Permutation Methods - (Springer Statistics) 2nd Edition by Paul W Mielke & Kenneth J Berry (Hardcover)
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<p/><br></br><p><b> About the Book </b></p></br></br><p>This is the second edition of the comprehensive treatment of statistical inference using permutation techniques. This updated version places increased emphasis on the use of alternative permutation statistical tests based on metric Euclidean distance functions.</p><p/><br></br><p><b> Book Synopsis </b></p></br></br><p>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.</p><p/><br></br><p><b> From the Back Cover </b></p></br></br><p>Most commonly-used parametric and permutation statistical tests, such as the matched-pairs <em>t</em> test and analysis of variance, are based on non-metric squared distance functions that have very poor robustness characteristics. This second 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. In addition to permutation techniques described in the first edition, this second edition also contains various new permutation statistical methods and studies that include resampling multiple contingency table analyses, analysis concerns involving log-linear models with small samples, an exact discrete analog of Fisher's continuous method for combining <em>P</em>-values that arise from small data sets, multiple dichotomous response analyses, problems regarding Fisher's Z transformation for correlation analyses, and multivariate similarity comparisons between corresponding multiple categories of two samples. Paul W. Mielke, Jr. is Professor of Statistics at Colorado State University, and a fellow of the American Statistical Association. Kenneth J. Berry is Professor of Sociology at Colorado State University.</p><p/><br></br><p><b> Review Quotes </b></p></br></br><br><p>From the Reviews: </p> <p>"[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)</p> "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)<br>
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