A Chronicle of Permutation Statistical Methods: 1920 2000, and Beyond - Rilegato

Berry, Kenneth J.; Johnston, Janis E.; Mielke, Paul W., Jr.

Rilegato

ISBN 10: 3319027433 ISBN 13: 9783319027432

Casa editrice: Springer Nature, 2014

Riassunto

The focus of this book is on the birth and historical development of permutation statistical methods from the early 1920s to the near present. Beginning with the seminal contributions of R.A. Fisher, E.J.G. Pitman, and others in the 1920s and 1930s, permutation statistical methods were initially introduced to validate the assumptions of classical statistical methods.

Permutation methods have advantages over classical methods in that they are optimal for small data sets and non-random samples, are data-dependent, and are free of distributional assumptions. Permutation probability values may be exact, or estimated via moment- or resampling-approximation procedures. Because permutation methods are inherently computationally-intensive, the evolution of computers and computing technology that made modern permutation methods possible accompanies the historical narrative.

Permutation analogs of many well-known statistical tests are presented in a historical context, including multiple correlation and regression, analysis of variance, contingency table analysis, and measures of association and agreement. A non-mathematical approach makes the text accessible to readers of all levels.

Su questo libro

Informazioni sull'autore

Kenneth J. Berry is Professor of Sociology at Colorado State University.

Janis E. Johnston is a Social Science Policy Analyst with the United States government in Washington, D.C.

Paul W. Mielke, Jr. is Professor Emeritus of Statistics at Colorado State University and Fellow of the American Statistical Association.

Dalla quarta di copertina

The focus of this book is on the birth and historical development of permutation statistical methods from the early 1920s to the near present. Beginning with the seminal contributions of R.A. Fisher, E.J.G. Pitman, and others in the 1920s and 1930s, permutation statistical methods were initially introduced to validate the assumptions of classical statistical methods.

Permutation methods have advantages over classical methods in that they are optimal for small data sets and non-random samples, are data-dependent, and are free of distributional assumptions. Permutation probability values may be exact, or estimated via moment- or resampling-approximation procedures. Because permutation methods are inherently computationally-intensive, the evolution of computers and computing technology that made modern permutation methods possible accompanies the historical narrative.

Permutation analogs of many well-known statistical tests are presented in a historical context, including multiple correlation and regression, analysis of variance, contingency table analysis, and measures of association and agreement. A non-mathematical approach makes the text accessible to readers of all levels.

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2014
• Lingua: Inglese
• ISBN 10: 3319027433
• ISBN 13: 9783319027432
• Rilegatura: Copertina rigida
• Numero di pagine: 580
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