Nonparametric Statistical Inference 5Ed (Hb 2011)
“… 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.
- Reviews (0)
“… 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.