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Robust Nonparametric Statistical Methods, Second Edition (Chapman & Hall/CRC Monographs on Statistics & Applied Probability), by Thomas P
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Presenting an extensive set of tools and methods for data analysis, Robust Nonparametric Statistical Methods, Second Edition covers univariate tests and estimates with extensions to linear models, multivariate models, times series models, experimental designs, and mixed models. It follows the approach of the first edition by developing rank-based methods from the unifying theme of geometry. This edition, however, includes more models and methods and significantly extends the possible analyses based on ranks.
New to the Second Edition
- A new section on rank procedures for nonlinear models
- A new chapter on models with dependent error structure, covering rank methods for mixed models, general estimating equations, and time series
- New material on the development of computationally efficient affine invariant/equivariant sign methods based on transform-retransform techniques in multivariate models
Taking a comprehensive, unified approach to statistical analysis, the book continues to describe one- and two-sample problems, the basic development of rank methods in the linear model, and fixed effects experimental designs. It also explores models with dependent error structure and multivariate models. The authors illustrate the implementation of the methods using many real-world examples and R. More information about the data sets and R packages can be found at www.crcpress.com
- Sales Rank: #2278802 in Books
- Brand: Brand: CRC Press
- Published on: 2010-12-20
- Original language: English
- Number of items: 1
- Dimensions: 10.00" h x 1.19" w x 7.01" l, 2.50 pounds
- Binding: Hardcover
- 554 pages
Features
- Used Book in Good Condition
Review
The coverage is expanded over the first edition to include recent developments in the field. … Hettmansperger and McKean examine a wealth of interesting problems in connection with applying nonparametric robust methods. … this is a well-written and nicely presented book that is likely to appeal to a reader with a good mathematical background and an interest in robust and nonparametric statistical methods. In my opinion, the book could provide the basis for a seminar in robust non-parametric methods for graduate students in statistics or mathematics.
―Eugenia Stoimenova, Journal of Applied Statistics, June 2012
… more logical and concise and more user-friendly … the book will be equally attractive to instructors, students, and researchers. In summary, this is a well written, structured, and presented book and offers readers plenty of examples and exercises. If I have the opportunity in the near future to offer a graduate course on robust nonparametric methods, I will definitely adopt this book with no hesitation.
―Technometrics, November 2011
This book gives an excellent treatment of modern rank-based methods with a special attention to their practical application to data. … a welcome highly up-to-date and very readable contribution to the field. It will certainly become a standard reference for nonparametric and robust methods. I recommend the book as an important textbook for research libraries. The book will soon find its place on the shelves and the tables of many kind of researchers and will serve as a graduate course textbook.
―Hannu Oja, International Statistical Review (2011), 79
… a fine capstone course in non-parametric statistics.
―MAA Reviews, June 2011
About the Author
Thomas P. Hettmansperger is a professor emeritus of statistics at Penn State University. Dr. Hettmansperger is a fellow of the American Statistical Association and Institute of Mathematical Statistics and an elected member of the International Statistical Institute. His research interests span nonparametric statistics, robust methods, and mixture models.
Joseph W. McKean is a professor of statistics at Western Michigan University. His research interests include robust nonparametric procedures for linear, nonlinear, and mixed models and times series designs. A fellow of the American Statistical Association, Dr. McKean has developed highly efficient and high breakdown procedures.
Most helpful customer reviews
29 of 29 people found the following review helpful.
a very nice advanced book on modern nonparametric methods and useful robust procedures
By Michael R. Chernick
Nonparametric statistics these days embraces robust and resampling techniques as well as the more tradional rank tests and permutation tests. Hettmansperger is one of the leading researchers in nonparametric methods and has published a fine text on ranking tests. This book in the spirit of Kendall and Stuart's advanced theory of statistics presents advanced modern results in a concise way.
The above review was for the first edition of the book a second edition was published in December 2010 by CRC Press while the first edition was much shorter and published by Arnold.
Since Amazon fails to make distinctions among editions I cannot wrote a separate review of the second edition which I just received. The new edition is 535 pages which is considerably longer than the first edition. The first edition seems to me to be more of a monograph while the second edition looks to be designed as a graduate text in nonparametric statistics. A nice feature of the book which I neglected to mention about the first edition is its presentation of robustness and L1 norm properties of many of the classical rank procedures. The second edition has added material on dependent error structure which includes mixed linear models, generalized estimating equations and time series.
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