Book by Gibbons Jean Dickinson Chakraborti Subhabrata
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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
Proven Material for a Course on the Introduction to the Theory and/or on the Applications of Classical Nonparametric Methods
Since its first publication in 1971, Nonparametric Statistical Inference has been widely regarded as the source for learning about nonparametric statistics. The fifth edition carries on this tradition while thoroughly revising at least 50 percent of the material.
New to the Fifth Edition
This classic, best-selling statistics book continues to cover the most commonly used nonparametric procedures. The authors carefully state the assumptions, develop the theory behind the procedures, and illustrate the techniques using realistic research examples from the social, behavioral, and life sciences. For most procedures, they present the tests of hypotheses, confidence interval estimation, sample size determination, power, and comparisons of other relevant procedures. The text also gives examples of computer applications based on Minitab, SAS, and StatXact and compares these examples with corresponding hand calculations. The appendix includes a collection of tables required for solving the data-oriented problems.
Nonparametric Statistical Inference, Fifth Edition provides in-depth yet accessible coverage of the theory and methods of nonparametric statistical inference procedures. It takes a practical approach that draws on scores of examples and problems and minimizes the theorem-proof format.Jean Dickinson Gibbons was recently interviewed regarding her generous pledge to Virginia Tech.
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