TY - BOOK ID - 699537 TI - A Chronicle of Permutation Statistical Methods : 1920–2000, and Beyond AU - Berry, Kenneth J. AU - Johnston, Janis E. AU - Mielke Jr., Paul W. PY - 2014 SN - 3319027441 3319027433 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Resampling (Statistics) KW - Statistical hypothesis testing. KW - Hypothesis testing (Statistics) KW - Significance testing (Statistics) KW - Statistical significance testing KW - Testing statistical hypotheses KW - Distribution (Probability theory) KW - Hypothesis KW - Mathematical statistics KW - Resampling methods (Statistics) KW - Nonparametric statistics KW - Statistics. KW - Statistics, general. KW - History of Mathematical Sciences. KW - Statistical analysis KW - Statistical data KW - Statistical methods KW - Statistical science KW - Mathematics KW - Econometrics KW - Statistics . KW - Mathematics. KW - History. KW - Annals KW - Auxiliary sciences of history KW - Math KW - Science UR - https://www.unicat.be/uniCat?func=search&query=sysid:699537 AB - 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. ER -