- Tapa dura: 554 páginas
- Editor: CRC Press; Edición: 2 (21 de enero de 2011)
- Colección: Chapman & Hall/CRC Monographs on Statistics & Applied Probability
- Idioma: Inglés
- ISBN-10: 1439809089
- ISBN-13: 978-1439809082
- Valoración media de los clientes: Sé el primero en opinar sobre este producto
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Robust Nonparametric Statistical Methods, Second Edition (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) (Inglés) Tapa dura – 21 ene 2011
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Descripción del producto
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
Reseña del editor
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.comVer Descripción del producto
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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.