exposition. One of the book's most annoying habits is to write down some formula for which the (almost impossible) "derivation is left as an exercise for the reader," then a bunch of pages later drag you through a more clever but pretty hard derivation of the same fact, then some pages yet later show you the effortless trick that makes the formula immediate and transparent. How sadistic! I bought this book to learn nonparametric statistical inference, not to receive mathematical hazing. I've had enough hazing.

In the age of fast computers, symbolic manipulation packages, and big data, it's most useful to have other things: knowledge of key theorems and when and why they're true (including the more instructive or constructive proofs); a broadened intuition for how new statistics or tests expose meaning in data; and an appreciation for what can be computed efficiently. That means a thin book with graphics and a light touch with the grunge. By veering so decisively into the weeds of derivations, this thick, dense book ultimately disappoints.
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Format: Hardcover Verified Purchase
Quite a wonderful book. Well written and covers the non parametric methods in detail. Suitable for both undergraduate and graduate level as well as researchers.
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