This is the second edition of the comprehensive treatment of statistical inference using permutation techniques. It makes available to practitioners a variety of useful and powerful data analytic tools that rely on very few distributional assumptions. Although many of these procedures have appeared in journal articles, they are not readily available to practitioners. This new and updated edition places increased emphasis on the use of alternative permutation statistical tests based on metric Euclidean distance functions that have excellent robustness characteristics. These alternative permutation techniques provide many powerful multivariate tests including multivariate multiple regression analyses.
Publisher: Springer-Verlag New York Inc.
Number of pages: 446
Weight: 847 g
Dimensions: 235 x 155 x 25 mm
Edition: 2nd ed. 2007
From the Reviews:
"[T]his is a nicely written book that contains many important and useful topics, and I am certan that many pracitioners and researchers will find the new edition beneficial." (Technometrics, May 2008, Vol. 50 No. 2)"This is a very well-written text that extensively covers permutation-based tests in a general framework. It has been revised and extended by nearly 100 pages since the 2001 edition. ...This book is packed with real-data examples and dozens of simulation studies exploring the properties of permutation-based tests and contrasting them with their typical parametric `competitors.' The authors do not shy away from presenting the mathematical underpinnings of the methods, and do so in a very transparent and easy-to-follow manner so there is sufficient detail to implement the methods in your favorite software ... . That is not a concern if you are familiar with FORTRAN-77, as the authors have provided over 100 FORTRAN programs an associated datasets for download in Unix-compatible and Windows-compatible format. These well-commented programs are briefly described in Appendix A with subsections organized by chapter. ...Permutation Methods is a superb book that is highly recommended." ( Journal of the American Statistical Association, Dec. 2009, Vol. 104, No. 488)