Robust Nonparametric Statistical Methods: Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Autor Thomas P. Hettmansperger, Joseph W. McKeanen Limba Engleză Hardback – 20 dec 2010
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
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Specificații
ISBN-13: 9781439809082
ISBN-10: 1439809089
Pagini: 554
Ilustrații: 38 b/w images, 71 tables and 11 in text boxes
Dimensiuni: 178 x 254 x 36 mm
Greutate: 1.13 kg
Ediția:Revizuită
Editura: CRC Press
Colecția CRC Press
Seria Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Locul publicării:Boca Raton, United States
ISBN-10: 1439809089
Pagini: 554
Ilustrații: 38 b/w images, 71 tables and 11 in text boxes
Dimensiuni: 178 x 254 x 36 mm
Greutate: 1.13 kg
Ediția:Revizuită
Editura: CRC Press
Colecția CRC Press
Seria Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Locul publicării:Boca Raton, United States
Public țintă
Researchers and graduate students in statistics.Cuprins
One-Sample Problems. Two-Sample Problems. Linear Models. Experimental Designs: Fixed Effects. Models with Dependent Error Structure. Multivariate. Appendix. References. Index.
Recenzii
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
—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
Notă biografică
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.
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.
Descriere
Presenting an extensive set of tools and methods for data analysis, this second edition includes more models and methods and significantly extends the possible analyses based on ranks. It contains a new section on rank procedures for nonlinear models, a new chapter on models with dependent error structure, and new material on the development of computationally efficient affine invariant/equivariant sign methods based on transform-retransform techniques in multivariate models. The authors illustrate the methods using many real-world examples and R. Information about the data sets and R packages can be found at www.crcpress.com